xAI_captum_transformer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z2m_ifyR4Dz6"
   },
   "source": [
    "### xAInet, a protein language model for sequence classification projected to describe aminoacidic importance via integratedgradients with captum\n",
    "\n",
    "xAInet is a transformer based protein language model capables of classifying proteins (binary choice) from only relate aminoacidic sequence (canonical ones)\n",
    "\n",
    "The model transforms the protein sequence input into corresponding tokens and then, to final embeddings (512 dimensions). \n",
    "\n",
    "Decoder module takes in embeddings anf generate features, then passed into a stack of linear layers in order to make the final class prediction.\n",
    "\n",
    "Embeddings representations are optimize via backpropagation and via a contrastive loss function.\n",
    "\n",
    "The model needs to be trained using a training dataet (csv file, first column: sequence, second column: label, 0 or 1).\n",
    "\n",
    "Finally, the model is passed to captum library in order to integrate gradients and obtain aminoacidic importance (saliency between input and output).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HBpxUjI5rTvh",
    "outputId": "2dd0c7dd-df54-4f58-f980-50d39f24434b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nPXd8ens4Aqf"
   },
   "outputs": [],
   "source": [
    "# install captum for saliency computation\n",
    "\n",
    "!pip install captum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "t3zh8yWpiTV_"
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as Data\n",
    "import torch.nn.utils.rnn as rnn_utils\n",
    "import os\n",
    "import time\n",
    "from sklearn.metrics import auc, roc_curve, average_precision_score, precision_recall_curve\n",
    "from termcolor import colored\n",
    "import pdb\n",
    "import random\n",
    "import pandas as pd\n",
    "import csv\n",
    "import captum\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "BYfvxgn6wTOW"
   },
   "outputs": [],
   "source": [
    "def generate_data(file):\n",
    "    # Amino acid dictionary\n",
    "    aa_dict = {'A': 1, 'R': 2, 'N': 3, 'D': 4, 'C': 5, 'Q': 6, 'E': 7, 'G': 8, 'H': 9, 'I': 10,\n",
    "               'L': 11, 'K': 12, 'M': 13, 'F': 14, 'P': 15, 'O': 16, 'S': 17, 'U': 18, 'T': 19,\n",
    "               'W': 20, 'Y': 21, 'V': 22, 'X': 23}\n",
    "    \n",
    "    # Open csv file \n",
    "    with open(file, 'r') as inf:\n",
    "        lines = inf.read().splitlines()\n",
    "      \n",
    "\n",
    "    pep_codes = []\n",
    "    labels = []\n",
    "    peps = []\n",
    "    \n",
    "    for pep in lines:\n",
    "        pep, label = pep.split(\",\")\n",
    "        if len(pep) <= 299:  # Check if sequence length is less than or equal to 500\n",
    "            peps.append(pep)\n",
    "            labels.append(int(label))\n",
    "            current_pep = []\n",
    "            for aa in pep:\n",
    "                current_pep.append(aa_dict[aa])\n",
    "            pep_codes.append(torch.tensor(current_pep))\n",
    "\n",
    "    data = rnn_utils.pad_sequence(pep_codes, batch_first=True)\n",
    "\n",
    "    return data, torch.tensor(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "SbUPb4xrifyr"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './test_loc_s.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data, label \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./test_loc_s.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m train_data, train_label\u001b[38;5;241m=\u001b[39m data[:\u001b[38;5;241m1000\u001b[39m], label[:\u001b[38;5;241m1000\u001b[39m]   \u001b[38;5;66;03m#I primi 1894 sono usati per il training\u001b[39;00m\n\u001b[1;32m      5\u001b[0m test_data, test_label \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;241m1000\u001b[39m:], label[\u001b[38;5;241m1000\u001b[39m:]\n",
      "Cell \u001b[0;32mIn[2], line 8\u001b[0m, in \u001b[0;36mgenerate_data\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m      3\u001b[0m aa_dict \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mR\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mN\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m3\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m4\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m5\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mQ\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m6\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m7\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mG\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m8\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m9\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mI\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m10\u001b[39m,\n\u001b[1;32m      4\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m11\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mK\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m12\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m13\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m14\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m15\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mO\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m16\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mS\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m17\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mU\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m18\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mT\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m19\u001b[39m,\n\u001b[1;32m      5\u001b[0m            \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mW\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m20\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m21\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mV\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m22\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m23\u001b[39m}\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Open csv file \u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m inf:\n\u001b[1;32m      9\u001b[0m     lines \u001b[38;5;241m=\u001b[39m inf\u001b[38;5;241m.\u001b[39mread()\u001b[38;5;241m.\u001b[39msplitlines()\n\u001b[1;32m     12\u001b[0m pep_codes \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/anaconda3/envs/venv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    282\u001b[0m     )\n\u001b[0;32m--> 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './test_loc_s.csv'"
     ]
    }
   ],
   "source": [
    "data, label = generate_data(\"./test_loc_s.csv\")\n",
    "\n",
    "# Split 80/20 for train/test datasets creation\n",
    "train_rows = int(0.8 * total_rows)\n",
    "test_rows = total_rows - train_rows\n",
    "   \n",
    "train_data = data[:train_rows]\n",
    "test_data = data[train_rows:]\n",
    "    \n",
    "train_label = label[:train_rows]\n",
    "test_label = label[train_rows:]\n",
    "\n",
    "\n",
    "train_dataset = Data.TensorDataset(train_data, train_label)\n",
    "test_dataset = Data.TensorDataset(test_data, test_label)\n",
    "\n",
    "batch_size = 64\n",
    "train_iter = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_iter = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9-vpJa8SrEMn",
    "outputId": "2def549c-ef26-4038-8b48-52c76c32ec1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lunghezza delle proteine x fase di inferenza\n",
    "\n",
    "len(data[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "bvKNJ008igZg"
   },
   "outputs": [],
   "source": [
    "class xAInet(nn.Module):\n",
    "  def __init__(self):\n",
    "    super().__init__()\n",
    "    \n",
    "    self.hidden_dim = 25\n",
    "    self.batch_size = 32\n",
    "    self.embedding_dim = 512\n",
    "\n",
    "    self.embedding_layer = nn.Embedding(24, self.embedding_dim, padding_idx=0)\n",
    "    self.encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)\n",
    "\n",
    "    self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=1)\n",
    "    self.gru = nn.GRU(self.embedding_dim, self.hidden_dim, num_layers=2,\n",
    "                      bidirectional=True, dropout=.2)\n",
    "    \n",
    "    self.block_seq = nn.Sequential(nn.Linear(15050, 2048),\n",
    "                                   nn.BatchNorm1d(2048),\n",
    "                                   nn.LeakyReLU(),\n",
    "                                   nn.Linear(2048, 1024),\n",
    "                                   nn.BatchNorm1d(1024),\n",
    "                                   nn.LeakyReLU(),\n",
    "                                   nn.Linear(1024, 256),\n",
    "                                   nn.BatchNorm1d(256),\n",
    "                                   nn.ReLU(),\n",
    "                                   nn.Linear(256, 8),\n",
    "                                   nn.Linear(8, 2))\n",
    "    \n",
    "    \n",
    "  def forward(self, seq):\n",
    "        seq = seq.long()\n",
    "        embeddings = self.embedding_layer(seq)\n",
    "        output = self.transformer_encoder(embeddings).permute(1, 0, 2)\n",
    "        output, hn = self.gru(output)\n",
    "        output = output.permute(1, 0, 2)\n",
    "        hn = hn.permute(1, 0, 2)\n",
    "       \n",
    "        output = output.reshape(output.shape[0], -1)\n",
    "        hn = hn.reshape(output.shape[0], -1)\n",
    "        \n",
    "        output = torch.cat([output, hn], 1)\n",
    "        output = self.block_seq(output)\n",
    "        \n",
    "       \n",
    "\n",
    "        return output\n",
    "\n",
    "  def train_model(self, seq):\n",
    "    #with torch.no_grad():\n",
    "        output = self.forward(seq)\n",
    "\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5wB5vdMsii3S"
   },
   "outputs": [],
   "source": [
    "class ContrastiveLoss(torch.nn.Module):\n",
    "    def __init__(self, margin=2.0):\n",
    "        super(ContrastiveLoss, self).__init__()\n",
    "        self.margin = margin\n",
    "\n",
    "    def forward(self, output1, output2, label):\n",
    "        euclidean_distance = F.pairwise_distance(output1, output2)\n",
    "        loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n",
    "                                      label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n",
    "\n",
    "        return loss_contrastive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PmvuSop0ikq3"
   },
   "outputs": [],
   "source": [
    "def collate(batch):\n",
    "    seq1_ls = []\n",
    "    seq2_ls = []\n",
    "    label1_ls = []\n",
    "    label2_ls = []\n",
    "    label_ls = []\n",
    "\n",
    "\n",
    "    batch_size = len(batch)\n",
    "    for i in range(int(batch_size / 2)):\n",
    "        seq1, label1= batch[i][0], batch[i][1]\n",
    "        seq2, label2= batch[i + int(batch_size / 2)][0], \\\n",
    "                                       batch[i + int(batch_size / 2)][1], \\\n",
    "                                       \n",
    "        label1_ls.append(label1.unsqueeze(0))\n",
    "        label2_ls.append(label2.unsqueeze(0))\n",
    "        label = (label1 ^ label2)\n",
    "        seq1_ls.append(seq1.unsqueeze(0))\n",
    "        seq2_ls.append(seq2.unsqueeze(0))\n",
    "        label_ls.append(label.unsqueeze(0))\n",
    "\n",
    "        \n",
    "\n",
    "    seq1 = torch.cat(seq1_ls).to(device)\n",
    "    seq2 = torch.cat(seq2_ls).to(device)\n",
    "\n",
    "    \n",
    "\n",
    "    label = torch.cat(label_ls).to(device)\n",
    "    label1 = torch.cat(label1_ls).to(device)\n",
    "    label2 = torch.cat(label2_ls).to(device)\n",
    "    return seq1, seq2, label, label1, label2\n",
    "\n",
    "\n",
    "train_iter_cont = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\n",
    "                                              shuffle=True, collate_fn=collate)\n",
    "\n",
    "device = torch.device(\"cpu\", 0)\n",
    "\n",
    "\n",
    "def evaluate(data_iter, net):\n",
    "    pred_prob = []\n",
    "    label_pred = []\n",
    "    label_real = []\n",
    "    for x, y in data_iter:\n",
    "        x, y = x.to(device), y.to(device)\n",
    "        outputs = net.train_model(x)\n",
    "        outputs_cpu = outputs.cpu()\n",
    "        y_cpu = y.cpu()\n",
    "        pred_prob_positive = outputs_cpu[:, 1]\n",
    "        pred_prob = pred_prob + pred_prob_positive.tolist()\n",
    "        label_pred = label_pred + outputs.argmax(dim=1).tolist()\n",
    "        label_real = label_real + y_cpu.tolist()\n",
    "    performance, roc_data, prc_data = caculate_metric(pred_prob, label_pred, label_real)\n",
    "    return performance, roc_data, prc_data\n",
    "\n",
    "\n",
    "def caculate_metric(pred_prob, label_pred, label_real):\n",
    "    test_num = len(label_real)\n",
    "    tp = 0\n",
    "    tn = 0\n",
    "    fp = 0\n",
    "    fn = 0\n",
    "    for index in range(test_num):\n",
    "        if label_real[index] == 1:\n",
    "            if label_real[index] == label_pred[index]:\n",
    "                tp = tp + 1\n",
    "            else:\n",
    "                fn = fn + 1\n",
    "        else:\n",
    "            if label_real[index] == label_pred[index]:\n",
    "                tn = tn + 1\n",
    "            else:\n",
    "                fp = fp + 1\n",
    "\n",
    "    # Accuracy\n",
    "    ACC = float(tp + tn) / test_num\n",
    "\n",
    "    # Sensitivity\n",
    "    if tp + fn == 0:\n",
    "        Recall = Sensitivity = 0\n",
    "    else:\n",
    "        Recall = Sensitivity = float(tp) / (tp + fn)\n",
    "\n",
    "    # Specificity\n",
    "    if tn + fp == 0:\n",
    "        Specificity = 0\n",
    "    else:\n",
    "        Specificity = float(tn) / (tn + fp)\n",
    "\n",
    "    # MCC\n",
    "    if (tp + fp) * (tp + fn) * (tn + fp) * (tn + fn) == 0:\n",
    "        MCC = 0\n",
    "    else:\n",
    "        MCC = float(tp * tn - fp * fn) / (np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)))\n",
    "\n",
    "    # ROC and AUC\n",
    "    FPR, TPR, thresholds = roc_curve(label_real, pred_prob, pos_label=1)\n",
    "\n",
    "    AUC = auc(FPR, TPR)\n",
    "\n",
    "    # PRC and AP\n",
    "    precision, recall, thresholds = precision_recall_curve(label_real, pred_prob, pos_label=1)\n",
    "    AP = average_precision_score(label_real, pred_prob, average='macro', pos_label=1, sample_weight=None)\n",
    "\n",
    "    performance = [ACC, Sensitivity, Specificity, AUC, MCC]\n",
    "    roc_data = [FPR, TPR, AUC]\n",
    "    prc_data = [recall, precision, AP]\n",
    "    return performance, roc_data, prc_data\n",
    "\n",
    "\n",
    "def to_log(log):\n",
    "    with open(\"./results/ExamPle_Log.log\", \"a+\") as f:\n",
    "        f.write(log + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0rBfcSfoiktp",
    "outputId": "90be3afc-64d9-49fb-cee7-b8cb18d671b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "epoch: 1, loss: 38.03354, loss1: 1.24773, loss2_3: 36.78582\n",
      "train_acc: 0.9030, time: 338.87\n",
      "================ Test Performance. Epoch[1] ================\n",
      "[ACC,\tSE,\t\tSP,\t\tAUC,\tMCC]\n",
      "0.7712,\t0.7945,\t0.7333,\t0.8432,\t0.5219\n",
      "============================================================\n",
      "\n",
      "================ Best Performance. Epoch[1] ================\n",
      "[ACC,\tSE,\t\tSP,\t\tAUC,\tMCC]\n",
      "0.7712,\t0.7945,\t0.7333,\t0.8432,\t0.5219\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "net = xAInet().to(device)\n",
    "lr = 0.0001\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "criterion = ContrastiveLoss()\n",
    "criterion_model = nn.CrossEntropyLoss(reduction='sum')\n",
    "best_acc = 0\n",
    "EPOCH = 20\n",
    "for epoch in range(EPOCH):\n",
    "    loss_ls = []\n",
    "    loss1_ls = []\n",
    "    loss2_3_ls = []\n",
    "    t0 = time.time()\n",
    "    net.train()\n",
    "    for seq1, seq2, label, label1, label2 in train_iter_cont:\n",
    "        output1 = net(seq1)\n",
    "        output2 = net(seq2)\n",
    "         \n",
    "        #pdb.set_trace()\n",
    "        output3 = net.train_model(seq1)\n",
    "\n",
    "        output4 = net.train_model(seq2)\n",
    "        loss1 = criterion(output1, output2, label)\n",
    "        loss2 = criterion_model(output3, label1)\n",
    "        loss3 = criterion_model(output4, label2)\n",
    "        loss = loss1 + loss2 + loss3\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        loss_ls.append(loss.item())\n",
    "        loss1_ls.append(loss1.item())\n",
    "        loss2_3_ls.append((loss2 + loss3).item())\n",
    "\n",
    "    net.eval()\n",
    "    with torch.no_grad():\n",
    "        train_performance, train_roc_data, train_prc_data = evaluate(train_iter, net)\n",
    "        test_performance, test_roc_data, test_prc_data = evaluate(test_iter, net)\n",
    "\n",
    "    results = f\"\\nepoch: {epoch + 1}, loss: {np.mean(loss_ls):.5f}, loss1: {np.mean(loss1_ls):.5f}, loss2_3: {np.mean(loss2_3_ls):.5f}\\n\"\n",
    "    results += f'train_acc: {train_performance[0]:.4f}, time: {time.time() - t0:.2f}'\n",
    "    results += '\\n' + '=' * 16 + ' Test Performance. Epoch[{}] '.format(epoch + 1) + '=' * 16 \\\n",
    "               + '\\n[ACC,\\tSE,\\t\\tSP,\\t\\tAUC,\\tMCC]\\n' + '{:.4f},\\t{:.4f},\\t{:.4f},\\t{:.4f},\\t{:.4f}'.format(\n",
    "        test_performance[0], test_performance[1], test_performance[2], test_performance[3],\n",
    "        test_performance[4]) + '\\n' + '=' * 60\n",
    "    print(results)\n",
    "    # to_log(results)\n",
    "    test_acc = test_performance[0]  # test_performance: [ACC, Sensitivity, Specificity, AUC, MCC]\n",
    "    if test_acc > best_acc:\n",
    "        best_acc = test_acc\n",
    "        best_performance = test_performance\n",
    "        filename = '{}, {}[{:.3f}].pt'.format('ExamPle' + ', epoch[{}]'.format(epoch + 1), 'ACC', best_acc)\n",
    "        save_path_pt = os.path.join('./Model', filename)\n",
    "        # torch.save(net.state_dict(), save_path_pt, _use_new_zipfile_serialization=False)\n",
    "        best_results = '\\n' + '=' * 16 + colored(' Best Performance. Epoch[{}] ', 'red').format(epoch + 1) + '=' * 16 \\\n",
    "                       + '\\n[ACC,\\tSE,\\t\\tSP,\\t\\tAUC,\\tMCC]\\n' + '{:.4f},\\t{:.4f},\\t{:.4f},\\t{:.4f},\\t{:.4f}'.format(\n",
    "            best_performance[0], best_performance[1], best_performance[2], best_performance[3],\n",
    "            best_performance[4]) + '\\n' + '=' * 60\n",
    "        print(best_results)\n",
    "        best_ROC = test_roc_data\n",
    "        best_PRC = test_prc_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j7hjeNzvoJ4m"
   },
   "outputs": [],
   "source": [
    "torch.save(net, './model.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d9vN_c8BE2h6"
   },
   "source": [
    "- Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "egHhpJhEmsHh"
   },
   "outputs": [],
   "source": [
    "model = torch.load('model_SSP.pt', map_location=torch.device('cpu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "-kMIvoFmjmAu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndef generate_data(file):\\n    # Amino acid dictionary\\n    aa_dict = {\\'A\\': 1, \\'R\\': 2, \\'N\\': 3, \\'D\\': 4, \\'C\\': 5, \\'Q\\': 6, \\'E\\': 7, \\'G\\': 8, \\'H\\': 9, \\'I\\': 10,\\n               \\'L\\': 11, \\'K\\': 12, \\'M\\': 13, \\'F\\': 14, \\'P\\': 15, \\'O\\': 16, \\'S\\': 17, \\'U\\': 18, \\'T\\': 19,\\n               \\'W\\': 20, \\'Y\\': 21, \\'V\\': 22, \\'X\\': 23}\\n    \\n    with open(file, \\'r\\') as inf:\\n        lines = inf.read().splitlines()\\n\\n    pep_codes = []\\n    labels = []\\n    peps = []\\n    \\n    for pep in lines:\\n        pep, label = pep.split(\",\")\\n        peps.append(pep)\\n        labels.append(int(label))\\n        current_pep = []\\n        for aa in pep:\\n            current_pep.append(aa_dict[aa])\\n        pep_codes.append(torch.tensor(current_pep))\\n\\n        \\n    desired_length = 299\\n    padded_sequences = [seq[:desired_length] if len(seq) >= desired_length else torch.cat((seq, torch.zeros(desired_length - len(seq)))) for seq in pep_codes]\\n# Apply pad_sequence on the padded_sequences\\n    #data = pad_sequence(padded_sequences, batch_first=True)\\n    \\n    data = rnn_utils.pad_sequence(padded_sequences, batch_first=True)  # Fill the sequence to the same length\\n  \\n\\n    return data\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "def generate_data(file):\n",
    "    # Amino acid dictionary\n",
    "    aa_dict = {'A': 1, 'R': 2, 'N': 3, 'D': 4, 'C': 5, 'Q': 6, 'E': 7, 'G': 8, 'H': 9, 'I': 10,\n",
    "               'L': 11, 'K': 12, 'M': 13, 'F': 14, 'P': 15, 'O': 16, 'S': 17, 'U': 18, 'T': 19,\n",
    "               'W': 20, 'Y': 21, 'V': 22, 'X': 23}\n",
    "    \n",
    "    with open(file, 'r') as inf:\n",
    "        lines = inf.read().splitlines()\n",
    "\n",
    "    pep_codes = []\n",
    "    labels = []\n",
    "    peps = []\n",
    "    \n",
    "    for pep in lines:\n",
    "        pep, label = pep.split(\",\")\n",
    "        peps.append(pep)\n",
    "        labels.append(int(label))\n",
    "        current_pep = []\n",
    "        for aa in pep:\n",
    "            current_pep.append(aa_dict[aa])\n",
    "        pep_codes.append(torch.tensor(current_pep))\n",
    "\n",
    "        \n",
    "    desired_length = 299\n",
    "    padded_sequences = [seq[:desired_length] if len(seq) >= desired_length else torch.cat((seq, torch.zeros(desired_length - len(seq)))) for seq in pep_codes]\n",
    "# Apply pad_sequence on the padded_sequences\n",
    "    #data = pad_sequence(padded_sequences, batch_first=True)\n",
    "    \n",
    "    data = rnn_utils.pad_sequence(padded_sequences, batch_first=True)  # Fill the sequence to the same length\n",
    "  \n",
    "\n",
    "    return data\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cMMZgCaYjnwO"
   },
   "outputs": [],
   "source": [
    "#data = generate_data('SSP_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qX8o3gaxpU_h"
   },
   "outputs": [],
   "source": [
    "#data = data.to('cpu')\n",
    "#model = model.to('cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7w3PmV8Rz2cZ"
   },
   "source": [
    "xAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "VqZTILbTjpyb"
   },
   "outputs": [],
   "source": [
    "def model_output(inputs):\n",
    "\n",
    "    #inputs = inputs[0].unsqueeze(0)\n",
    "  \n",
    "    out = model(inputs)\n",
    "    # Apply softmax to convert prediction scores to probabilities\n",
    "    probabilities = torch.softmax(out, dim=1)\n",
    "    \n",
    "\n",
    "    # Get the predicted classes by selecting the class with the highest probability\n",
    "    predicted_classes = torch.argmax(probabilities, dim=1)  \n",
    "    return predicted_classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "3O-omLR4z9Fb"
   },
   "outputs": [],
   "source": [
    "def construct_input_and_baseline(text):\n",
    "\n",
    "    max_length = 512\n",
    "    #baseline_token_id = rnn_utils.pad_sequence()\n",
    "    \n",
    "\n",
    "    input_ids = []\n",
    "    token_list = []\n",
    "    \n",
    "    aa_dict = {'A': 1, 'R': 2, 'N': 3, 'D': 4, 'C': 5, 'Q': 6, 'E': 7, 'G': 8, 'H': 9, 'I': 10,\n",
    "               'L': 11, 'K': 12, 'M': 13, 'F': 14, 'P': 15, 'O': 16, 'S': 17, 'U': 18, 'T': 19,\n",
    "               'W': 20, 'Y': 21, 'V': 22, 'X': 23, 'PAD': 0}\n",
    "    \n",
    "    for char in text:\n",
    "      if char in aa_dict:\n",
    "        input_ids.append(aa_dict[char])\n",
    "        token_list.append(char)\n",
    "\n",
    "    baseline_token_id = 13  # Corrisponde a 'M' nel dizionario aa_dict\n",
    "    baseline_input_ids = [baseline_token_id] + [aa_dict['X']] * (len(input_ids) - 1)\n",
    "\n",
    "    #baseline_token_id = 23\n",
    "    #baseline_input_ids = [baseline_token_id] * len(input_ids)\n",
    "\n",
    "    input_ids_tensor = torch.tensor([input_ids], device=device)\n",
    "    baseline_input_ids_tensor = torch.tensor([baseline_input_ids], device=device)\n",
    "\n",
    "    return input_ids_tensor, baseline_input_ids_tensor, token_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MASYARKFLAIFLFLTIAVFFFSTLEVQAKVCLRYSKYFQGKCFDP...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MDEKWNLSKKEGSGSSYHHSSTNAKSSFLRSGSTSKSPLLRCSSQK...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAQILIFIYALIIVLSLRLVVSSKTTLPCVSKDDCPLGLPPLSVTC...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEVLSLNKNMLLKMIMMAMMWSMAKSVRHDVGGDDHGWNTNINMTK...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSYPHQKQPSVAGYPPQPAATAYPAGPYVAPPPVGYPMKHGHDYPQ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2362</th>\n",
       "      <td>MKGRRDKKHPPNVENNYKQCREKEYGGHLMDEEREKRRPEFYSGDG...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2363</th>\n",
       "      <td>MLMSWINSFLFGCKERCTHISLIIYFRWYFTFYKEYVNNSICQKFS...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2364</th>\n",
       "      <td>MEEENTLEETVYNWDYTSAFAAVLLLLIATAVVRCICCTTDLRFCC...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2365</th>\n",
       "      <td>MYAPPEYQQASYVDHVRHHVQQRHEEKGCLYACLFALCCFCVFFCL...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>MELYMEEKLKLMKKGSRNCASRREPEARASVIAGAVRPPRSFTSRC...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2367 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    seq  label\n",
       "0     MASYARKFLAIFLFLTIAVFFFSTLEVQAKVCLRYSKYFQGKCFDP...      1\n",
       "1     MDEKWNLSKKEGSGSSYHHSSTNAKSSFLRSGSTSKSPLLRCSSQK...      0\n",
       "2     MAQILIFIYALIIVLSLRLVVSSKTTLPCVSKDDCPLGLPPLSVTC...      1\n",
       "3     MEVLSLNKNMLLKMIMMAMMWSMAKSVRHDVGGDDHGWNTNINMTK...      1\n",
       "4     MSYPHQKQPSVAGYPPQPAATAYPAGPYVAPPPVGYPMKHGHDYPQ...      0\n",
       "...                                                 ...    ...\n",
       "2362  MKGRRDKKHPPNVENNYKQCREKEYGGHLMDEEREKRRPEFYSGDG...      0\n",
       "2363  MLMSWINSFLFGCKERCTHISLIIYFRWYFTFYKEYVNNSICQKFS...      0\n",
       "2364  MEEENTLEETVYNWDYTSAFAAVLLLLIATAVVRCICCTTDLRFCC...      1\n",
       "2365  MYAPPEYQQASYVDHVRHHVQQRHEEKGCLYACLFALCCFCVFFCL...      0\n",
       "2366  MELYMEEKLKLMKKGSRNCASRREPEARASVIAGAVRPPRSFTSRC...      0\n",
       "\n",
       "[2367 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('SSP_dataset.csv', header=0 )\n",
    "new_columns = ['seq', 'label']\n",
    "df = df.set_axis(new_columns, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MASYARKFLAIFLFLTIAVFFFSTLEVQAKVCLRYSKYFQGKCFDPAGCKNNCTQMEKAISGDCHPDHGFGSACLCLFNC', 'MAQILIFIYALIIVLSLRLVVSSKTTLPCVSKDDCPLGLPPLSVTCIKKICLYYVEGF', 'MEVLSLNKNMLLKMIMMAMMWSMAKSVRHDVGGDDHGWNTNINMTKWSSDKHFHLNEWLFFGYDRHFFSVLEVNKTSYENCIDSGFIKNITTGVGREVFQLSEAKTHYFISGGGFCQRGVKVAIDVNEHVAPAPQPTPHKSSATSNIQIYHSLAVLILIFMCTNFLV', 'MGLRGVGSIFILFLNLIFVSTLVFAKPAPKRPPPPPLQVTCPSLPPPVHAPSTPPSTPPIMVSPSTQPPVVDPFTPPPSTPPSTPPTMVAPATPPPMVAPSTTPTSTPPSTPPTIVAPSTPPPMVDPSTLPCTPSFMVAPLAPPPVAEPLTPPPSTPATMVAPATQPPMDDPATQLPLK', 'MTSLEVIVMFLWTCAHSETNRNVQNKFGKSRETVSRKFGEVLESLCLLAKEIVRPPDFNLAETPSKIKNDRRYWPHFEGCIGAIDRTHLPAIVPTKDQIRYIGRKGFPTQNVMLVCNLDMLFNFVVVGWPSTAHDTRILSLRHVIVFVKNISFIF', 'MAGHCWIFLLWFVCASAAGFSAMVLVSNWCCCLLVRCVLLQQLRAVAAASVLASSVVSLPPRLARLVWGLC', 'MSNLSGNNVFVMLFSLCLLLLLLLNIVVGSPPSSASGVEDYQNLFGLSSREEVVEIAGYGEEKLSTVLITGSLHCDGPNNNHHSNAWPIPGALVSVNCQSHATERTGKAMVANGVTDEFGDFMVDLPSYLHAIPNLEKICRVKVHKIPKGSLCRPARHVNKQKGLLKLSTIGNGIRTYDAGTIRIQHSASELVRASPRKDIGEIKE', 'MERSVNYYILLLLLAHFLCSGYSETNKEAHGHVTQVNPQSHKEKLLEFKAPISTTQRDITTPITTIPNLVPTISTSPILNPDSNPDTYSPSSTVPITAPSTSNSPVSSGASWCIASPSASQRSLQVALDYACGYGGTDCSAIQPGGSCYNPNSVHDHASFAFNKYYQKNPVPNSCNFGGNAVLTNTNPSKASTIYDHGFKLQSDTTM', 'MAQSLPATIVLGLLIFTSVMSEVKSCELTRIGGGCPDISKCMKTCLPCYVGIGEIHYYCRHPGGPILYETCVCVMVKGAPCQVRGCPGNTPPAAVASNYNQTQFMKYLNVTA', 'MAKILKFVYVPILYLSILLVLTIYDQVYFNNSPPCVTDKDCPRPQFRKSNVRCRNGYCVNLGN', 'MALMREKSINKLINANIFTIIVMQFVLTFVLFSQVTYAWNWRCLGARNISITFEADNIEADLRCHGLGDWKNSMVKS', 'MDFLFSFTNTLLLLVRVLECGFEKTRDKGECSSDQVSHEQMIIEHRFFLSPQHQW', 'MAHTIILKLWVWVGERWVDCFSFPSDVVCRKPITLFLFPFDGGIEDSVVVDAGSPNLSFGWMSLVSGATGENLHGYSVQSLMETLVESWTSWCDVALPMPRFGTFHWFQGLCLIFGACHWFLDKVQEPDLLGVAQAYVLFFGVEVVVVFDVCSCTSLDGENKFSTLLV', 'MIYRRWSLLTGPTALVAGVVATVAVANFVLVKNVISSLSLSPPFMRLIHPCLYLLKKIDFYRCK', 'MTPSSYLSLFLTISVIFFSHAIPAKSSPKLYQSVCKESRHKDFEQRCLKTLEAYPQITSTEDYLTFSRLYLKIVAIKNAGKAQHQVKEMKNKYPSSGVIKYCAESYSGAVNELKDAFSEEDPDLIIMSVQYAYYAIGACEHVLAQEKSVNTSSIYALNNEIMFLSDIACIASRHV', 'MTTIQSQAGKESSTVEKTASTFKRWGRKGPFLRYGLPMISLTVLVLLVLAIFCKAGYFQKCYMVGVWLTDISIANQH', 'MFMRSAAAISYFADLLMQIVWTIFSAFGVVLFPTLLRFFWCQRREFRRPVWSEASRLLEFVFCSKYVVVMV', 'MFDETLALACWLFGAALAGLACELPRCRKGVHMLMQVELHQIANKQRLQLHHMLPDKKKEGDQEFQGSTNYQVSKTTLILLMFLFPTLPLSY', 'MMCEEGKSFYIWLLQVICLLGLLVLCLWLALRPNSPSFSIVVISIDRPSDQNGTIFYSLEIGNPNKDSSIYYDDIILSFLYGQQEDKVGDATVGSFHQGTSKSRIVSDIVNAKPGPFKPLFKAISNATAELKASLTTRYRYKTWGVRSKFHRLLLKGILPIDSDGKLSRKKKKYPLTRNSKKLGRSKVKKH', 'MKSIQALFLVFSLLLVANMSYARKDLGDLWKNKMNKQPMPEAIKNFIQVPKALGEGKEDHSFTTDFDVNPNIILYHTHVHQDEKPFEHAARKMESLLPKRG', 'MFVYDLILFISLILVVTGIKADTSCHSFDDCPWVAHHYRECIEGLCAYRILY', 'MTFSASKFYIIFMYLYLAIFINSAKDEEAKICKILLSSTMNHGCEEIKCNEGCINEENANSGECMATGIVVFFCYCFYECEQ', 'MAPTFALHFLSLVLFFCTVFLKLGKRFKNNLRIIIRCKKGKLCIHLHTITGNIRRSMGAP', 'MVLIFLIRSAIAAGTAAFDNRNFSISTSTKWINSVTSPKVKLLHTIFAIQSLAPPYDCYACPITLD', 'MDTTKICALITIYLLAFTIFASDMSVKSEGKGFCNKDADCDPCPSPGQHRKCDIHRCFCIGGPYKISTLRSPIAN', 'MYKTGLLIIILRPHFLTISTFIFFSFTLTTNNNNNNNALDEEGFPSTTLPNRIPFPKRIGEKQICRRFLSPIVGRWWLVVVVGNLDELCGFGFKLGLGFEY', 'MDSYKKVMIMVMLLAIGNAKFSTSITICNLTREERETCEPYVSGENSVDATRKTFKACCSVMAKADLECFCRYKNSILLSYYGIDPKLALELPVKCKLRKSFKC', 'MAPPLILPKLILLILVLCLLFQHKNHNKNTNSNANTFNSINIVPIHSFSSFPVSLSAINNSHVSESSSSDSSSNFLLEYDFYRDSCPHAEHIVRSTLHLLYKTNPALVPALIRLVFHDCFIQGCDASILLDNDEYIDSEKDSPPMIASKDLM', 'MAKLMKFVYFMILFLSLYLVATTHDCHNDLDCGDKIKCVPPRIALCINYKCYCILENDAVIPWST', 'MRVAKSGHLRRCWFWLLFLLSGRPSCCALSQPRHRPFGRSLLLIQGSGSILGQKGGYACCVLQGADANVAHSTGSQNVISDALDYDRPPDGLPHFECGGHG', 'MLGNLTELLLLLVSIPSDLGGGRVKKYRTSPILGLVLREEEHAPEALVLVLLFELDIAKARAIGYYGKGGLPKLFIEKKYIDEVEEIQKLHDDKKLEKLLDCCERIDDIEGDDGGFEACRDINFVPCETCYGSCKIYYEGDYEEDVLIAMKMA', 'MLMWPLTAMWHCHMAQVQLFFGQELGNISNYAPSGIPTEHTTSSLSVKMMTNYGHYTFAVGENLTSRCNYTLP', 'MGVFYFIVALLALASSVGFAYDPSPLQDFCVAINDTKTGVFVNGKFCKDPKLANADDFFFQGLGPGNTSNPLGSKVTPVTVNEILGLNTLGISLARVDFAPKGLNPPHTHPRGTEILVVLEGTLYVGFVSSNQDNNRLFTKVLNKGDVFVFPIGLIHFQLNVGYGEAIAIAGLSSQNPGVITIANAVFGSKPPISLEVLTKAFQVDKNVIDYLQKQFWYNNS', 'MKMKKVNTLFLVFVILTLVQCSSTRIYHSNMERGSRNIAKSIEVAFARFKNEGVTVGTVEDSAREVPTGPDPLHHNNNPFEP', 'MVKQVRASLTCFVLFFLVIATSMISIVHSTNTVTVTREDQARKPRKRWQKKTWMNHGSHRGPRKHLVNPTTENVFQLAREFPV', 'MAKYTSNALYIFVALISVFAMQSILVGATCAKIVGSCATMDCKGYCRKLTISGDSHCRYYNLCTCFFNLSLPPKIQGIPMCSLGYGLCDANCDSNCCDAKCTSAERVYKNPYGKCTNMANKNYCICYYDHP', 'MIFIYILCADGFFSFWLFWMKLDTSCPSEKMCSQDLSKSQTLLVHMPHENGFEVFEGYCLTDRGQ', 'MVAVHFWLCFLDSITNLLRSAGFQSFGIVSVVQEKMGHTSEDKSSFCLTWDDVDSKESLMFAVVQEVDGH', 'MKKKNNKPDVVGKIVFLRFRLLLLRVKTQPNCQVAPDDHTRTYMCGLTLLEVWVHRPPAIGCNLTLEFLWVSVFRVVNLVGR', 'MQFVINSISLSLSLSLSLSLSLSLSLSQYLSIYASVNLK', 'MPRSTHLFFRIIWFAYVWVLWKERNDRVFQNTTSDLSTLMAKAKLNSYLWLKSKQGLFSYSYHDWWNHPLLCMGVQLY', 'MEAFSSKILLIIFSFSFITHSIQANDRPYQQSNTLFIRNSCSSTTYPRLCYTSLVKHADFIQTNRMLLTGTALNVTLASAKSTSALMSTLSKGQGLNPREAAAMKDCVEVLSDSVDELRRSIDEMSRLRTSNFELTMSDVQTWVSAALTDQNTCTDGFQEINATENVKTLVRGSIVQVAQLTSNALALINKLSTSHG', 'MAQVKFNIGLVLVTLTLLYASGTAQSSCTNEIASLLSCLDYVTGNSSAPTIGCCTQLANVVKSQPECLCLIIGGKGSSLGINQTLALALPAACNVQTPPVSLCKIANPPAGSRTVPLTDSGSSGGNSVKLSTPMLFVTFAATYICFNFQDILAYCFFRSYRSPI', 'MKCQLIYKDPNMILILPLKLFVILLALPYFKIVKAKFHPVSRQPDCPVIENHKTCQTFLLSHLEQGSDSRTRMLILALTEGIHWMLDRSLSTPMITLETTRIM', 'MDYNNLISVMLAAIVMTSIYFGYGGASSAHPPDQFQQPPRHRKAKTTDITSSLRCIFFSSILGASFVNLDIN', 'MKLKLISFHYGLLIIVVITIIMIQVVSPNARKLGESVLQKCATRLIIPVCDIAKSLFLTILKDLIFCTN', 'HLQSECTLFKDCTFPPKNEALSTGKDIIQLKTEGRKWYVCGVADHCSAHQIKFVITVLAEGASAPSTPPSSNAHSIVSSMFGVVMVAIVAMATIFA', 'MAIVLRAIGGLLLFRNLLGNNSFANEYGNFIKAKTLWSKPVCYSDVGKALGLYHAIQWVRDLTLLRM', 'MELIKIITFLCLFFSSLLVESHPSFHHQDQQLSLPILHRKIMFTEKVKESDEARHLVSHKRKDSLPAGKQNMEVGSKGTRQEWMESDDPSQYFTMDYNRVKRRRPIHNKQLPVGP', 'MGMIPALFSYATLQALVRLFLMQSMILLLVIESSITLYFHVAFSWLLVFKSELGCLELHFLLLNVIILELFVKFSSDCEKTLVSNFNGVIPWNRSVLSVCRSFNRNDLPLFVTYNLIISALSGGHLSS', 'MAMSTLHFISLFAGLVHHGTLILLLDWSVLLFAAAALCLAWSFCQSDSTSRVSLTEIVGYSFDCARPFHFNDAICTKTSLQRFSDPAHSVPA', 'MFGSSIFIILFNWYLFVKVIESDALTEDQVKKSNKVQRKLEQRQKDRRLDTLIEAQFAGGRLLACISSQPGLCGRAMGNSTFWKLNWLNHDPHVLLVQY', 'MTSLETLVMFLWTCAHSETNRNVQNKFGKSGETVSRKFGEVLESLCLLAKENVRPPDFNFAETPSKIKNDRRYWPHFEGCIGSIDGIFQPLFLLKIKFVILLERDFQQKNVMLVCNVT', 'MPKSLKFVYTMILFIFLFLITKNVDALHDCEYDDDCPKSTSKRTYRCINKKCRSYFTRVEK', 'MNTYYGLLLFMVYFVVRTTTAMVNNSADLTCGNQVIKFPFHIKNHNPNPSSYGYPGFELFCSSNNETMIELPHSMKLNVKNIDYRHQTM', 'MAGTLKCVYIMILIYSLCFLVAGGIKFNGECRNINDCYRKYTLVPYGYMRCIRGYCKLQRDVKVETSYTNN', 'MSIQFSNEFVLPELPSGVAVLLLLLSVVLSLIIFERTIKNGRKTKVIDIKLEDIEGRRIHCSFWGEFAKTIEKYFAANDNSLPIILMFFIKQLILQFGKTRKYMGRYSQLHITIHVIDYDIALELMTRLWSKFEEQLRAKNHEISTSKSKDK', 'MLIVILLMEMWIWGYAEAGRVGSIQKFADRLRCYTWSLLLHLTSKLQKEVYAVVVVQFPLSVSFAITINKSQGQSLKHVGVYLPSPVFSHGQLYVAISRVTSKDGLKILINDEDGEDIDVTSNVIFFFCQVT', 'MFPVKFEIWVVVIGVFGWKLVSARRAICHHSPRRGNLARLASGLDVNSQQLVFYSPRELISGKNSDFVEMMFWFKFYMNSSAFSCLDKFWNQIWALGS', 'MDIFLSQPINTVLAILLVLLYNIWKIKKPSNKFKGMKPPEPLFALPLIGACPALIISNQNAIKQCFTTNDKILASKPK', 'MASSMFVKVTCLAMICLVLGIPLANAAPSCPEVQQTLAPCVPYVTHPGPPISPPPPCCNAVKTLNGQSKTTQDRRDVCGCLKSMMGGIPGLNLPAIASLPKDCGVDIGYIISPNMDCNKYISHHQPSFSISYVHYYA', 'MATHINLLFVTIAMLCLASVTFAGSGEVSGECFPFPTCPNDTFQCSLHCLNKGYQHRGDCAFGKKLCCCNPNI', 'MCPLRLILIFLSATLAGFFVLRNLRSQPQITELDQNNEDIPEKETLDSAKPTLNAPSKSKVKAALESWFWTFIDMASGRYLWKHLVKSSSK', 'MAKITASFVLALFVVAILNGYCAEGAETSNQPKKDDDIYKSEKTFGCLFLYKMCLFFQIDCPDYYKLCFPSPPSTPNYPSPNFPSPPSPPNYPTPNFPSPPNFPSPPSPPNYPSPPNYGNPPAEGGDSPTAQILP', 'MSIKEVIGFGAVFCCCLLFGGLSSVSACLLVIIAAESSASCFHGCSAPSFLEFWFGCLAVIVRCSSGVQNQESSSSPPLSTPTLPFSSGAALIHHSLFYFIITKKETKNKNKKKKIGIIIHGFNSKRSFFLVKPPQQEQDQQQQTQIPWLIRITQTSSGTTKLSQCDYCSLGSRLSPFLLVLSS', 'MLGTIVSQILSSIRTQFVFQFFTIFIILDVAVANILFLDSPAGVGFSYCNKTTDLYTFGDQKTGNVVSNYVAPTQN', 'MSGRNYVPLFVSVMVLCMVMTTLHASQIDDISCSEAISSLLPCLPFLEGSLPATPSSDCCTGATNLFNKANTIPARKSVCQCLQNASPKLWIHSERAKQLPKLCHINLFFPIDKCNS', 'MASCIKPILLTFIVICIASVMVAGDVVFPPGKRCFVSIYCPTMPKLCDHFCKASSGFCFKDEQVCCCN', 'MPKAHGATSMAQLLRFLSFLGSNNLSRNLGTEFLPLLSLQRLLNSPAHLQENSNAQRLFQETYNAQGHYQETSIAQVQLQETSIPQARRQDISNAQRAVLRDFYTQSQLPETSGAQSFTKRFMVLKLNKQETIICLQQL', 'MPSGGRTRIIDTLLCFMIFVGTSAGSLPTVQRGAFTCRSGFSDNTDICRPFPDLLPMFVALNPEDVVVAFQDSILHVLSQVDRGRQIAADQWRCRKGYIAWFYSVSHPIMSDHAPIAEYPAPIPPYEEVIFQEKYARQVPDPLQIIGKLHCQN', 'MWHIASSTNNITTHLWYIFSGLISTNVAVLITHTYIEEDSFEIEKSWQHHDSSLAFASKGYVVPPRYLRVRNGYSWYSSTIHGFYMSVKIVFFEGSPYCLPSRTNVHLHGSQFLLPHNLQITLLNQKNIHPLCCLVLLGMNVGIK', 'MAISFAVVALIVSAFCFSSKNVTFKKESTTDKDGFYMIVCKGDHEEDVCMVKPENTKGTCTGFMENKSDMIVPTKNVGRDSLVRIVNPLIFREKSIDPECEKLVKELGLDKLDD', 'MGKIVKFVYFMIIFISPFVVANHAISGLLPKLPFGCCTSNLDCPRHMCTHPQQPWCIFYGNRIMYRGSRLGICKCS', 'MKLVTIFIIILLACAYPAAMSRTLPKKSSSSVDEVFQQWMLEYNRTYSNSTEMKIRKEIFKKTLESIENFNSTAAGYTIGVNQFADYSYELDHCDCLLPNEDFHL', 'MAFLVYFLIFLSLTGHSSALYCVCKDGVSSQLLQKAIDYACGTGADCSPILQNGPCFQPNTVKDHCNYAVNSYYQRKGNVQGSCDFAGAAAPTQTPPTAASGCVYPSSPGNAGTPSTGSPGSTPGTGTGTGTGTGTGTGTGTGTGSTAGSPNVIGISPSSTIDGSSAAGESCMKNTNTILLLSLVITMCLAFKV', 'MKLSFLLFLLGFIAIVVDAGLDPTTFITQEQNNEGCCNDCECSSLTFPPCRCNDATGRSCHPGCDNCVCDSGLPPTCRCEDYIYSCNKCTNSNQ', 'MPFLVDLFDQQLSWSIFVFILGVVMLGTFIYTLVTDGSPFRTAIDSVQFPVDKMMTM', 'MATIVQALVLFCLLVLLMHKGDAYEFVVGGQKGWSAPSDPNANPYNQWAEKSRFQVGDSLVFNYQSGQDSVIQVTSQQDYENCNTDASSEKSSDGHTVIKLIKSGPHYFISGNKNNCLQNEKLLVIVLADRTNKNSNQTTSPPSPSPSVAPSPSPLSSHSSDALTPIPPPSPLNGSSTPPSPVLDGSSPPPSPLDGSTLTPPPVQQVGSSPPPLGTDVTNPITPTQSPVSEPPPPNAASSILVSFGCSVGALMVSLLVFSK', 'MGHLTHEYRCLVSVLILTCVWRMAPVPCGPSLRSGGNSSYKYCIPHSQFLIQNEAVQEEGNHWELQGRILSSHSRFF', 'MAKTLAMLLLLLQLISFTTFAEELETLTPTYPPHTTPAPLHPPANAPHHHHHHHIHSPTPAPTPTPSPSPIHTPLHPPYHSAPVPAKPPTHGHHHHHPHPPAPTPVHTPVAPAHPPLHPPVHTPVVPTHPPLHPPAPAHPPLHPTPLPRSFIAVQGVVYVKSCKYAGVDTLLGATQILGAVVKLQCNNTKYKLVQKVQTTDKNGYFFIEGPKNITSYAAHKCNIVLISAPNGLKPSNLHGGLTGAGLRPEKPFVAKGLPFILYTVGPLAFEPKCPR', 'MHTCSVQTLVLFSFSFFKPQPSLTTQPLLLFLLFLFAVSNHRAIETAALRLIPKMVPDPNLHQKRRFCCDSE', 'MTNNYNRKVTPGNFDTGDLVSKRGVWAFFLLGLNALAGREQPFEEIGQGMVSESKSEGNEVSSMRFFRLIGSHPPNCKGKCGTCTPCTLTREQLPPSDVTDPAPERWMCKCGNKLYDPYGKI', 'MIFIIFLLTLLSHSTTNASVLDFCVADLSSSQTPSGYPCKKSVTINDFVYSNFNSGNTSTNFFKFSITPAFVNQFPAVNGLGLSAAKLDLDVGGIIPMHSHRGASELMIVVQGRITAGFISSDNTVFIKTLSKGQILVLPQGLLHFQYNAGRNKASAYLTFSSENPGSQVLDLALFGNNLSSANLIKSTLLDPAQVKKLKAIFGGSG', 'MGLFQVTTKYLIVILALSIVYNSFQITQARPIKPLNQQSSLNTQDSGAIHTNSFRPTTPGSSPGVGHRNFVVGDKNTRTMVVVQSPDVEVFVTNKRSDDGFKPTNPSHSPGVGHGYHTKIRHLN', 'CMYLFIKFFKFVSLLLLIDLGFVLISVVGLNFDYAVLNMVKQCYYLVYNTTLYFSPVVQRQYGNKYGHKQVIYYTILYKKHVFGCVLYHNHHDIMGCDTQ', 'MSLMCPEFIQSFLAGLVASGVLTSCLRFLTKVAFEESDDGLRKEASTWELENILVYAYFLLVLEL', 'MVKILKFIHIMIIFLIFIIVTNGASNPCVSTRDCTTHTCNPPLVARCINLRCYCGYK', 'MVVRGGFNGAGRMVCGGRSRFGFRSCGGYLLQSSELWGFQFLVGKITQFSLNLISVEIPETPCESDAECPYYSPSLYARCIDGFCTLFLS', 'MQIGKNMVETPKLVYFIILFLSIFLCITVSNSSFSQIFNSACKTDKDCPKFGRVNVRCRKGNCVPI', 'MWCCSVGRGDGYAGLVVVVMSIFVVATSCLKLFGLLRCSGCMSFELSLFSMLIDVCTTAIHVVCFTCCVLDSDGAGYFCFEIGSWCLAAFLSGGLGVSLVDWGLGVPVWFDLEVWWPIAFDSRLIVSSSVHVWFLGCYDVLSVDQRLCGGGS', 'MLVISIIFASLSLSLSLSLSLSLYSKLYTKKPIKEPHTHSHTHTYTH', 'MSGLSIFLFLDLKTNAQVQPPEKEKEKILFTTKIDPKQWHKLQQGRYKCNVDASFSSLRNRTCIDICLRDDDGAFVLAKTHEFVSCEFCTRG', 'MKPVLSLTLSFFLFSFITNISPNNAIEQVLDINGTPLTPGGQYYILPESDNPSIGGLILNKIDDLECPVTVVQDITVIGLPVKFSMLENSTSNILPGTDLEIEFTTKPDCAKSSKWSMFVDHDTQLSFVGIGGSANNPGVETTSGKFLVVKHQHGSGHAYRIGFCLDTTGDCGFIALEFFNSEDGGPRLIFTVNDAYSVVFVDAASIKSASHALPI', 'MILTLSAFFSSLLFSCFVSTVFDDDDDYDYVYYYTHSDPDRFFSSTYRFSFIHFSQS', 'MSEILKFIYFLIIFVSLFLAVVKGYDSKECYSDSDCHKKYSCPYTHMMKCVGGYCLGYHDD', 'MALDLALLQVMLLAWEVVTMLIFELQAGILKGAVRVRRKDLTYVAVDDDSSLTIFFIGFGLECANPSADGVLAGPESLLLLKVSNVKLGGLIYLRKK', 'MCICRCLHPKLKSKSSVIPWIEIKCPMQTNGIDCGYFVMRFMKEIILANQDMILENVCITLEITNGDYKCKTYSKDKLVEVEEDWATFMVEYLRDYIAQRLTL', 'MLAKSYVVLFLVILFVLCNGSFDAKHQENQTMNNIQSNLKTNISLKSAMPLNAKWVNFDCGEVGRFVLPSEKAHYITWIPIDQDLSCHVNWGHSEATVTVFDAKIDHGHLVVNWLIRPDGFYHSLDNFFWDKRASWEH', 'MIKIVKFDYAIIIFISLLVVAMDVDAGPIRICSSDDDCIGYWCPLSIQPRSTKPICRLVESISKRSRTPVGLCTCI', 'MELLHVALIHLILLSFASNYADNMNKKIVMVLDIVSRAVKWTGPTQPALARWANTY', 'LNCLRVLLIYFILLHVLETYSSLFVFAGLALRLRNHNSIGYESWSQVHALDEDLSGAVCYFGKKYRSDSDIGKGICLLI', 'MSQCLPTIPMITLKFILLASILDTPTFLNLYNLGPDDPKFNNNNIATTKSPKYRVISESKGNNSLCL', 'MPQILMFVYTFIIFFSPFFVVTNGTTSCITDDDCPKAVSFLVFKCIDNICVRVEIL', 'MRRCFILDIFGLGIVLLRSQGEIFEVNLAIDVMSYTEHCQQKLLGSKLICSNNQSNKYDIYLSKYNGDKILRVLVSQVICKKSRVFETTFDHATKVGLFLFMSRSNCFPDNFLILHPTNQHSEKFGENSVLTSHTCVNMIT', 'MCALELVKLLAVNLFSCGSKAQIEKFHEELANPKIFRFIAQSNATQPNYLSAEGNHNPRTEHYNTGHGKSRFG', 'MIKFLKFVYGMIILISLFFAVRDVSAAPPVYCIEDEDCYDLCTSPLVEICTNYQCICLKRF', 'MSLANIFGLILMFFFSYESKCTAKEMEFPDIPSLPAVRFEDLDGDEDEICSICLVEFEREDGVSKLRRCGHVFHFNCINQWLERSQFSCPLWDQMKYLEQACMGWKIRPLQLASVYLELLEG', 'MGRQWKEIGLTRHHVGGSLVEEIASLVFMLCAVLVTLSLFAAIIFFCADGVSKTSTKASGATANNYAGGSACAAAGCGAGCGGGCGG', 'MKLLKNNGGHFLFCCVLFQILVAGCESLEAVAPQQKKNCTYVITIETTCTWGAETSNRVSLRFGGTNSSEILVRHLNIKHLRQVDPLEPEVLDDIPRKPFQACMVDQFVVTAPCVESPICYLYLKLFGNDDWRPGYAQVQVLEGSHLSSNNFYFRRYLPRHVWHGSDVCDSEVTPFGLKKRKVHVENP', 'MASFRFLVLLLIVLIVIPQGLAQPECEPIGYGARDPSFRPRRPPGNKLPVYTPSMKKASVEKPPVKKPHVCKPQVEKP', 'MAEEQQGQGTNPPQEQPNEPLPRTYNQLLGTNNPTKTSWPELVGVTAEEAERKIKEDISGVEIQVVPPDSFVTADFRFKRVRLYVDESNKVIRTPIIG', 'MASFYLSLLLLFLAALILVSQGLADLLVPIYKPPIYTFPNYKPPINKPPIINLPPIYTYIRPPQLPPPPPIVYFFEPPRRPPPPPVYFFGRKSPPKYRSSFRNSKVDKKPKPKYRSSFRNRPIDKKPPSKYRSSFRYRPIDKEPLYGGQPPPTVYEPPSK', 'MPIVLVVSTIVCTLSYLSQSDNNIVFSPLSLQVVLSIIAFGSEGPTQQQLFKFLQSKSIDHLNYFASQLISVILFDASPVTLALADGGRLLYPRLISDGSVFNFTLLDQRKPLSTSNGFGVQLTETISTAKWFWSTTH', 'MYIIQKRIRKWSSKWICLQLLSVACLIISIAATVGSVAGIILDLKVFKPFKTMY', 'MKPSCVVVSLLILSLLLTKTQGIRLGKGSLAFEQQKQHDEERSTMLKRSNTDSEEATLCNDQQKCTGNIKNRKLVTTSISTTKSLSKNVKNSEDVAHTSVNGNTRNVNLNGEAKEIKVKSLSTSNYLPEDLVDITEMDYSPARKKSPIHN', 'FLVVMNVEDRVNCKNANGCPNELCEYPFEPKCLKPKFLFFSKKEGFCACI', 'MVCWCLYVLFVGGLLMVADVRVCGWCCTFASSLANGGGCFKGFAIQEVLFRSAVFSPPLVLFYYNDERILLVTGDHLFMIWVVVPLQTGVDARVLCYIVTVVVMRGGGGGDAWWW', 'MAFTNIRFLTFMLLITSMLVPCSFANANGTKKQDVTQKPNLNDILFSNNIGVQGLVYCKSGSKLIPLEEKRVLKECRVFLEASPLNNCNYPTDFNKGISGAELHSYHFLHDNKMNLYTVGPFVFTSTPKSISNGY', 'MDLRGSPPYMVVFTLSFMLIISHTMGESAQEKQKCAEQLTDLTTCLPYLGGSANSPTSDCCSGLIQSTKNNKKCICIIIKDRDDPDLGLKINITLALGLPSLCNTPDNFSQCSSLLHLDPKSSEAQAFNQLRQNSNGANSISPATSPSAEGSSQHSRNQGTDETVTTKNGAPFKGKSLLYSLVAGLLVLYF', 'MARIRDLLIVVNCCMTLWTAGVSSSKHLSLPFRLKPKYAIIKQRRLQPKADLCCESEAQIGTLICAATVSQSVQRILLKENQKSVRRKEWLYRFKNYFLKSGKFVEFVVLAISKSEFEVNIDECYDQLLRKNDVGEDEVVGMNE', 'MNFTMIGLNIGSKSGFVLITALIILPSTWLRSLGVLVYISVGGIMTSFVWVYHNQESYLEGLREDDIARGEEADKKSIAKKIKKNCNRLWQVKLERSMFGPLKKRSILEGPKI', 'MENFFQNLKRRVRESILTGGGGVVNVERDSILTGGGGGSDVERNPISSQDFLDKDMFCCRPREVKWSRYRPGINIFALLLVHNHNKKENANLKFVNVVNAEFRRCGAMGYHITLKAKDGENVNVYDTGIFVEAWMDWKEMLILHELTLSHNQFLASDKQFIVAQKLSASDGVQNWKWSWSRNLNHEENQPAVELVIALPTVQLQEGVTHTFKYGRLHWFLSYV', 'MLELKMLLALILSNFSFSLSPRYIHTEARPWSPASSKKTINL', 'MDAILKFVYTLILYLFLLYVVPFHRCEKDEDCAHAQMAQCVLTNCICY', 'MMFTNTSTASTIKCLDTRCTIFFKSEIDLQQINVKTRQDMESQEKSKVSVDWPRRFLGGPGSFPPRCNAKCGKCIPCKPVHVTVPPGTPVTAEYYPEAWRCKCGNKYYMP', 'MRVHKSMVLIIMFLLLLILLVLHLSSSRHMEERTRVKFPLSFPQSFEEFKDKKVSRFQIVSHTSTPGGPNPLHN', 'MNSSKSSTLVLFSIINMLFFAMANGCFFCPKPNPNPNPFPYPNPSPSTKSCPRDALKLGVCANLLNGPIGAVIGSPPEHPCCSILEGLVDLEVAVCLCTAIKANILGIDINIPISLSLILNACEKTPPTDFQCS', 'MDSGFLLNPCLMLSSSILVTCESFSILTVKLLWNSRKNRCTRKKTLVIITNGIYQSIEHPATVNSPDNVSSSTKSCYSRKTSTSVVDHFNGYFSQELRGKSYLNEMKITKGGE', 'MPFPFFFFFFHCLLFSFLFLSLSTTPPPASPTFLFQSTSYITRPSQIRRIHFLNSKIQICYSLEFQSKFVTPSTMKMSYVYKHWYLIVETSKRIPNEKSVSYKLSFSLPLWVPPATELEIL', 'METLRNSSLLVSFLLAFLIITSEASQATRMTEKLDSSCNTAEDCTIQNRHCPPGLIVICASHHCTCIH', 'MSLFIQKIILLCALILLSLHNIHGIHVNIHNSLEGSLDLIVHCKTMDDDFGVHPLHPGDNYGFSFNEKVLFDSPCSFGWNGETHSFNIYHASNLRKSNCDDCNWNIFKSGPCRIQQHGDPICFPFDN', 'MAQILIFVYAFIILFSLFVFFTSGAIPCGTRDDCPKTTTYMCFNNICVLLR', 'MIVSWRHQCLLLVLVTFIVLSKGSRLLKDQYWEQMLPKKLPSPSSSPSRGTNYVLTTLKTDHNILPTSDGKEDSMIISCVNFEDNNDIPQPE', 'MAKIIFYVYALIILFSPFLAARLVFVNREKPCVTDADCHRYRHESAINVVVMFGTELEL', 'MYHHHRKPFLLRVCILLSSLLRHSLLWPPEHKMSSKQRRCKNGKIDGGGGRQLRQRDEEGGGDGEGKRTVVKRVAAEGKMEVELQGKYKEMDELRKQEENRQQKLKQAREELAAAELELENLNPYEPPKDEILLFRSGA', 'MVQTPKLVYVIVLLLSIFLGMTICNSSFSHFFEGACKSDKDCPKLHRSNVRCRKGQCVQI', 'MQSRIYSIRFKIHVQVNYFTHVLLMMMKLIAILLNIVIQTTASNIASQCSLGLESVIETDTIVAIDMFDDCKFSISL', 'MVITNFFNNCTFILSILILLCSFLLNCVMASVYAVGDQDEWSSQTNYATWAERYNFSRGDVLVFKYVKGQHNVYEVREETFRSCETSSGVLAKYESGEDEVVLNKVKKYWFICNIAGHCLGGMRFGIEVKEVSNNGTDFMDGALNPQIQPTPSQNSCISQRWRVIGKFVPFGTGLLLLNLYF', 'MDNHLAVFLMLLVLVLLPFGTENLMQDVQVHLITLENTPKVITEKNMHLEEPKPRINERMDLELNDYAPSGANGRHTPRAP', 'MCETYYHYEHVKLLLLLLGDQSLAISFNQNVVEMITTLTEFAFQRVQCPKELRPTMKTVTELAFQNIVI', 'MDLWILLLMRCSTFTLLSDVGHITHTFDGAPQGSWDWDEEEKLPMMDNEEWDGGRFEWQ', 'MKPYLSCVNVAVFVFRFSVMFPMKKAEALPECDKKECNRWKEHCSKECLCIELFMGIGYCYPIPTAMKKLKEHPNLCQSHMDCINKGSGSFCSYYPNSEIQHGLCFTSKVEAERRYFEFEVLANATAKDMPMVVAAA', 'MSLLVSLVLKVLIFAERSKFHFLSKAVPFFSRSRFSLSIQLDQKHKISQNFSRTPRTKTLTVFQKLKKVHQNQQFLDQFSEILWNLHHRIDHFSAIQKRELALKQAQALITEGGSEEGKKQGLKDESRDVRIFKFWKFFTGKFIGFAGEDEVSGGPSRSPP', 'MARYISNSSCFFLILILIAAVQLIGVEATCSIFLGSCDGIECDVNCKANYTDGKGSCYFNNLCTCFYDRAPPKDTTEHFRQCTIGYQQSCDADCDESCCNSKCAKTFRDGLGNCVDYSGNKLCICHYSPQ', 'MLNVHFVKQISEKTSRSTQKNQEVLWAGFGISLFVIFAFAFVVFAVFSGCGILYIIQSSTKIAPAPLDGD', 'MESNKNTFVKLSVLLLLLCFTATIVDARFDSTPFTTRVLSNEMMDVTQLAKNVIALEIQNQKLLLVIVQMSCFSHVMIYGQAALSATCPKQTRARW', 'MVETLKFVYVILLFLSIFLFNKSPFSQIMFSDCKTDKDCPQFRRANIRCRKGQCVKL', 'MMDTTRLMHLQLALLVVSSIIVSDIFIEIVAGRINAEILDQKPKFKPTQNCPYCKRNEDCPHCCDSFKPPLYGVPVCRDFPPFRSHCVCDIHSTVPNNISSLTPTIAN', 'MNETKLNILLYDVALDFVSEILILILFLWAWMQVTMSIAREVAAVTRHGIEVALVVGGGNIFRGASWAGSSGLDRSSADYIGQTRDLDRRHDLAWPLRAHRWPESEIGKNGLDRRAKTWKMLATVMNAIFLQATMESIGIPTRVQTAFRMSEVAEPYIRRRAVRHLEKGRVVIFAAGTGNPFFTTDTAAALRCAE', 'MKKIVSILVIIMFVVAQADDSLPMSSTPELSSISSTIGCLGKCASQCRLVRMFMGAYAACTVGCGVVKCRGVMSHLLTILICFLV', 'MMKLEHSLFLGTLALLLATSFSSDPDYLQDLCVADLASGVTVNGFTCKEASKVNAFDFSSIILAKPGSTNNTFGSVVTGANVQKVPGLNTLGVSLSRIDYAPGGLNPPHTHPRATEVVFVLEGQLDVGFITTANVLISKTISKGEIFVFPKGLVHFQKNNANVPASVLSAFNSQLPGTQSIATTLFAATPSVPDNVLTKTFQVGTKEVEKIKSRLAPKK', 'MKYGLNRTPKIKLLLFLTLMVAALAIPLEMGLVTLKIYFMKIGVFTLPTLFEKETIVLIANLGASSSIDIMIHASPQFGLSNLLIADSMETCFRRL', 'MASLSFLVLLLFALYIIPQGLANYEKPPEYKPPVENPQFYKPHIEKPPVHKPLVEKPPTHHPPIEKPPIYKPPVEKPPAYKPPVEHHPVYKPSVEKPPVHKPPVEKPPVHKPPVEKPP', 'MATFHTLSILYVAIICFSMLVKSEISSPISPSSNESLSPYQKYLTTCIEKLTQPCDDEIFYTVYIGNQTVSYECCSQLVNDMGQRCHDEITRFIVESPKFKANKISILQRSKKVWNECSNAFDVDSPLGSIVPDI', 'MASHFLSTFQSRMTTRINPLLMSFIIMCVVSIMAAGIISNMFESIATGAEDLTNDEHCFNFPQCTGTDAFCDHACRVKYGDDNGGYCKPDKITCCCR', 'MASFFLYSLGLVFLSALTLVPLGLADKSPSHNMPPNPIYTTPIHKNPTNTPVYNPPIYKPPPTYKPPIKKQPINKSPNKKPLLLKPPLPKPPHKEPPSRKRPINTPPDKKPPLLKPPFPKPPQKKPPSRKRPINTPPNKKPPLPKPPVNKPPHKKPSNKRPPPYGNQPPPSIHF', 'MCSPPKKPLCYLFASSPFERKIGVCECH', 'MKADFQLASAAYVRNIGHSLGAWVLCFWLFLLMLARPIGPRPKLTKLGSDPSPPKQTISYDGHALKPTGSWYKSRVPHLTSQELCYDYPNGHSRMYLQA', 'MASRLHQLFLLFCILCIALVLVPAATETFQEPPGMTNCGETTCAKGDGVCYKSCIEEGFNRGGECLFHNNHEDEICCCYKNESSFSPNYNLN', 'MIVFLSIFHVVNSYVVMCEKDSDCVDSFCVPPNVPKCRVVCKCLPK', 'MHAEARMCESQSHGFKGACVGDHNCALVCRNEGFSGGRCQGLRHRCFCTRLC', 'MQMGKYMAQILKFVYVIIIFLSSFSVAMNNNGYYECTTDQCRA', 'MSILLNIFAALLLLLPTCLTLCLSIFHRVCLSLPTAAAQPRLSTHSNTAASLSHLYQLSQTNSFVHFPNTMKSETPATAAVTKQVKNTRNGGCCEVGNTNDRRGDKRCSRETPTTNNLSPARSRSHRRFTVRRPPSSLS', 'MTPSTYLSLLLTISMIFISHAISPTSSPNLYKSVCKETNKQDPHYEQPCLKLLEPYPQITSAKDYLTFSRLFLRIVAIENATKAQHQVKEMKKKYPSSQTIKGCALNYDKVVDELQIALDEYSETISLDVAYASDALEECEHSLVNEKIVNTSSLSTLNSEMRFIIAIISFASDHLQ', 'MKMKSLCVFLLVFVLLKVMCCEGCWKQEREALNAMKSNLENPNGDTFDMGNALFWTANTDCCEWEGVACNNITGRVTKLKLQYSYTRSLDYSEFAIFKDLKTLDLSESEISNCTRTHQGLKNLEGLYLSANPFDNAISVVSCLDGLSSLKSLSLADMYSMTSFQDFHTVLETLSSKLLHLEVLDNFLLKW', 'MTVSIWHQKLPGGLCSSNNECPTYGCPQQAVCNFFYPAVLHQGSRIGFCGCF', 'MVEIQKLVYVLILFLSIFLEMIVSNCTFIGFQDNPCKTDKDCRKVRGVNLRCRNGHCVMILQ', 'MCWWESVIVIPFSLFTLWECWSGVERDKRICKGLRLIWHATVWGLWKARNDRVFNGRNPDVEEIVEEIKVWSWRWSLDRISMPPCMYYEWFWSPKYCLLRLV', 'MERKSIVSICFFLVILLTSQKQVVHIEAYCEKQSRNFIGQCLGLIGDKLCYIICVTKENSISGSCQHLRCICSHAC', 'MGSKIPSLSCVIILLMLLALGISATRSNFQAMEKGNNRKEASTMLMVDNMSKSDSGKTSLFLLAKRQVPPSGPSHRGNNAPH', 'MGCESKACNQMFSYDFCIFKSFCSICSSSWMALFALLLLFSSYLLVGYGFLSLLYTSMQRKVNVDKLVEEKFLNSDDEKSNEENEAQEEVNIDGSESESQEDHLSTSEGSEVDHSHDYSDGSISDEESLIEIALPSGNYVNMQKDESDYYSLQQKKKELFGDALFSQQSLMELLSELNEVNEEENFIEIDLSMGSIKYSRFEIEA', 'MVNEDGTALQRSAVHLRVTWVSVILSLACLGVSLSTPHLPQGHCFLLPALLFPPPSVFLQ', 'MATTKMYRALIFFLIIVLPLFMSSEARTLPKFSSMMSTKKVGSELLLRDMIKNIVRINSEYIHKRSMLGGQLERVAPAGPDSQHH', 'MKTNQFNFLCFYALLLIFVVKTESYEVVKKIEKTKKSKTDIGVDCYTYWSNRGATWKANDRGMGNGWFGCGLGGIKGMGYNSYFWWKGTAWGSSEWWNGGGKGGGGGKVGGGRTWGGAGYVKPISSYGKGGGATDIEPPDDGKGTDIDKGRS', 'MAGVSHMFGALSSNLCLLQPVLCGIDVGGAAMLSDCKVYPEGGFYLVEFVRSG', 'MKLYNVAIFVLFLSVVSVFTVSDGKDALVGGWTPIKNVKDPQVVEIAKFAVTEYGKQSGSKLSFVKVIKGETQVVSGTNYRLVLAAKVVSATKNYEALVWEKPWLHLKNLTSFKPVA', 'MKTNLIILLCFSAFILISVVANESYEEEEPLAEIEDEELNCYGWWVRWKDGVKSSDACTGGNDKTYPDGGGRSGNFVKFLKNAKRKGKGESDGNNIGFLIGRKMGGGGKEKDN', 'MGSFSLLFLLGLTLIMGSHVAHAQDSPADYVNAHNNARSAVHTNVKIPNIVWDNKVAAFAKNYANQRKDCQLVHSGGGGRYGENIAESTGNMSGVEAVKLWVDEKPYYDYSSNSCANGEMCGHYTQVVWRNTQRIGCAKVKCNNGGTFITCNYDPPGNYIGERPY', 'MRRVAILAVLIGVECIHLLSLLFPHSTHIKHIIGATSDSWIRWWAIANARCNLACEQSKEVE', 'MDCSFASQLWCWLATILNLNCTFTSFLDIIRISDKNWTPQSSSSIAEFVILKHFNVKINHPKKHIIKEVLWFPPCFNWVKCNIDGASQGNPGLASPGGIFRNVDADFLGAFSINLGITSALSSELIAAMVAIEIEDVKNWINLWLETNSMLVFLAFKSSKIVPWSLRNRWDSSFFLLSRFNFRKILW', 'MAISKLLVASLLASFLLFHHLVDATDQSAYAQTQGSLLQQIDCDGACVARCRLSSRPNLCQRACGTCCRRCNCVPPGTSGNKEKCPCYASQTTRGGKPKCP', 'MVLVFGIFVLLDSVDFLQIQAVLLSETCRLKLTEKYIQVVPLSETCRLKLNENRFKLSFCRKLAA', 'MSYLAVYFFFLLSYTHMYYLFYRGRRVSSWWHILMSIIRGVGTGMSNWFDYNFVRVVDDGTTTYFRCDSWLEGGMLCHRFRGLFNLAENIRCQLLIYVLWVGVLGVELGCGDKVCLRGKRSRWRSVVSCRIKFLPG', 'MGAILKLVSAMFLFLSLFHAARNIDALSGCITNEDCIKYQCSAENCMVCINFACKCKYSVF', 'MVTTCLFTQGLSGEVITGISLGAVAGLVLLAFCIYVACYKKKQAPKQELLSENPARSSLKLRVPIFTLIYHLLLDEISGNATYGTSDSVSPAHIIGIRVEKSGEFSYEQLVNATNNFNMANKFGQGGFGEVYYAELNGEVADFGLSKLIDDRISSVPTANMTKHIWLHATRV', 'MKQGQSRKIVLLIAFLVLCCFSSFLLGAYARNLRIDSFEFHARHTGNGGVKHEDYVVSTMDYAAVKRKPPIHN', 'MVSITHNHSICWCTKTNNYKQEAKEKASHDDCAGAEIFFDGESKLEQTCSEDFECYIKNPHVPFGHLRCFEGFCQQLNGPA', 'MTQILKFIYTLIIFLSLFLVETNSAPKPCVTVKDCPKTVFPIGYKCIKNLCILVI', 'MAKHISQYFLLGILCIVLVLALGPTPVLSQCIWPGMCLGPAICDARCKFLLRPGGRCLGFVCCCT', 'MYVTMIILLFLFDVAAMGEPFYWPSLCSELQKEGTVNPGNYACEEFKRFCT', 'MRFQSLIVILVVLLASAAMNQALPGGYSPIKDLNDPHVIEIAKFAVTEYDKQKGTDLKFEKVVKGESQVVAGTNYRLTISAIHGSDSKSNSYEAIVYEKPSSEHLKKLISFIPVNA', 'MNNINIKSLLSIFLFSLLLFSSLFSSLPKPINTDLCRRNRKLTVLDLWWFKVASQTLLRVSRQFPDYNIRENTKQRVVDAFRSNPTLSEPSLLSKAFLFGKSQLDVAKQQAIVYSIYAPSLPSVTDLPNKPF', 'MIQIIFLLSLFLSISNASVQDFCVADIKGSDTPSGYPCKPASTVTSDDFAFEGLIAPGNITNIINAAVTPAFVAQFPAVNGLGLSAARLDLGPAGVIPLHTHPGASELLVVTQGHITAGFVSSANTVYIKTLKKGELMVFPQGLLHFQATAGKRNAVAFAVFSSASPGLQILDFALFASNFSTPLITKTTFLDPVLVKKLKSILGGSG', 'MLLFLLSLMNNHANSTTTRCCNDVLWYISPGIPFSCQCVDIAETCESTCKKCTCKQPKQCSCDDYNEYCIQC', 'MVKTSKFVYVLILFLSIFFSIIVSNSSPGRLSFGDCKIDKDCPKLRGGSFRCRKGKCVLE', 'MSKSFGFFAILALVVAVQLIQVEGVCTRIVADCTAKNLSCPAVCKAVGSGAKLIGTNCDFYNLCTCTYDHEPYDIALCDIGMGLCTKDCRSDCCDAKCTGQYPKSGVGLCIHHFGLDYCRCTYRRP', 'MLYLGFVYTVVVFVSLYFVVVDRQSKLILIFLFSYLIHNISFNFNNICLFLFYNTGTCYTFVHCYIKNGWPFKGVWRCNKGFCELVI', 'MASNKHSALIILFSLLAYSTFSNACGSCKPTPPSPPPPSKTPKACPPPPSTTPKASPPPTAITPPSTTPKSSPPTPSTAQKCPSDTLKLGVCADVLGLVNVIVGIPASSNCCTLIQGLADLDAAVCLCTAIKANVLGINLNVPVTLSLLLSACQKSVPNGFQCS', 'MSVKECNHHKGKKHKRFRKIFWGIIIFLFIVLVTILIIWAVLKPSKPSFILQDVTVYAFNATVPNFLTSNFQVTVSSRNPNDKIGIYYDRLDSYVTYRSQQITYRTAIPPSYQGHKEVDVWSPFVYGTNVPVAPYNSVALSQDQDNGNVLVVVKFDGRVRWKVGAFISGHYHIFVRCPAFITFGPRSNGISVGDSGAVKYQIVQRCTVSV', 'MSRSASLLIVSMLILGISLLSDAVAPVNSPAVSPTSNSPSPSVVHSPTMSPAVNAPAPSATNTHAPVPSPTSTNAPAPSTASNNAPAPSATNTHGPAPSDTTSNHAPAPSATSTHAPAPSETSTAHASAPSVTKTHAPAPSPHHSGSARLSGYVGINVVVALVLGSFVF', 'MKELLLLLLLSLTFRLTLSGSVSESQSVSLLPHANESFNISYIQMKNAGSCSYFVVISTSCSSPRYTSDQISIAFGDAYGNQIYAPRLDDPSSGTFESCSSDTFQINGPCAYQICYVYLYRSGSSGWKPESVKINGYSGEPVTFYYNTFIPRDTWYGFNLCNDAASSYKVSAQKWLILVILGFVLSFWL', 'MESKRGLIFLFLALTLALAIVTEASKIHDISGLISQVNPYRCGLTIKNSYQFMRIDHLMDQLNREYVFSNISPKTRYLHVRVDTCDI', 'MVEVTKLVNVMLIFLTLFVVALSDDSKPFSSLFKSPLVYCLQHSDCQAYECELPFKPDCLMVEYSPQFVALRCGCV', 'MLKLQTLQSLFSFLFFLSPPALIATLLWGGGGERERERERERERERERERERERERERERERERERERSLN', 'MGFICCLASFSMLCMFSLRNQSNNIASVTSLVSIFQSSREHFKICAFDNFHTQEYRILFSQTQHAFISTITASAINVCEPLKAESNAASSPSSLDTSHSDCPTSSKCSSEIDIQEGMSGMA', 'MSDPTTKILTLVFFLVLVSQGYSECSLSDLHVTQHPTGIEINQNPEFLVIISNNCPCAQKNVKLECLNFQTVEPIDPSILSVSGDVCIVKGGQPIASGDVSVTFKYAWPHTFPLNPISSDSVC', 'MVLFIFCWFLGLIQEEGWPFGLRLLNSRVGVMRNGDFSGSVSFSTLLSGSPTPSTYSSSDLDTQSTGSFFHDKSITLGNLIRVSSFLQLSRRSSRGRVMDSSNDNKRNHKQKPWLFSLCCNLSTDAVNGNDAHSLGHYLEAERKAASTYRRIQFPTSL', 'MYVSFGGCIVVLNAVLNLVPIFLLSYLKMQVNLWKKIVKIQIGLENGLSGCFGGRIVVQHVEGAKLGSCVGLKGKMVYQHIENGGLRVRDVRVVKLILLVKWEWMLLQSDQPTWKELVVEKYGREVERLLEGSGLT', 'MANSVALCHLAIILVINIVMAGPANAQINTPCNPSTISTLFTPCMGFLTGSSANGTSPTTECCGALKSLTSSGMNCLCLLVTASVPFKIPINRTLAISLPRACNMPGVPVQCKASASPLPAPGPVALGPSHSPASSPSGFIPTPSPQASSDLPASPTSSPLAPQQDTNVPLLTPPSPSVSTTGSGRSSLTPSSAITSYNVSPTVLLIALGFVALKYY', 'MAISFNRVHFFIALLCIASILTPGGAIDSASPWIYCIGKGPCPNKDQDCANYCAKSKFPKGGKCIASYCCCNG', 'MSMRLSIRTLIILAHVCLFITTTTIAQFVLDTVGEPVEGDEEYFIRPVITNKGGRSTMVSRNESCPLHVGLELTGLGRGLVVKFTPFAPHHDFDDVRVNRDLRITFQASSSCVQSTEWRLGEKDTKSGRRLIITGTDSATNGSYGNFFRIVETPLEGMYNIQWCPTEVCPSCKFECGTVDMLNENGKILLALDGGPLPLVFQKE', 'MLFLVVFLLVQSKAHIIGATIEAPTPAPAPAPAPASQIPSDSTPETPVTPTTPETPATPTTPPAPVTPTPPATPPAPVTPDPTDGTTEGSLKPEECAPRCEDRCSKTHHRKPCLFYCKYCCAKCLCVPPGTYGNKEVCPCYNDWKTKEGGPKCP', 'MQMTENIVEAFKSVYVMIIFLSLFLLSTNIDAAECYQDSDCPEDMCSYLAKPTCIFTEYFPIFWMAVCGCD', 'MASHINQSRLCWILCFAFVLLSGLSIATIFHVDYLGQSCLYKKCMDFTQCAQECLKRGYKNGGECVGSDHQNMRCCCYGS', 'MSLNKKKLSTSIVVLIVMKHALCYVFVADDEAEIVYDNRNCEEHVVLNDVVGGLIIRFGSTMKKGFVLDLRNGFEDCVLYEISRSYWI', 'MTIFLFLLLNPNCRKDSHCPKNMCPSYLVLLPIFFKCHFWRKFLFLFNCFKVQCNIKCYFINIILSYLLRREKYMK', 'MFIQFSILFLLFTTPFTSLIKAKQDNTNFVRSLDLKELNLENKQEKLSHLKFYWHDIVSGNNPSSIPIVPPPLKNSTTAFGLVNMIENPLTLGPQLSSKLVGKAQGFYASTSQSEVDLIMAMNFAIIEGKYNGSTITILGRNPVSDKVREMPIIGGSGLFRFARGYAQLRTHWFSSKTNDAIVEYNIYVLHY', 'MAATFKLVYVMILLISLYHVAGNFEDISIECMFSIDCPQIKSNIFRFKCIEDRCKIEFIYQRKKYEI', 'MKSTTPASLLFIFFLCIIFYAPLPSSCTSLYEALCNEYKPEADIPKCLNLMKTDPNIPLATNYHDLSHYILEMALFKVTSVHIYLEVMAKYFPTNEAIGQCLTEFNNSVGAVNNALIKLDNDPKSAREDAITAGFGGDNCNKALQNPPEKYVLDTIQDRNNEIFFVNVIASLSIIHLFD', 'MTKLLKFIYAMIIFLSLFIVVIKANAYRECNSDGDCPNYNCRVKEVGMCYFTKCYCIRL', 'MGCNASLEVQDTQGAVLFIRLLLLAIAPTLAVFELHCRCSIVKLQQEEYDRQLSEALSRGCSVDEALTLLAWTKVVGEKKKGRLYGAGNLAGNYRKGVATTLKLTLNVGEGSSRQPELTPEMRDMITRLTQEQLAAHMQTQQDLIHEVSGSRESVRAPPVCADDADDPYEVYGEGEDDDDDDDGLEMDD', 'MAEILKILYVFIIFLSLILAVISQHPFTPCETNADCKCRNHKRPDCLWHKCYCY', 'MFAPLVFLWWTGGEGCPSNLVLKYGGPTAFDSRSGVNYLVQVLLWAYFNVLSVDLKLRGGGS', 'MTSGAVVLGVFIPMAPATGGEEIGLEERRWRKRRNQCYLIGHINSFGVGGETEGAQGPLENKRP', 'MTMKYLIAYLLIVVMVIPSNKAQLGIINDLLGSANIQGTVLCTSKDNVGDVNGAATPGFSNAQVQLKCGGNMLSNATTDGDGKFSMMMDNPLLYDLSSLLTGCNLMVPTPLSKCNTKLPSVGGLISTLKYVGISRIGTHTLANIAPSGFHFIPLT', 'MSKQNVLVIMLIMSAILSLPIQSLDLKAGNWGKSFKKKNKETETNLQFYFHDTLSGKNPSAVKVAEPVDKTKTFATQFGNIILFKKITDIMKKRLSISI', 'MGGSAKELNDLILLFFILFDQSCYCGRKETKVEVKTETMKAQNGKKEKANAKEDLV', 'MVETLKYVYVMFLFLSIFQLMVVYDSIYFRKPPPCITDKDCPQMKINNVRCRKGFCIQIHKFTP', 'MAKDELLIYVLVSLLLKFVNCKRTTLKESIVRKIACKDSHVRSGSMQPFSGTNRVVYGLSSVYLWSSILLLRIDHDGGLKWVPRIRRRLLVGAWIWIDREAQIGDGDGSIATVECISVHFGPSSLNASSVQVLVQKKNARFGSTVFLPIPNLGTYVLKRLHCTCMKLVNYRLELGHKISYDHALEDYSRYDVYESKSFFFNMRISTESGLSV', 'MTVPNSISLKFSILIIILLAFEASDTIAISFAEVKVTIVNKVLAPTPTNITFHCKSRDDDLGFHTLVSEGSYAFTFSPNFTPWFSKTLFFCSFTWPGNPQLHYLDIYDQVRDNCYRCRWTINKDGGCLNTHKCYKWNSVKLMDA', 'MVFTKNTLILVLLCFILIQELEIYSGNQHIVAAQIDCNGKCGYRCSKASRQKICMRACTSCCRKCNCVPPGTSGNSDMCPCYASLKTHGGKLKCP', 'MAYKASTFLALILITNIMLSTTCQARNSIPKNSNTDEKKEPQWFFHFDGIPGFGRVGFPPLFGSTPQNPYNGGGGQGAGSESGPGSAPARGRYVPGGDDTFIPNPGVEVPIPGSGGVVPVPAAVHP', 'MAKHIFQYCLLGILCIALVLASGPTPGLSECDFPTLCDTIAICDANCRKIGFHKGMCTQIGLVTTCCCM', 'MKHQIIILFFVIILSSFLASPRLIVPPNGSRHGEKKELKIIGNTIAQSSTEVKEDMEQLMGLEECYEKDEECLSRRMNMETHLDYIYTQHHKP', 'MVEIVKFIYVMIIFFSLFLVVTKVDAVYWCFDNSDCPQHLCHELIIPRCKIGVCVCLP', 'MAKQSNTLILLILTTTTLTLFSVVTAKETVYDLLPKFGLPSGLLPNSVTDYSLSDDGRFVVHLSDTCYIQFDYLVYYEKTITGKLSYGSISDLKGIQVQRVFIWFNVDEIRVDLPPSNSIYFQVGIINKKLNVDQFKTVHSCRRNSLRSSPCQPSSSLLPAPLKEIPMLLTE', 'MAKTLQFVYYMILCISLFLVAKNINAIHCNDVNDCPPDISNPFVRCESNRCIYSRLEPPFGC', 'MSTSTDPNLEAKYALNSKVMIGSSILLFILILIILLFRTYVYICHLRSRRLFSQDSVTTTTFKEERLDPSVLKSLPTFTYSSSSSATGRTLHDCAVCLSEFTDGDECLTLPNCNHDFHSLCVEPWFASHSNCPLCRAPVQPAKVQSYTETCTVLGSGEPGEGCSNFPEPVWCPRKPFSVIVELPPEVVRVGSCTRR', 'MALVERGMVLSIMMVAMQISYAAVYKVGDSAGWTTLGNIDYKKWAATKNFQLGDTIIFEYSAKFHNVMRVTHAMYKSCNASSPIATFTTGNDTIKITNHGHHFFFCGVPGHCQAGQKVDINVLKVSVAASPAPSSSPSALASPAEATVPASNVPAPSPSNAAPQKFIALKMMLAFLAMQFLAVNF', 'LHALRQRHHSAHLPIFSKFQFPPHLFFYINPHPLPLYTHQSNKPRKTKTHFSLQRNIMASMKVACVLLMMCIIVAPMAEAAITCGTVTGSLAPCIGYLKGGSGPSAACCGGVKRLNSAATTTPDRQAACNCLKSAAGAISGLNPNIAAGLPGKCGVNIPYKISTSTNCATIRA', 'MAEETAPEIVDPNTAAAAATTVDMEVENVSESKEKRTREEEGEEEPKDDVVSKKAKVDEEKSVEEERLEKLEKNEGDVKDEKEASGDVKLGPKTFGSSLDMFHYFHKFLHAWPQNLNVNKYEHTMLLELLKNGHAEPDRKIGVGICAFQVRDHPRYNSRCYFLIREDDTADDFSFRKCVDHISPLPEEMQLKSEGNKKFGGGGGKHHGGNGGRGRGGRGGWRGGGGRGRGGRGRY', 'MGFDHHVICISLFFLVISSWILQGLVTEGRKIHKQSGFQQTVSEEKMILRAQIGSRPPKCDRRCRSCGHCEAIQVPTNPQVQNVKINSSKFSSIAYSRGNYNSNYKPMSWKCKCGNLIFNP', 'MKVVLIAFLLIASAMFHSTLAARLMKTETPISEDSKNLVGHTIKCAGKKDQPLTSCKPPITLKPSPKADRPCLPFPGKPGYPICNRPPANLPKP', 'MAYVKIAPFAVFLLAAFSLSFTVMFSMKKIEGAKCGEACDTQFNFCNAGDGCRCFITDAYLTLPGFCAQLTSIEKKVEELPNLCWSHAECIRKGSGNYCAHLPNSDIKYGFCFASISEAEDVLLKRDFLKMSVSA', 'MPPSTSTLPPESSGPPLYSLLFFILIAVGSGHRTEIWTHIYIYVSRDFMSNQQLYIVSYAKNDNK', 'MALAALAILGQISCFGSSISIIDRLSEVINAVDPMDFTNFDRSYSVVNEKLKLNCETETTETEVLAHGFY', 'MFVFVLRFLRTYMLSTQQQALVAMTLWSMWKTCNAKLWESTDTSPSYIVTRAKDTLNEWSCMQRAKAPIHNTNSEHAWIKPPVGMIKCNVDAAAFDNNSVMGYDRQAVHNRSTGGKYYINIINNTDNQE', 'MVEIVKFVYVMIIFLYLFLVSINLDDNPL', 'MVEILEFVYAMILFLPLFLVITEVDGDIYCETDADCPQITDWFYVVKCVDHKCELTKKLRRLYEYQTQKSAETPYI', 'MAKHISEYCLLAILCIALNMRFENVNFIFVAGPTPGLSFCYRPSVCDSQPNCDARCRAYTYNRGICILLVCCCE', 'MLTVFPFIVIILLHHFIRDYVQKGILDIQFIDTEHQWADIFTKPLSVERFDFIKKNLNMHFVSD', 'MDKIHKFIYALIFFLALFLVVNARNGCIVDPRCPYQQCRRPLYCRRR', 'MAGHNALTVLSYLPQLYTLFLIYCMFISDFDSNIYACVNASGPLVRNSVTDLDKEVDKLRMKYRSYIVEYDVEDGIVLLPKLFGAAFRLD', 'MYTSKKVGKMYYLSVIASFLVVITLTMAVNITGLNPKQKEAGQKPNRIPGIPHIQPPNIFRRPPPMS', 'MAKHISQYCLLGIFCIALVLASGPTPGSSLCNNKHFLCDNPDNCLATCHFYFCKRGCNKDLQPKKKEHTQRRGLSQDPQKATNLKLVIPSLF', 'MAQFLLFVYSLIIFLSLFFGEAAFERTETRMLTIPCTSDANCPKVISPCHTKCFDGFCGWYIEGSYEGP', 'MEFGETAPLLSLLLALYLKMSSTNTTTPVAAKTTRKNASGNQYDLGWKHGIDVLGTSTRVK', 'MARTLQFVYVMILFFSLFLVAKGDDVKIKCVSAIDCMDLFNLLPIVYKCINNICVYEQSSQRLI', 'MAKIIKFINLSILFIFMFLVVTDVSATTKCKNNFECRFLICMNPLVPKCKVPYCRCM', 'MKSFVPVVMAGVLGIYGLIIAVIISVIMVLGCQAINWTCTFRPLISLFFLGKGKRRKTLEFPNSGVGFTTGRC', 'MTKMNPLALAIILCTMVSSHCIIIHPKSNANLIQQTCKQTPNYANCIHYLKSDPRSSDADVTGLALIMVDIIKSKANTALNKINQLIKGSHDQKEALNSCAGRYRAILVADVPKSVAALKQGDPKFAEDGANDVAIEATTCENGFKGKSPISDENIDMHDVAVITAAIVKQLL', 'MFLKLVFMLFFLVTLVASEDNSFIYNNRGFQSFHLYLDGIAELTSGGLPRLTNDTKQYKGHALYPKPIVFKNTSSESVSSFSTTFLFAIKSPDPELTLSGHGIVLLFQLSPTKGQPNSLPGQYLGLFNNSNNGNSSNHVFGVHHTYSESQYVTSLYI', 'MTQIVKFVYIIIIFLSLFHAARNDDYHLKCTTDYDCREGFCPEGLAPKCFVSFALARFLSEGRCLCI', 'MQRVKKMSETLKFVYVLILFISIFHVVIVCDSIYFPVSRPCITDKDCPNMKHYKAKCRKGFCISSRVR', 'MKILLTALCFFLFVLIAAQEAVVQIEGCEVKSPTFNGHCNDPISDKICDINCRFGEGLINGSCKNQECMCVC', 'MFFYAFIIFLSAPLPSVRSDFPCKTKADCLQHIYYIVECIFGFCQYFKPLKHSV', 'MESKTIFLVLTLFLCSITIPVNSEYHSEGTLKSLHLKEKVTHLHFYLFDILSGNKPTAVQIAQPDTTVGPKSATPFGHVYAIDDPLREGPNKSSKVIGNAQGLYLSSSQSENLSLVLYADFGFTTGKFKGSSISVFSRNPITETNRELAVVGGRDKFRFARGFVQVKTHSLNYKNGDAILEYHVTLLHY', 'MKLSLLILFFLSLLISSSSSVIAPVKPLVEALSCDPNKPGIIRIWFCKDEECHRKCFAKYGSAGIKNPHCQGRDTCVCCFNN', 'MIKQFSVCYIQMRRNMTTILKFPYIMVICLLLLHVAAYEDFEKEIFDCKKDGDCDHMCVTPGIPKCTGYVCFCFENL', 'MTKTLGIVYAIILFISLFLVLQNTEFEDYYYIECQRDFDCPQLNSEIFAFKCIEKLCKLEFIYQQAPFLLGQV', 'MSPFTQSLLVICVLTFLSMHNVIGETTDRQIVVYNSLEGRLNLTLRCQSKDDDFGFHDLQPDEHFGWNFNVNIIHTTLYFCSVKWNNEFHHFDAFRYNRDRYRFVLYWYIKEAGPCVVSAEGINGCYPWK', 'MVNQMSNYLLSLMLVLFVVGQVTVKGSETCSQFIAGCLDTPSCDSYCKEQYSDGNGICANERCICNFTCGGKPGKKAPKRDCEVNLGFCDNGGSPSCNDKCSSRYQAGTGYCDASIDPRFATCTCLYVC', 'MTLQSPVFILIVLLVLSATNQALTFIDWSPVDINDPHVVEIAKFAVTEFNKRITIEKLTFENVIHGESKNVVDGTVYRLTFSAHGLSTSYKYRAIVFEKPSEHYRKLASFELVHA', 'MVLVALGAPEFFFTALVCIFLTLLAGKSCCSFLQSNFNKLVKIKTRMNPEYVFRREQSIPPLQTIKLLLTGDANDGTTQTDMRFVL', 'MDSGSIIIYLLLVSFVSPIYSFKTSSYQLENQTFQIEKFHKLKKTIKNRLQQINKPPIKTIQSSDGDIVDCVSVKREETNALSLGIQIILSIPGSVWLDTLPIELHLDFFRDRVGLSNYKFP', 'MEKKSIAGLCLLFLVLFVAQEIAVTEARTCEHLADTYRGPCFTEGSCDDHCKNKAHLISGTCHNFQCFCTQNC', 'MGSFSQLCLLGLTLIMGSHVAHAQDSPADYVNAHNKARSAITTIKIPNIVWDNDIAAFAQNYANQRKDCKQIPSGSGGRYGEYLGENIAVSTGYISGAEAVKLWVDEEPYFNHYANSCIDGHECHHYTQVVWEKSLRVGCGKVKCDNGGSFVTCNYDPPGNIAGQLPY', 'MAFQKSYTMTLNPLMFLFCLLLVMTNFVTISGSRQFKNIPIKEKEVIGGGKVEDQVNPSKNYNIEKTQVDKNQLPPFPLPAPPLPIPTLPVPGVPRLPIPSPPLLPPGDIPGVPPLPVPSPPIE', 'MTKAIKFVYIMILFLPPILVGAGEIPYHQCKFDMECMLMKCVPGKVNVCSLGRCYCVNS', 'MLIAVLTSAMVPTLNCSASACHIAAKGRFATLLLHAIDNTITGYEKKMNSEDPTDISATITLLAAALECKLITSCNKPSYSLLKKIDFMDAVLRHLLGDTRFRWETHNPASPVLGTPQAQRMNDKRVTHNSSKNSTSAQLQYMYVHQNFPNLILAIAT', 'MNPKNCWILGSLGLTLSLAGHVFCYMSMRMKFQIEVYLVARWCPKEQVLNHP', 'MPLIVLIFFLNIPLSAYLKNPITFNFNLHFFLLQSRKISLEHMCFWGLLPIHLCACKCRILTRE', 'MASKYSQVLLLLLGMMVLTTVVSYGHNKFGYAPMLDLEEDWPDQFFPPLTIIEGRKSHSFGYPPILDPEED', 'MTTFLKVAYIMIICVFVLHLAAQVDSQKRLHGCKEDRDCDNICSVHAVTKCIGNMCRCLANVK', 'MNTKSIIFVSFICELIFISVVTIEPSKDEKEFCEIEEFKLKVSWRPPGGSWTSHGVKGKGGKGGSKGGSGTGGNGSEGGAQGGGEQIEGGNDKESELDGGGGGGDQIIK', 'MKIIHIVFLFSFLSFTTSQASVNDFCVANLKAPKTNSGYPCKPLASVTSDDFVFHGLVAGKTNNTFKLGTTLASVTNFPALNGLGISAMRADIGEGGSAPMHTHPDATELIILVQGEFTVGFITPTSVYSKVLKPGDLFVVPQGMLHFVLNTGKGVATAYVSFSSENPTVHLLDFLLFGNKLPSNLVSQTTLIGLDQVKKLKAHFGGSG', 'MVSKRQALTLFLVLTTSPFTISSSNGGNMVVYWGQNIEESTLKSTCDTGFYKIVLLSFLNIFQEGRRIPKLNFIGHCNDKNPCTNLEPEIIHCQQKGVKVFLSLGGAYENETYSLGSLEDAKNVANYLFTNFLNGQFGPLGSVTLNGISLDIQGGSDQWEFFAKYLLYVRQNYRLGLREVILSVRERKKGHNAK', 'MILHVTHSLSLSLSLSLSLSLSLSGRLHNLC', 'MQYQHVPLDGIVLIFNGFLLFLRLIEGLVKHLKQFGLDFKGIFPIFVSHVDKLCHLTESNQ', 'MAYFKKLMLKMALLFVLLISIKLATARVPLWISEPHLKAHGRLLLTADDPRDHHYFHHNP', 'MFHADSSFIECMIFLLILIILLRYIKKKLKQH', 'MLMLMISIAILRRFPKKIFSAHLSQLSIIFLWLSGMYLCGANALGFCFFSLPNHFETQVHRRSMSQRIYRYSSQ', 'MAQFLIFVYALIIFLFPFLIEASTFRLTFFFLIVNAVNIRCVTVDDCPKVIKPLLMWCINKYCQYYFSEIP', 'MKMIVAISFIFSLLSLSHASVVDFCVADYNLPNGPAGYSCKTPSKVTENDFAYHGLAAAGNTSNIIKAAVTPAFDAQFAGLNGLGISLARLDLAAGGVIPLHTHPGASEVLVVIQGTICAGFVSSANTVYIKTLYKGDVMVFPQGLLHFQINSGGSNALAFVSFSSANPGLQILDFALFKSDFPTELITATTFIDAAVVKKLKGVLGGSG', 'MNLTLVTTLCFLLFSFTTYFPLPFVSSSFPSSPTDADLIVKDIHGNPVVPSGRYFIWPEFLVSGGGFRLGETENSTCPFTVLQDYSNLGHGLPVKLTPQNQPSSDDPITRGLHLDIAFDYKPDCAESSKWLMVEAENEFPTPWLAIDGTGKNVHDGWFELTRYKRSGYLIFFCYKNDGCIYLSRKNDKNGMRLVYETDGDGDALGAVFVKDDDAARARISSVV', 'MAKYISVSFVSVLLVVTTMINGAYGAEGDDGEPLPPGLQKLCDDWYWKCYDYPNSVYCQWYNKRFCLAAPITCDSSPKPESTLP', 'MWSNMRKHILTIYMFLFAFDTFDFLVSEVVDLPHRVQRAMHNNVMTLKSINIVFTKIGFAIQKVLTP', 'MVCHSGRFGGWWCFSWRCQWCVAVLGDVQVAVMQSTCGDLRSKIYVEEVMVVQGGDVGHVFGGVRRCWWVFNGWVRS', 'MVHFIILLLIRLFSPLITSSYLPSIVSTNGSETQSQGIGAIDIDQIISSLSITFVLYVPESPFDLIFISRSTRFHDCVVTFTNGKVNLQDQLSRQKNWCWM', 'MDEVFKFVYVMIIFPFLILDVATNAEKIRRCFNDAHCPPDMCTLGVIPKCSRFTICIC', 'MLFYVFLVRSASSVLFFVLATFPLLAGSDTGVGRWCEGWIRRVLMLDSIPLAVWLRFYSIRVEEYKEITCRGSNRVLLDLETKA', 'MGPSGICIICLILSLLTSIHILSLLTRKFLLLLLPNYILMSTHGTIRNVPKLNKSLHMGC', 'MAKIINFVYNMIIFFSLFLVATNAGGCNPCLVTCPDDLLNRCPPGMEPICEYGVIKCYPIGKETNRVLT', 'MTKCSALIFLLVLLICITGKPASVEARGPFECPHMVDCIKVCQGYPYCCVKGFCICKTCPPSLNDLSIIQPNSHLN', 'MTSQSNQSAYIMGVLCITLILISGFVTSLRIPTLDSTCVVLQRKETRKACCVEDCSPNCDADCKSRGFPIGGSCDPRTGNELCCCFME', 'MLLYNLLMPAISPAWLHLTVAAAWLHLPELITSKLKDSDTFKFAPEEAAAAAKTEGIFPVCVDQLSFFPVPILISDKSKTTSIVCALCQLFALQSLS', 'MASSINISRVTASFALFSLLLSTVADSSVANSPVANAPVAANAVVADSSLANCSRTCKAELCDTMGIKYGKYCGVGYWGCPGEKPCDDIDACCMGHDECVDRFGMTHVKCHKRLKNCLIREQKANKVGFSKECPANVAVPTMIKGMDLAILLSELGGNMPDIEKFI', 'MRTIFLLLLISGLFRYSLAQEEGFCSAPSVLSKESKSKPLYSKIYDSTLSPLNLQNLPGFTRSVYKRDHALISPESHVYGPLPDCWYATDFCFTTFAENSRSGLPLHDVERFIFVLHGAVTVTSAHGESHILKIIWLHSDLAHYLNPISRSVSHKALHWIMTMMSVLRSNIARTSYWSLSLESP', 'MSQDSSLLLVLFVLCVTSSNSISAKVIDVDVICKEASNQSYCSNLLNSKPGGAKANYYYRCSLDLLSNGGSVSTKVGDAQVDLYYKRYAAMAKESADIMKYILECIDSLHKHETSPLLAKYVENLRQGAQVFQIITKYLNLGK', 'MVKFLAMLLLLLQLISFNVFGEELESLHEPTPLHPSANAPLHHRHNHNLSPTTAHTPLHPSHPAKPPTRHHHQHPPAHAPLQPSSHQHPPSHAPIHHHQPRHPVKPPTHHHHQHPPAHAPVQPPTHRHHHHPPAHPPVHQQHPPAHAPAQIAVEGVVYVKSCKHAGVDTLSNATSLNGVEGAVVKLQCNNTKHNVVRKGKTDKNGYFYIKGPKDISIFAVHKCNVVLVSAPNGLKPSNINGGITGARIKHKKSFVSKAHNLILYNVKPLAFEPKCTN', 'MATHFFRSFLFGLLCIAFVLASGPTPSSLHPSGAVNCLGPVPCDQCPQRCIAIGANSYICGILTCCCYYN', 'MGCYMKIMLVFTTIILLLSSQQIRARPLLIEKPLLLLQKIVAIQSLQRGPVQGSQTNPCSTVPGRSRGRCALADVNLSDHVAQPPPPQPPIQP', 'MPKPNPFGEKGMAAPSSSNLHYILFLLMFLLILINGEDSNSDRSWVMIFRLNWVFPTTDLSHCNHRPIFLPSAKQLKAMNNESCAHVRLAVNINYFLFAKEYIG', 'MCRTWRFLCLCILRSHDMVLHWVEGFEVISDDQALYIIKNFDIEEKVVAGKKAKDGEKLDKDNSFHLDDIDIDLQAV', 'MAMQFNHLSFAVSIMCIVFVMVAGFQDSPHLDKPDCFLSFPCPQNDGPCKKVCIEYGFPKGGVCQPNGNKCCCIGN', 'MVLSSSKLNSAWILFTIGVSGLLVTSQVKSERLKVIDRAEDFKTRMDNILDHETDGVKEQNHLGLKMVLKILIKVMQRHNKSKIKIHNDANGVATNVVEIRNILIRHFQDIFTSSNPNNTKESTQVVQGRIIDNLKNLLGMEFPEEKINQDIKNMKPNGIFYQKFWNLKKEVYTRL', 'MGQIEKFVYSLIIILSLALVVTCNGIPICQTYMDCPSDMCTRPKHAYCVSYKCYCV', 'MVTLQTTTSMCCFMIMLLFLSVTKSFGDLVTDHKNLTTVTKDEVKCTPCGQVSSPPPPSPPPPSPPPASTNNCPPPPSPPSSGGGSGSTYYSPPPPSVYYYSSPPPPASTGGGGGGGLYYPPPTGGSGSGSGGNYGTPPPPNPIVPYFPFYYHTPPPGAAAPPSLKGSVMLCAVSVLPIFMALF', 'MQREKNMAKIFEFVYAMIIFILLFLVEKNVVAYLKFECKTDDDCQKSLLKTYVWKCVKNECYFFAKK', 'MKISLLLFLGFIATMVDARLDPSPTTLNGVDFKLLNEVCCDDCSCSSSTFPPCTCNDSKRECNPGCDNCACETGPTCHCNDYFFSCNKCTNSKDGAN', 'INQSSARSINGITTYIVEIANTCVRGCKISNIHVACGGLSSAHFIKPTIFKRLNKNECLVNNGKILQNGGIVSFQYANRIRYPLSVSSVRCN', 'MAKTPKLVYVLILFLSIIFSIIVSNSFPDKIFIGDCKTDKDCKPKRGVNFRCRKGKCYPR', 'MAAGCCFKFGYGWCFMVKALCFCLGVESLVISSSTFPLTFVLGLRGVDMHMAHWLTQGYACDVSCESYASAASIGCFGIWLDKSSERLSEVANRQRVCKDVFDDSRNIMNELKDDGTKPRLN', 'MLQSECTWWVMLCIVITRCFFSHPLNNRKLTSGSQIKHLEPPCSGPSHIHTVSLIYILMLPYLPNSANNLLCACVTKEVYISIHDRTCLDEFSLPDHIQNALLVAILHILLPRTEWLPIVIRQTRVEI', 'MVRLPNFFIISMIVPCGMAAPLLLKWFVSIDVYTGAPFSNWTITPILIPSFPLFPKLTKKVVDYTKTGFNLVSH', 'MTSGLTISVECELLIAIAFAILPSYVGNSEVRRKKVLFHQNSSANCDVFTRF', 'MAIVLVIGIVSGLNLVYALVHKAAAPSEDVSNGWDIRLWPDGIKLLNLRFYYLWTIYHQLTKIQEESNNISLPPNRDPSLTTDDPNTLINAADSRKFQAHGKPNISKLN', 'MAKSTIPLVFTLCFLSFLGSAYSKSDRFFVEGVVYCDTCRTQFITRLTEFIEGATVRVECKEDNGTLTFNKEATTDKSGKYSVEIDGDHEDETCEVILCKSPRKDCSEVDSEAHLQQAARISVTNNNGIVSPVRAASPLGFLKKERLPKCGEVLKELGINEDGTEKED', 'METFVFGAIRPRPESCEPEKEPEPESESNSKSKVVLRPNWDWQLKPLEDLTPFNAIPVPFLAMTCSSNFPRPIDIDDDDDDKRPLLTHLSKFSLDKYNAENKGSNYEFEKLVKAARRPVPRGTYYITFEAKDATDTDASNRPATTFQAHVWNKSSARKKFEKELPVVKSCSIKT', 'MAKRGLKICLIVSTLFLIIISIVTLTLILTIFKPKDPTIRANFAQFNFLSANITMNMTLGMVITIVNPNYAGFKYQNSISYINYHDTIVGNVPIESQLVPARSEINVTTSADLMVGKLLQNPQFWKDMVHNRMIINFTSTTELPGKAILLKYIKLKAITYCSCDISVNITSNGADSNCKSRIKLFYFGS', 'MAKHISQHCFLGILCIALVLASGPTPGYSCYYPTFCPSFGYCDLRCRAAGFIRGVCTDIEGSISCCCMK', 'MTKFSFIQIIFVAFIFSAIALTSDGCITTLPPGDCTLDACGKWCHDTYQGYGRCIPADKSINPKYYCLCNHIIGCPI', 'MKKDNLILKFMVTCWCLVNLLGGILAQSKNESNIVVKVGAVIDISSNETVGKIGLSCINMSLSDFYLSHSHYKTRIQLIVRDSHRDVVAAAAHGNSY', 'MSTISFTIFILANVWLLVVTTSIAQFVIDTSGEPVENDEDYFIRPAITGNGGSLTLVTRNSCPFNVGLDPDAPQGFAVLLSPFVSNREEDEVRLGRDLRVIFQAGTSCGQSTEWRLGERDATTGRRFIITGRDDSTVGSYGNFFRIVQTPSRGIFNIQWCPTEVCPSCKFECGTVGIVRENGKILLALDGSALPVAFQKE', 'MTTILKFAYVMIICLFLLQVAAQEVLVIHECNRDRDCHISCVPPELPKCIAHMCFCFNS', 'MAGFHSISSFLLVRLLIIPDQKVIAGVVNVVVVTVVEYVRMLLGADGECVPRGTPTWIFLSQEVIKIGDYEFLPDCEHVSGVPAILWFSFALQPNYFSHRKGRVCHENARLEERFPRLEVWSIVILPSMMYFTGHFIHVTCTLHVLHVLDDSGFTYEQPYELSSERSDKLTDQHSELTF', 'MSYAKLTPLALFLFATFLMFPMKKVEAQSCIGFCSVFDSKPLCGSSRCRCNKPLNNPFVGICERRPSTDAIEMEEEHQKFCQSHTDCTERGIGTFCARYPNSDNEYGWCFASSSEAEEYFKIASKYKFTKEFLKMPITA', 'MVLHVYLAGTVMLIFIMGLYGLFISNIPHDISRSVDRSLKGSSLWNVCFEGNRKPYIHVGGTVAVLHSLYE', 'FIWNWLGLSSVVSANLPDHSLQSCQTGGIQISFRSFKHLVWLSCVWTILVDKIKVQSYWWLNAKNHIFASDYHVWWLNPLLWMGFTI', 'MEWQKYYMDIMFVPCGFLILVLYHFLLWKITRSQPMNTTFGRDADGRRHWVPAIMKMLSFKFVKRSGELCCREG', 'MLGFSFGEIFLLLGATAAVIGPKDLPMISRTAGRLAGRAVGYVQLARGQFDSVMQRSQVNQVHKELQETMAQLEAIRHEIGSISFINPGPSTRRHDNLDQASISNDNRKPEYAGVNNSISSHTKDSTSLPSKSLNMQSQATAYASLAQAPAVKNGSSANSTEVEDVKVGLQLIVMPVSAENTGLLPNRGGADVKGSDIVQEAILEAEVAHNAKEFFSQPQNQI', 'MASIFKLLSLTLVLALVFQAYGDCSLSDISIRHYETHDYAHGMPVWKVNITNNCICTQSQEKLNCTGFQSYLGIDEALLTVSDTECLVKQGAPIYSSESVIFRYAWLPKFQFKPISSNIACS', 'MAKYLLFIWAFLLVVILAVFLSCILWSIISVKDCQVGKVKTYQWMVDFLTHLSCLRFWITFFPFSRYHQGSEEASKINWIHRDKACSDKELGVLEVYADQGWSWYKVLVANWGQGIILAKGAVMSQCLPVWYNDKNTRKMYFSDPSFPRAGLPKAFE', 'MTPLGCICLIVFFIGVGLFITKMFRLVDLELFEPEFQVDSVGSLNSLTVNGPQFSAELNISVSAYNPTDYSCAYYEAVSAEVFYGGEGLVLSKTSLPSFTTHSQSTSVMKMTLLVNKSDDFGGVATAIAQSRKNGMVEFGLIVSALFKYKNRWDQSKWTSLKAVCKPLKFAVSPNDYNTTIPGILLKGSRC', 'MTTSISIQQYIFAILCFALVIMNSGVTSENIHYKVCITKGPCPNNDPACAKFCQNKSYPKGGNCIDNVCCCDK', 'MHHNTIITSTLIHTLEVHSLFKNFQNNHGYLNVCQGYLLDCDMLGSGHIHDQCCPFMSSGATHYSTIPCLGYLRNPGPSVPAPYCNGIKGLNNQVKTTHDRQSVCRCLKSTALSLSGLNLPAALLLFLESVVSTCPIKLVPPLTATRMYMFEHWCKVGRKLED', 'MTIRWQGLWAMVAALICDISIVTQYVTMSLPSILVNYDWLSKRKVGDVGHVYWYEYGSKTAIHTPTSGEQLLSFGQQGNKEM', 'MSFNNTFSLFSLFLTILSLWFSFSNAQNLPQDFVDAHNTARAQVGVANITWDNTVATYALNYANSRKSDCNLVHSNGPYGENLAKGSSGTFTGVTAGVLGYNVPMDGGLSLATMIPRAIMLVKNPIISNNKLRQ', 'MTDGGATRVSALCITVPFRRSVALSVVVKVVFMVVLILGLLWRGSWWFLVVALVVVFGGAARGGWCCFLVSVNGGFCGGQW', 'LKYSLFMFRFYFFLYLLSRYMIMFITIILLVLLIKIVHLHNPAVILGVVNTIVILLV', 'MTSKGVGLKILSLNLFMLVDTLIIMLWIYYKRDITNLEVVENLVYVGTNPQVHNDGDDGGDKEEERIFDGLSLGAKERGLGTDLVRKYEDWETKIYLPQVRNLAQPVLGLARPCHPPELLLLLLLRF', 'NAKHFLFSFLHSFITFFFIIPKSNFQFLYLSSIFNLNLKLSLRNPITHTERKKMDWGNVTAEDLIDALREVDWSSPPRPLSEFFSRFTVPRSSNKWNSRLKCNLY', 'MGQCFDVVYAVGDLAGEVILEEIEAPEVLSNFNLNDDCSISVNSSQSRLRERERTRERRVSLARAGILLLWVMLVFALIVTLFFSINNETKNNNNNNHSNIRLLRQRTFNKALLSHAPSKQRTRTTHHHDQARTTTNVGDPLYGDDKRLIHTGPNPLHN', 'MVHYYSFLLVLLLCVTSSYSSKIVEVNVICQKAKNPSFCSTLLNSKPEGANGADLVSLAQYTIDVLRVNLTNTVKLINTLIS', 'MASWRRVISFFCLFVLFSIFFSLLDVLSSNPAQMGETIDQFSGAVTDDLHIHLKQFTEVAENFKIPGIEDHGFKFRLFPYSLTRRDKATWPMVN', 'MCCCCGEDCKFRPLGFLLGLPFAFLSLIISLVGAVIWIVGLTLTCICPCCLCVTLIVEFALALIKAPIKVMEWFTSKIPC', 'MDSNKVLIALLTLVLLFHYSLTESTTLSHNHHRHLNKEEKQEQEQELSVQRRGSGVRRSLRCDPFYLYLYGYCGQWPFPTYRSPNNPFDQTPPPFRYFPRPPRLIPPPLVPSPPPPVVFQPPLIIPPPMPLIFSPPPSPWWISSPPPPQPWWYSPSPPPPSPDPIPWYIQAPPPPPPEPIPWYIPAPPPPLV', 'MEFSSSLRFLLITLVLFKLLLRFFSPIFYCLPSLSQKLLFSTLIRRQGGREWLWLHECHTYLQGGLGKYCL', 'MANLSFLLPVLALSLLAVFAVDAATTLQDFCVTNPIGQGNSLCKDPKLVEANDFLFSGLHIAGNTTNPAGSRVTPVFASQLPGLNTLGISMARVDIAPWGVNPPHSHPRATEIFTVLEGTLEVGFITSNPENRHFKKVLQKGDVFVFPIGLIHYQRNIGNCNVVAIAALSSQNPGAITIGNAVFGATPEIASEVLIKAFKLDKNAINYLQSKF', 'MPVSLSHLSLIVLCSLFPHFSLADIVVVKAEERRASIVVVMEGVDVGGDGGERARGCW', 'MGWSYVKLVLEFGIICLIGSFVGVEVLQAPPLRFVITVLGCELLDVSNETLALGMYEILTIEVYANAME', 'MATKFTLVFLFLTLLSIATSTPTNFIKSSCSTTSYPTLCVESLSVYATTIQQDPHQLVQTALSLSLNKTQSTKGFVTKCKSFKGLKPREYAALHDCVEEITDSVDRLSRSLKELKLCKIQGEDFSWHISNVETWVSSALTDESTCSDGFGGKALDGRMKASIRSRMVNVAQVTSNALSLINQYATKH', 'MVGTLKFLRVLISFLTIFLMIIICAFYFIPDSGPCVTDKDCEQEIGYIVKCDTNTGFCVKILQRS', 'MKPLCTLIIVVIFALTLILSLEVNVAKARTYPSSSNVSKMLSSFQSLSHKKGSSKSSQEVSASLKRIPPSRPNPTQNKLKPRIKD', 'MENTKTLLFLSLLLAMVVLMSTNGAARDLAETSSIFTRGRVLAYLPRRTLIIDDKCDKSSIQINQAPTTPLPNGIPQYTVEIVNTCLSGYNISNIHIDCGMFSSARLIDPTIFKRLNYSDCLVNSGKPFPNGKVISFSYANTYPYPLSVSSVVCY', 'MGWLHMALILFEILDLAKISPMANTPWNNFAISEIFADSELLQFCFSDFAKTSLSAYNTLSGTISLMEKCSLMVKYLVNFDYFLSFACIFLHKKG', 'MRMLSMIIQPRHSFLRRIMLLLSAQILLTFPRTKVSVVDGPLLPMFGLTLTNIVEVNHQTVDNDYLIDGIGLMTVISEVREYIKDGKITKMILL', 'MLSAVILSALGYPAFTVGTITGTPEVRPSRSSRTRERSSQCSNAHTGYGPNCLTTF', 'MDFEVKLSPTLIFFKPYSSHAHGNLEVKCCTQSQSKETTSSVQIKKQCLRCNTLYSDQQNSPVSCSFHGHTNGIAEKECITDDDCNRKYPMHANRGLQCLNGECKSSRIIKSR', 'MKIKHFIFVFFLYALILISVVAINSSKDGEQFGATEEFKTKIVVDMDEGLKGVSHGEDHGGGHVDGGLGFWGNLGGLKGWFEWMANEAISGIGESQGGFGVSWGGGGNMGGGGGNMGGGGNMGGGGGNMGGRGGGNMGGGGGGGNMGGWGGGGNMGGRGGGNMGGWGGGENEVAMGRGEYSGYGGGCGGGEYQMPHMRGGEQEGFENQGPWECP', 'MMVSVSKIALLILMSLTILDALQFADGKIDGYYTIYEHIINNMTNTALGVRCKDKNHDAGFRRINFQQVYTFSFKPNPIARVTLWFCRFTWNNDFQYFDIYIQKRDRSCTKDCAWFINRSGPCRLKGTSLDCFPWNPKVAIDYEHMQLGHENNTLDV', 'MSEIVKFIYLMIIFLSLFIVAMNANAFSICQNNSDCKDQEICLPPKKHWCNKIVPVMIEETMVGNCECI', 'MDSFFKVVLKLVVLLFAVVSTLVGGVQGNEVIPETGGGNVCVPYIPCHRPGPCCPPPSRKATNP', 'MIMSRSQEIRNTMWCQLCHLMLLFPYLLFILSSVTSFRGFNWSDVQALDAKDKPAADINVSGECKFNVFPLFSVINFG', 'MKFIFILLSSLLLGGLLILTFQRDSLARVVSGQEWNKNRLEVAEITAISSQKEPFQRITGFFHMRKLGFGTIMHHEETSVDSKNGKRETSKISGNENDGLRKSFRSLFQVKMNDAQKHVTMKANTKFAIPRNSLTNTNTKCSQNCDVVPIKGSPERSTRSIQISEEAQSIDAAKEIGSLMYKDYSHKGKPSHRPPINNHQPNNP', 'MAETLKIVYIVILLVSLCLVVVDGISIYVRCASTNECYTTFKFAPLGSMRCVEGYCKHLKDFKVTQFLYCV', 'MTKFITTSFVLVLIVVAFLNGAYSVDEGEGRGNNKMGNDDPNLYKSEKFGPTIDCAILLYNCLASPIPFVPNPACLLYNQFCAQKPPTPSTLP', 'MILIYPHALLVHLVTLIQIDHWSKSTSTKSIRRKHSHTSKWAIPIIEGGNIQNIVDMRLQGEFSIDSAWKVVEIAMSCISQTATERPDISQIFAELKKCVPLEMVQTNNGSTRSRDDLVSVATVSETTILAR', 'MASISMVIAPKCKQHAFNGEGDCDWFGYTNISCIEEDYYNGEGDDDDDDGGYDYAPAA', 'MGIYKVYLVVVMSLLFISLVVADRVTSYNIKRKVPTGPNPQLSPDPVPVMAMTTNYNFIHEIKRKVPTGLNPTQSPDPVPAVERSNDCNFNHVIKRKVLTGPNPAQSPDLVPNACGGLSRDHRGSLLGAFACNIGVSTVFYAEVYGFLLALEYGTQHGWRHIWLESDSTSALLVLKTVLWYRFYFVTVGTTLATKVYRLLLLTSLGKEIVVQICWLIWVTRHKARFGSRCCLKLFRLISS', 'MSLRFLRRWWLLCSKTLVFGATLIGERGVREYGIMSLAWYVKVSHPRIIPTDEGSPPRSANREQLIDEEHAREMPDTLTIIKDVVQISNNDVAMSAEMTTEEFVQKMIRIGSTARPAPMYRIAWQRMGHRHRRQHELKNLKLDEND', 'MGMVVVISLPFIFFCLVIGFGCYFFGRARGRRDVYTNPQVYGMPIPPPGTAAANSSFPSSSPPPQYSKPNLASNV', 'MLVVFLYTKIYVSLLLLKTILLLLTSSFDCGSRDGIEIGKEAKGNLKDYDVSVLVDGKVCSEGKES', 'MAQPLTQSNPQATKPKSSMLLRYIAIIILALIIIVGLAVLISWLVVRPKNLQYSVEDASIHNFNLTDANHLYANFDFTIRSKNPNSKVSLYYDSIEVSVRYEDQTLATNAVQPFFQPHKNVTRMHARLTAQTVALYDSVPKDLKLERSSGDIALDVFIRARIRFKVGLLKTRHRTLRISCSPVLVNFSKAKSFERAYCDVEM', 'MVLLVLMVATAAFTSLGTTSPRYIKQQAMYFPWRGSHLAIMEEGSKALLVISATESCSW', 'MARSSIYIVLVVFFFATALAKLTVNQQQYLRDCAVKMGKQCGTQFFNKLFTHDKTIITRDCCYKILQVGYSCHIKMTVFILENDPGFKNADRNDYLTKSDHIFQKCDRVTEPENQKFLAKCVEKIGADCGDQVYNNLTRDGSITKQCCKKLVKTGEKCHTNMAKALIRTPAMANIDPDEFLEKNQKIFDDCERTE', 'MHPSNTLLSDTIGLFLLVVEVSAIKKTGPDNLEVALLLQAYMMDLKAQFDGPAQAYVVEARLDKGRGPLVTTIVKVVCGQHAVEGSQWGKIRSTKDAAGRLTQRATPAMPVEIEGIRGLPMAGDDVIVVHSEERAKFLDMCMAHMTCVYELHVRRGKFAAKSPLVTLCIKL', 'MAMLGKWYWCMLHEWEGLWLKVLAARYGVVWGRLQEGGRRGSSWWKEVRLIRDGGGAQVGGWFDENIRRIVGWGENGEAWKWRRRLLAWEEEMLVVCKYVLLDVFLQVDIPYRWLWLPDLVKGFTVRGAYNLIISDSDDPYQVATIPSVIWIRKISLKGAMISSVSKEDWSLNGW', 'MAYIFNLFSISLRFLILMIIAFALFVQLAPFVSADMKIRKLGNSPSPPPPPIQNEHQGHP', 'MEIALKMFFKFFMLVVVIASAMVGNVKALPPCCRTEPIECQPITCPSLYINGEFVVDPAKYFGLILVHVSLGKSTYQIYMQNI', 'MPSSFKFIRYHLFIIMLLVFAATQIFSSSTHDSPFNSFLDITKSPKVYDWMINIRRKIHKNP', 'MAMAAEKINNTNQITETNHQQQEEQTCFPCKTFVQKCDHFVKNQRAKFYIFRRCIMLLLCWNESSSD', 'MAETFKFVYVLILFISICFVITISDSLNLADYSCIKDKDCPKVKSFNIRCRKGFCVNIL', 'MRLQSVVFLLLGFLATMSVRNQAIAGGWEPIKNINDPHVIDIANYAVTEHDKQAGLNLKLEKVISGETKVVDGIIYCLNITASDGSASNKYNLAVLEKLEQHFRNLTSFVPLQN', 'TSQTCRSFMQLIWLLGVWIVWNERNNMIFNNIQTSMIDLLDKVKYNSFWLLIANHAYFVYDSQRWWLDPLLCLSID', 'MVLHAATLLKLHLQLAKPAVAGFSTTICVLKLLMGFRFFKDEALYQSKLFLFRLSQIAFNNEPQVSTVQRMERALRLIFPTHATVTPSINSTSSQEEQDLQNDQEEMFYSLSMLAL', 'MSSWSDNFRTIFQVFMLSIITALVIDFIYLTIRMCYEIFIKWWRSSSPATVAPETGNEVEMVPV', 'MIISIRRSLTACCIFLCSPLISWKSKRQNKVSHLSSEAEDRVLAFTRSELQ', 'MAFPFLIVGTVLITFVWVLNLFVVVHTLETNISLGNVSFSFTHTKAIEDDTISVVIIVAEIFKLKVDKVKLIEVFGINLNFQV', 'MVMKEHRKLVSLLMVVVIMASMLKFSNGMSLCNMNEDGLDACKPSVTQPYPAKPSTECCKALTGADLQCLCSYKNSAELPLLGIDPTLAASLPKECDLTPPSNC', 'MRVTKYGLFVAVGFVMLDVHVVGARIYGGSRIVDPLIEYGHVIGKLILEYMVDRIVLILMIYRIR', 'MQREKNMTKTFKFVIATILFLTLFFIVKNVDAQVKCKTVKDCPIRRNRKYYCLFGICKYDVM', 'MMYLLLALPVSLHLHSCIIVIYDDFSAFPPHSLQVKLRQKKKKKNRKRKGK', 'MAKLTKFFYVMIHFISLLLITTNVHAYDDCYNHAECKTKIKCVLPRIAECVRFKCDCVRLNVPRTPWSTRPENAHKPYKEE', 'MASSSPKLFTIFLFLILVVLLFSTSEVQAKLCRGRSKLWSGPCINSKCKRQCINVERAVSGGCHLDNTGVFCFCDFKC', 'MGLCCYGRCYLRLHRWFDQSVALLLFGGSVVADKICGRFDVVQFQNPKGSVDFKNPKWSGVTNVLESSLDLGSYVYA', 'MVALLRIVRICGTGCPSVFGLQLLLRLVVSYLEVVSIVIVDHIELRDHFLRSGHFTGLLCSSHSFFRLIWLASVWTIWKEKNNRVFKNTTSNLHLLLEKVKLLSFVWLKAKLFTFAFDYHDWW', 'MTIALTRCLLKVFIETVFVFLSVRLMMVVRMTATVMVFSNLYTQIKNQIKGNTSFVISLPGWDTRAKESGFGADLVRKYEDSKTKIYLPQVINLARPVLRLVSFGSFCLGSTLEGISFYISWNLDLVASGYLWCEVCSTYDLLPL', 'MAKTTASFVLVVFVVTLLILNGYSAVEGEGRGNQHEKDEVGGYNSRSHLYECYKLLKLCLRDPSNNCPEYYEKYK', 'MSLTPRSHVPMFLLILVFILSLTWTTEESSLEKILHNHGLPAGLFPQSVKSFKLDQMGHLEVHFDRPCLAQYETTMFFDTVVKANLSYGQLKILEGMFSEELFLWLPVKDIMVIDPTSGLIVIDIGFAFKFLSLSRFDEPPICRSYLGLSFRMDGRKGIEFEDQRKGYSSFV', 'RIVSVAFLILLLIKWQRWLKCILLSGGVEGVAWHWRRHLLAWEEEKVHECCDIVLQPTHPGRCIWHLHAFNNYNVSSAYNHMLTSTSNNLAVTNIIEIWNKEVPLKISLFAWRLLRDRLPTTDN', 'MALNLCMFIPRTLDDKKMIILPPKMAGVEGDLSPYDCSTIVCPSRCARSQYKNECISYCSMCCQKCSCPYALAEKILNKGNGEVPINNVEDDCKYICGKSCGRIGNKDGCLEKCYSCCAQCVCPFPGFSEQLLLNTNTSMLH', 'MASSVVFRCCLIVITNLFMAFDFAHAQPTRAFFVFGDSLADNGNNHLLFTTLRADFPPYGIDYPTHKPTGRFSNGLNIPDIISEQLGLEQTLPYLSPLLLGEKLLVGANFASAGVGILNDTGIQFLQILHIHKQLKLFEQYQRRLRAHIGAEEAKKLVNKALVLIIIGGNDFVNNYYWAPFSARSRQFSLPDYVTYVISEFKNILKVILLLS', 'MSIINVISKFMLLLLLLLNPHSNITKRCNYAPFYVDAARPLEQNVPKYINLKPEKGNGEGRVFQGRNVEDCLPKGFRRSSAPSRYINYQPLGNSCSSTKTVVNEFEIVFNSPEYIEPPRYA', 'MACIFCMFTHPLVRLISSTFSIFAVTLLIPKRMNDSSFQKYEGLLNHFHQSWEVDQVSKTPQTPSKCP', 'MLTWSLMVMIDVGWLKTIDQYYVGSNHTIQRACVQSVLDSLIPALLADKNRKFIYAEHARVKTIIYV', 'MFIGIFVAISMLVDIEGRVQYFESISNYYNYLPTMAHDTVHKRTDNKDCCAGLIEKLVISTTATQQSLIRYFYEQYIN', 'MTLSSTSSSNLVFLSVLTLSQVLFFGISIFFELLCIILYAIYFPKLSIVEYYCSKAAPEGLKTVSADLTVDGIQDEISQQVMYTLVLIAVFNVSDLISRSYSPPQIPEVGIKKVLTRSNSF', 'MNGCRHFALTVRTLDIILQVVDGCTRTKEIQQEKLPITSVHVLEEVTETREEPFEVANAATTQSLFQDVMELEWSAHNQGIPTAETNYVTAESDETNVDTNDVTVEKAVNDMEISHPTDVHPTLVNSNDGQIMHVNSNDDAIEQILEAQIIQSLRCSTKRHTPAKTFSAVWIYGLEFVSMTNELVWRHRMEVSRSANLVGMSVFPPDAYPYSLLSKQKFVV', 'MKMLSFVLVAGKFFLLSDLEIAEALVMQNGLKLTVDISFLNLIAESDASNVVLRLNVHQQSSTYVISIE', 'MLNFRRQFYTAILIYLLGIRLPHVGTDGIGKLKELRELIIQYCLLNSTVSELVKCQKELETVYLEKLDISSWIFQSTRS', 'MAEILKCFYTMNLFIFLIILPAKIRGEHIQCVIDDDCPKSLNKLLIIKCINHVCQYVGNLPDFASQIPKSTKMPYKGE', 'MFRTKYNASSIVIWIRIFGYLMAKGGQDCLEDQDLVLHNYFTILNSEPDRVQGEALVDMKDIDGSFNKLLADIEPLNAHYGEEVSLIHNTRLKDIVSKEIIPIKLVSHDTSDLTKSSDIGMMSFPNSASTSLG', 'MKMIQIILFFLALISYTSNAIDSDDFCVADLSFPDTPSGYPCKSENNVTADDFVFSGLNIAANTSTNSFKVAFTTVKVDKIPGLNGLGISVARIDLGFNGSVPMHTHPGVGEFIIVIQGQMTAGFITPKKVFMKTLNPGDVWVFPTGLLHFILNTGPGNAVAYSAYASSNFNTHLTDFLLFGNNLSTSTVQKTTFVDPAQIMKLKVNFGGSG', 'MPTTGFFHTPPYTQQRNPPTHANNIMRDNLLRDIFMFSPDTILQASSPKDFTFSLMIFRPKTFVNLSLQLQVQEHTLTKDDAKKRSNEVATSTNEESVQNLNTTKGNRISDLQKHPKGRNSRASKPLFQWKNKIFNASEHEVPSGPNPISNR', 'MDKRLKFVYVIVHFLALFLAVMNVEAHFQCHIDSECENQIKCVLPRVAKCVRYKCDCVRFDAEQDPWSART', 'MHYCSHFKVIFCCIVVFIVFSRANLIIHNSRKTKENVTVTVRFWPFKLAYLFIILEWKISPITYLRVPPSRCGDENGETIIDERKIRIKPLPKKCVSRSPDAAVDIPVSDISLVRKNSSNLDSFLDPAFMSLIKGVWNSGRKLLQHKEALIVLNYFDMATLTGLRCLLPKNILFTLDTYQKQTL', 'MASKIISLIWVSLILCVFSLCAMKLAGASELHSSSYEMVRSRRLLGQEQTGGSRSPCGASLINDQNKQASDQPRCGNYRHQSHP', 'MAVTMKTRACRGVFVLRSFTLVLIASALLKINDAKECDEFYVVREGDTFYSIAEKCNDPFILLSNPHVRDPQDI', 'MKIAALLGLLPPLGLFFLFSKIDHAQVEVEDHVWYSTFKRTIKVFLTPDNKLDELKIQLKRYFVHLGENQRSSHHIFNNNIHTSSSLETLTKSTNDDNNNSINDHNNNSLNDNNNYNNSIYNNDNNTINDNDDKFSINNNDNNNIEIDPRKLRTKFQKCWKKNYSLKRLPARLSVQIPVATVTARLPARLQRMHSSDSN', 'MEASMIRKCLVMMLMVMFVVAKVKCITPSQEFELSKNKIPSLCELKCDALCIGHKIIGTFDKCVEDCKKTRCDGAVYLHAFVDLSLESWQQC', 'MRRWFVRCLCATLFVFFVTFRYLFGSDCQISFDPVQIQSTTLTSSTLQIRRHDYSEDINIVIYV', 'MRRIFIFIAVLSVLLSLRTTSFNFYHTSFRSVKLAESEMVPGLPSSKLLYSTSGRLILAP', 'MIMLKMFCTVLGIVISLLVFGDLWGTIALLLPGVTHSLVAQKRFPFSFNSSIPHTTLFILQTPMMLHIFFSF', 'MAMAEKKFELQQQHLWISFTFFLLVRVSSSSCLCFFKLTSRTQKRVSREEGFKRGSVGT', 'MTSLSFLRTYLQIILLSLTFHAEFAAECAGKILNYRPNVFCGVFHLSYGSKILRKQLPTKVLRNLNPGKIRRKSVLRKIPQNPPIIPQENLEFLEV', 'MSAMSGFCCLLLVQVLHLWKPDIALILCTLLKDKIYIASQSLLKNFLVAEKELNAKLVNTFYRSDGD', 'MERDMGKNGKSRIKVCVGLVSFRLVWFIVVRISLFVYVFLGFFGSTCLVVLAALSVKITHFGCCS', 'MITGNKFPVSQSRKMLATGFDFTPFINRHRHSRLHHRHQDHHYRSHTRMAHPNEPKKSEIDPLYGVDKRLVPTGPNPLHH', 'MFGVKRKSTLVGLAFLMFMGIAVYFRLWAIDYNISIDDSELLRRQFDIANREAMDESAEWRLKYDKEVDRMNRCLKELQVFQNSSQIAKDASGINHKFAILQKENAILLERVETLKRQLEEEKLKCNSQ', 'MAGIIKFVHVLIIFLSLFHVVKNDDGSFCFKDSDCPDEMCPSPLKEMCYFLQCKCGVDTIA', 'MISIIFLCLIFYAAVPVLGLGLYEILCNEAKLVTDDCLDALRNNSTITSATNYRDLSKYILEKGLKRATSVHNYLIKVAKQYPNDPAIKQCSTDLYDSTVGAFINALSELDNDPDSAINFARAAGDGPAACEKAIEDEKAKSLAIKILNSEISVLSIVAFIAIEHLTHSGGL', 'MGGIIKIVYAFVIFISLILIVTSNVHSLLPCGTDDDCANDPCIHPEYPHCHMEQCHCV', 'MVCYSFIHVHESPSSSIIACWSWLLTTALIYVHAFKIDPKTLEEQQSCIREQRTEQGLVTPRPRYKMCR', 'MFVYAFIIFLSIHLPTVRSDIPCKTKADCTQHRKYFAECIFGFCRHFKHLEHPF', 'MESTRKFSLTMVFLLAFLIIASSLKPTDIRMESQGRRMAHGVVPPSCKSDGDCKNYKCPPSLPFPYCDHDKCGCGN', 'MMLSSILGNNVTAWWYLGLIAVFHLKLLLGLTKSTMPKHWDNLKERFTKGNHFQFSGLLKEMNSFKQDVRYLPPIDKVYSLVLQQEGQLSCIAYVDAKVLFNSAEQSRFPAQGG', 'MWHASTTWGAIRFGVVGRFLLREQPSFAVVCRQWERASGKSGMGVEQKTKFLNPAARCFFFWEGAQEPWLGFRDQS', 'MFWVKFYMVLSFLSCLEMVRQVLESNLGIEKSKLRFWGEKWVFPERCTVTASPISCSCVFFTRFCFELAFFTNTETSFDGKK', 'YLSHFNNSFLLSSFFTDLFECTSDHDCEDVGYCPDTFVAKCFVSFALSQFLSKGKCLCV', 'MGFSLFVLYAQAALFSSCGFQENKGKRASVTTSCRRRVVTTSSLAFAIYITRSLVDLNDFCFFFLYGVPEIPVTYKGNGNNEIPGRMVSVHDQPPSLSTVSVFNVIIVTSKTPPSKPFW', 'MACGLANLTASKYGGNQLWVSIGEAIMPSSVVKLWVRKKELYIHVNDTCVNHEFCHAYRQVVWKKSVQLGCSQATCTDKKEAGLTICFYDPPAPRRVIGESPF', 'MDALVLFFIWKILTSVTDHSLVDMSFRGWGGAGLGDPSPLRAGLGVQFLPPIWFEDDGRRGWGQGCLNPSRPASLPCLLFRTLNGMLKECYGNTR', 'MEFGVTMFAFMFSIGSFLMEWVWSMISSGLVLEWEFGLFTLVKVASNRVVKHEKACSHNQHDFKPFAFDTFSFLAPEIVDLLHSVQKVMHIMSCLIGP', 'MAKHISQYCLLAILCIALVLTLGPTPSLSNCIYPMLCDSFVICDARCRGFGFLRGVCSDFPGTMSCCCVQ', 'MTVTTSTFFVRVRVTVLGVVAVHFLTSVIKLLLYELTGGSSYDSVQEVATGGASAAIGDDDVLGSGEAHGSLSNISYQVAVERGRGTPGS', 'MAKIILALIFLLLQITSFVVFAEELETLHHKPTTPLHPPTKSPVHKPLAKPPTHAPHHHHHHSPSHAPLPPPHPAKPLTHHHHQHQHHSPAPSPYHVPTPLQRPAKPPTLHHHQHPPAHAPTHMPRVSRSSIAVEGVVYVKSCHHAGFDTLKGATLLFGAVVKFQCHNAKYKFVLKAKTNKEGYIYIGSSKNISSYASGHCNVVLESAPNGLKPSNLHGGLTGAHPKSVKRIVSKGVSLIRYTVDPLAFEPKCNH', 'MEFGTCTSRLHILGYWVVVLCVAGSGNQKLFDSIYPTEFSLMELMRLHNQLENYIMDVRSSEQFSNLDGINDLSKIMVEIRKYIVQPMMYLLIKLSLFLPVATPTTERSFYAMNYVKNLLHNRIRGEFFNGCLVTFIKNDIFDSVENEKIIKHF', 'MALHKLLVFGLLLLVCLSKVSSDHEIEKEEDEEMHLTDKPLIVRDGNRRLMQDIDCGGLCRSRCSVHSRPNLCKRACGTCCVRCKCVPPGTAGNREFCGACYTDMTTHGNKTKCP', 'MFAVLVWFCFVFSSSCCGVRLLQIVKSISIKCRSNQCLFLRYGFRLASIKYIFITNSEIWLFQTTEYSHICRLMYFTILGYEHECYEFVRKFIIFVFSEFVLYRRTLTI', 'MAMTKTTLVVFLSALLICTLFTQDVEAVNIIGNPAMQRDTIPCNRRNPGSCNPVSANTYQRGCEIEDKCVRGGTPEAK', 'MKEASSKLALWIIFIILASNTCFWLAGVQLKRMVAAQAQQTYNIKCQTDADCESRCDAGVCEKKCLELPSICLNGQCACPFGPEHSVTKTLPNSTCAFDDDCLKFCPSACSIRSCLEAQCHCGCHG', 'MLDPLSLGWKWHSGVGLLLTLLGLQFWVLNTFLTPIVEELDKENPIACGRRFSQVTL', 'MNIFLFIFIPSQHVVVAITIRDFSFSANCHKMEEDLVGSYKKLKTLNTKNPKGRIRESSYKIKYPR', 'MDMMVMPWLKNLLAITAFFTTCEVYSNESKNERNMFFLYCNDNPFYRSCIKSHHKDQSNTNVIKTIEIYKHLDILRIQTCVICNFTVVFINKWSYSQSTMNKIGKIRQRNIVDTYYFCSLTCQDADFFLNAKETEEMERLLEESIKAPKTKSQKNLMNKI', 'MIKFLKFFYATIILISIFFVDNVCYSLCLPPFVGICTDYQCICLIR', 'MLVSIFFLVDNVNGLSLLILAFQLHTQIIYLDSVNITNLNTEAVDLWFIFHTSNNDE', 'MLLLFSNLLFQILLLLLYLKFSHNKISPTQVLLKVSEVPGKGKKSFVVGTYTFNQDLSRTRTELAKMIILHEYSIMMVNHVCFRSFIHSLIPLLKIPSHNTMKTNTMKLYESERMKQMNSLSKNKSRVAINTDMWTSSNQNKGYMTVTTHFIDEHQNLFIYVPAPHTVEGIGDYLSKCLFEWNVDRKLSTITLDNCSTNDALIDYMIISLLF', 'MKLFGVIFELSGVLYLYIDSVTVLAKIATRVTLMGSLRKGLKDSGRRGTSGGTGLVFSWNELLFSESKILTYKRYILVEGKLIHEGIDVSFLLVHAPSCSQERFDLWRDVMNLKKSISMPLMMFGDFQEVLEPEEIIGNEVTSQGMKDFQHCIFTWYDYENKHASRVDRVFVDQEWLEHFKVLKLRGLERSVADHRPLLVQSGLVEDIKSTC', 'MAEFQNLKYKQGEKVATKKKRNGISSKCASLVKEQRARLYIVRRCATMLLCWYIQGED', 'MKKYTYVMTLILFLFLIAMNVNALYVCRKVAECPENFCVPPLITKCVNYTCICDDPAYGEPIYDFVSVRTEKQKIKIKR', 'MAKYIFQYFLFGVLCIALVLASGPTPVLSFCFNNNPWYCDNPDFCKSMCHGLLFTKGVCDGHICCCQYG', 'MVRRRAVVLQSCCQSFLLNLGSWSFARRSLGLLWVFSLGFSVVQVSDVSAGDGGGGCSTLTAAGAVPLSSTINSPSRNLRIVRVARIVVVCGFLTDGYGLRQWRVAVLF', 'MSHPQMPSPHHLLPFSFIINICLLLSLFLIPIHVTSDVESFKSCAPFSCGNFTNISYPFWSINNQQNYCARPSWRDEASSWHEISNVFQGILLQPSWRDASPSWHDTPDF', 'MHCIGVTTSSRHQKTCTFISFHLLGGLKKAMFFVLLITFLVCGENQESSVVEPQKKHSQMQHQHQTTAQPFDVYMSQTRRVPNTSDPLHNR', 'MKPTLLATLPFLLFALTTYFPLPFTQGVEQVKDKNGNPILVSKKYFIWPADGSGGGLRLNETEQCPLVVQQAFSEDVKSLPLKFIPTENINDFIFTGYTSLDIVFEKKTKCAESSKWVVVKGGFMEPWIGIGGGVNGKSVIDGLFKIETIRSFRGYKLVFCPTISDPTGQCNNIGRFFDNENGLRLIMSENFKPFEVVFVDVEDTAGFGRSVV', 'MLGTVFVFLTSLSLSLSLSLSLSLSCQEGRKHVWKWAFFFWVLKLC', 'MAHILMFVYALIIFLSPFLIGRKGGPPGGRTYIPCISDDDCIVAQPPYVLLCVNNFCTYFRDDDLPQR', 'MSKTIMFLYAMTLFLFLLHIEKSSGVLIDCKTVKDCPTSYTKIYRCEDNKCRFSFVIGL', 'MLVLVIVPSFWASKLLRAFPTSPSAFWSATFLRFVKGRVVVVDPVSGWLVVSLPPRREWDNSVFSKTVFSVPYMFGCTSANVAHSTGSQNVISDALDYDRPPDGLPHFECGGHG', 'MAHFLMFVYALITCLSLFLVEMGHLSIHCVSVDDCPKVEKPITMKCINNYCKYFVDHKL', 'MHKPRNSFWCIKISLCTITAPLDLMIMTVCTTMNFVVCCVTVCTGNVKSGSDGGTSEPLTGSKVNLVITDHCMPGMRDYELLKKIKVSLKVSIFACGCFVTNYLRNKFWRIEVFYPEACLCVFDCGQVEEARHLVISCNILGSMWPLLGSWFGFEGANYQDISDHFSQFIFSTGGLKSRRSFLHLVWLLSVRILWNE', 'MASSKTAVLMLLVALLLSSAVARSSPKSSPFPSPAISPSADSPVASPPVPLNSAPSSSPTASSGSPAAAPAVTPSSISIPPTQAPSPTIFTPSAAPLAATPAVTPSSISTPPSQAPTPAISPSANSMASISAPVPVKSSPSPSPSAINSPPSPPHASPAAAPAITPSAISTPPSKAPSTFTNAGTALNRFTVAGSAAVVVFTAALMM', 'MAHFTRSCLIFVLLLISCELLSIEGRSLRKSIGSPKAASVETMTRSVVLSPRQLQNNGRNLEGSVEAFRPTTPGHSPGVGHSLKN', 'MIMENATSLSLSLSLSLSLSLSLSLSLSLSLSLSLSLFKMCEMNKRLS', 'MKLQSLVFILIVLLALSIINQAITSSDWGPVDINDPHVVDIAKFAVTAYNKRNTVGKLAFEKVISGESQNVINGTNYRLTLSARQIITVQKYRAIVLEKPLVHFRNLVSFELINA', 'MTKVLNLVYVMIIFIFLFFVVANTETYVLCFYDSDCPQDKCKNPDDVPRCIFPVCHCIKS', 'MASDQDFCECRPLGFFIGLPFALLSLILSLIGAIIWIIGSILSCCCPCCICCTGLLNVAVCLIKLPVRILRWFVNKIPC', 'MSQMVWPLIIFWSIVLGARNNDDKFLNFEVSVRGEVSTCWMVTVDFDIVNSKSPT', 'MYHDLKKLFWWSVLKCDVAQFVYACLTCQKSNVDHQKPAGLLTPLDVPEWKWDSISMDFVTS', 'KWCIIYFLFCYTFSSLWPLVRYWLDFMGVDSNVLSDHFLQFVYSTGGGKATRSFLQLIWLLCIWVSWIEHNNRLYNNLVTPITRLLDKVKYMSLCWMKAIKATFRFGTDRWWSSLLQCPGICWMFCLTNSVYIQ', 'MILLFSDFSLLLVFLPFSPKTGFTALQALPLWYNSNNVRKLRVVKPINHGAKILSLGRPNGNENWFWDPIRESGLHDLVYITLSQLY', 'MINSSSTLQIITYLILFTFMMSPVQSLKKNPGARIRKKGQRVQSKQKL', 'MFLAMIPFLVASSSAAFLFRFRRRPVLYLIRFRRGRRQISSTTYSLFHYGLPLHLFRPPFINHPFRRITITSATLPTTTHISDSTSHLFRSPPRHFIAYSKSLNPFLMQFFSYIINEKTYMFIL', 'MKISSFSIIILLTISSIFFIPPIQCHQPPKLDLIKAICRKTPHYHLCVITLKSNLYAVKYIASNTDISGFVRVTLKVVATRAPVILHQLQTVQVQTKDIQLKAALDNCIASYTKISKELVPQAQKCVDKSDYNGVKQSATTAGNLADTCGKKCNGTTSSAVLSQLGDSNQYVKNMCAITVSIANQFSQSNRQTLT', 'MNQIFKFVYALIIFFSLFLVVTNAGKPFLCTILKKK', 'MNRKCQSSPIWYFFLVFTLWIEIGYHIFGFLLLLSKSQLVGTMHNICKVWVQTSTTNKKKSSFNISFLTPKPLFVGMFGASKTDEVEIAQREVGERERERKTSFSSAILKTWILNLIIIMF', 'MQGEKIMAIISKIIYPLTIFISLLLIETIRRLQCKHDSDCQKDMCRSPEIAKCVYFVCECV', 'MASWGRVTFIFQPFCTFLHFCSLLDVLSSNPAQMGERTYHMRMTFVCENANPTLPSSTPPSPPIPGQKPAVTTDSRSEPVVATFMHMSDVGKSIERNQSKKMLGKKTKIMTEQAASWSRHVSTVVGFRRR', 'MGHSRFLVLLLVSSILFLSFGYGFGRVAMMETIEDKDVSIKGLVEHDRKMREVYEIMDYSLPEPNTNPKSGYTLTPPTPTVTSPPPRA', 'MQIRKIMSGVLKFVYAIILFLFLFLVAREVGGLETIECETDGDCPRSMIKMWNKNYRHKCIDGKCEWIKKLP', 'MTLFILQTHKKYKTEEWLQFIFKSDEIFHKCDLVTRPENPKFFAKCINELDSHCGEEIFNSIVNENNISKKCCGKLVKMGEECHTNMAKALIRTPEMRNIDAIEFLKKNKILFDDCRTME', 'MTVIVKFVCVMIIFLSLCVFSMHIEITSFYYFIIAAVTTCIYDSDCPEDMCYPPKKSFCSTFEILSIERKVGVCECI', 'MVRFSILVVVFLLFVASSYADHDVDVQSICKQANDPSFCLVLLKSKPNGVGGDLKSLAQYTLNVLHTNTSNTLTLINKLIAQSGSDPKKQNHYKDCLALFGVEYKGVLGYVLDSLKQFKNSKFNQVGVDMVYLRFHVDDCISGDPSDTSLLPKYGNDVRNIADTTLIMTNMLVRRS', 'MTLTWYTWLLWLGKAIGPRPDEYPKKISAMGRVKPCFHGYGCGRNDKTIINSHTQVEVPEFEPGHGIRPNNFGILLVELGLLDIFII', 'MEFHSLPNLFLLLSFSTLFLSSQANVTFKYCDKKADYPVKVTGIEILPNPVVSGAPATFKISATSGKALYGGDVVIGVSYVGVPVHSETIDLCKEVSCPVANGNFVISHSQTLPAITPPRYQTASMTDCSSGSFITAMANAKDGGAMAFYGGNDA', 'MAGLPRSTHLLFRTIWFATVWVIWKERNYRIFQNTTSTSVALIEKVKLNSFL', 'MKNMAQILIFFYSLIIIPSILLIVSDRIPCIDDMDCPDMFPSLNTQCIDNFCDVVLG', 'MGAFKIFLFLFLSCYTYTTVTAQNETGFVSSIDPKVFQREQNASHFRFYWQDIVAGDNAASFDIISSLPDYNKTSVFGLVKIIDNPLTLGPQLSSKLVGRAQGIYASISQTVLNFLMIMNFALFEGKKNVANNKVREMPVIGGSGVFRFAKGYAEASTYSSDPNTGDATIEYNVYVSHYI', 'MCNSEGECRPLGFLLGLPFAFLCLLISIVGVLIWIVGLALTCICPCCLCLTIIVELALELVKAPLHVMEWFTSKIPC', 'MNHQTKRTILVPMNVNVLSRQLKLVSMQRHALHSDSRLVIVSKRRVPNGPDPIHNRRARKYRQPPNQA', 'MSMGVSRSFLFGILCIVLVLASGPVLAGPTIGLDSYEVDCNGHGPCERCDERCIEHGYKHGGPTTISKMVSEPLQDTLGQLLAGFRYWAIPYMSTNQA', 'MDSTRKFSSVVAFILAFFILASDLHMESQAWRVDHDPYKSCHSDEDCVKIHFYCPPPKVPYCRVDRCDCFN', 'MGGVGFVFTMVVFVKESVLGGDMNFGGCCPQRFHHKTRPSTRANARVIWKGVKLEGSWEIDVRSSLRETKPRERKPGTREENQELIIKNGILGKNMFSARTEVHRSPREQLCSPWRVSNFIARLASNPTRHGE', 'MATSTNSREFLIFICVLTLLVVRSEARLVPMLFSTKEKVMNSKFGLREVINDVRNSEWLRKRALLGGKPERVSPGGPDAQHH', 'MASNINGLVFVLLAVANLLHGSVAKTFTVGDGLGWTIPSVGSQFYVNWATNKTFTSRDTLVFNYQLNAHAVAWVTKSDFDSCNGTNALAVMTKPPAKVFFNLSTVGQQYFICTFPGHCSAGQKLSIFVDEVYVLPTSSPTAPVPSPVQEPAPTATTSPAPATGMYFTLLLILIFFF', 'MESSIWEYSVILFLRPLFAIAFVLSLISLGWLLAWKLVLVHVPLVQEIFGLKKKPVRSKPQTGRLSKIYSNLNNPPISSSSSS', 'MRSGLYIDFGWNVSMYWCLCLCSCLIGHEELDGVKIVAMLLAILGTTSPLASGQSSISGSCPGSKFD', 'TSIIDFGCIGSPRYYCCKEACTPDCYQACTQKGYKKGGICLDIVTNLCCCYEK', 'MPISILKLMFPLLFIAMILPKSANAEFELWCVADEQTTDSDLQDALNWACGKGGADCSKIQQDQPCYFPNTLKDHASYAFNSYFQKFKNNGGSCYFRGAAMTTEVDPSHDSCHYDFFP', 'MHMDHGGGGISNPLVLSLCWLLTKDWEKDNKEIEPRNTTIINTKLSPRNLVVAM', 'MMNMKVVCALMLMVVLMVEVATIAEAQNCDPNELLPCAGAIIFNSTPSAECCSKLREQTPCFCEYIRDPDYSQYVNSPRAREVASACNVVIPNNC', 'MKFFISYKTMSLLGFFLIMVTLVSQVQSSPCSSTFFSSLVQLIPCRGAVAPFSPIPPNDSCCNALRALGQPCLCVLVNGPPISGVDRNMASQLPDKCATSFDPCVFLVSLYPLSSIFVYFEKWFF', 'MAIFKSLITTLLLVVTISNMSLEARHLLQTTTQPNLPTIPTLPKPTTLPPLPSIPNMPQGNLPPLPTIPSMPKLTMPPLPSLPTNIPTIPSLNIPPLPAVTSLPNLPSIPTTFPSIPFFSPPPSTSSP', 'MTTPSTIILKCSILLTTLLAFEARETIAYSFAYNVTIFIYNKVLAPTPADITVHCKSKDDDLGDHTLMPGESYVFSFKPTHLPFKNTLFFCSFTWPGNPHRHYLDIYDEAHDECKHCSWDINVNGGCLNDGKCVPWKSIAFMESYNTSKWPGEKGLHELAHGHPLT', 'MTMSKLVLIIFFSAILICSTFTQNVEATPIGYPAFQPNLPIPSPPTPVNPPSRGCSPINRRHGPPPEGAEDEDKQQ', 'MKYQRRVKLSHCWSLSLSLSLSLSLSLSLSHTHTHTHTHNWMDIYDK', 'MALKVGTILLVFVCGAILSGGNLKNVDAQKICPQFCYDSVAYMTCPSSGDQHLTPKCNCCLASTGCILYEADGTPICTAD', 'MQVTGMVSIALTLFLMEIMLSLLTHTSTFSIAYDPNALDLKTLSSAALEYSQDLSVVYGGWSYTPAMGCRKYIKGGSKGSIWDRSQ', 'MSLFTQSLLLLCVLTFSKQNVLGVHQVNVHNTLEGNLDMTLHCQSGDDDLGVHLLDPYEHFGWHFNISLFYTTLFYCSVKWNDELHHFDAFIANYRDRHRFVLDWYIKKEQPCVVSIGGKDGCYPWK', 'MHALLLIGFIINLLDSEFISYGHLKNLSFILIHSRLKLLFLTKYMCKIKLDTSFYSNAWWSDEEVEFVCRSCRYGLTKKLSRITRDMAQKIFIVEVEKANEVTEDSCRAVASSL', 'MSHLISVLVLLLAVITLTSVEGNINPKYQPARSNGFISYEPPTTDTQKLVALNHRRRRHLAHFPPRISVPPINIPGRPHPRPLRLPRRPPPKM', 'MAQILMFVYFLIIFLSLFLVESIKIFTEHRCRTDADCPARELPEYLKCQGGMCRLLIKKD', 'MALRIRFFEAIFLVVLIAFPVIDAYTSLQEVLRGHGLPVGLFPESVKSYHLDQSGVLEVKLDSPCIVKYETRVFFETVVRANLSFGQLKGLEGLSQEELFLWLPVKDIIVDDPSSGLILIDIGLAYKQLSLSLFEDPPICRSQGLSMNMAGRKSVGFQDQR', 'MPPLNLVAFLFFLQVGFSTQKDKICIFYWAWAWQYEEKNFYGFAYKPLLSISSALWADIVDVSFLRT', 'MKFIYIMILFLSLFLVQFLTCKGLTVPCENPTTCPEDFCTPPMITRCINFICLCDGPEYAEPEYDGPEPEYDHKGDFLSVKPKIINENMMMRERHMMKEIEV', 'MVQRPGTNPNMLLGIILMISAIKVKTADDLSLITHKKINLQVFRESSFGTG', 'MVVQISQLLFICASCGLFFLETLHASPFRLRDIGPCISAYLIIFIFIPPIN', 'MTNMKVACMAIVMCMMVVSTTQAPVRPICDLVKITLADCLDYLTGGVKDPSSLCCDGVKELSTRANTTAIRQKACNRAKDIAMHTSNFNNLRGSGLGLRCSTLLPFPISSNADCTRVR', 'MDTRLKSFVTFLTFILLVSLFVSLLPNVSEARPFLSPLQGREGVIGEVNGVFRTLKDAGPSPGVGHKLKNLQKLHNFEDHKLNTVQGLGVIKHSGPSPGEGHKYITNNNNS', 'DFFCQLWFVVYDWLGFVLVMSLNISVHLAQFGSFEDFLKHKKDIIRLVWFASPWVLWKERNAKSFKNSQNTIHQLLDNIKAQSNSWLKTKHKNLAFNYDLWRTNLLNLL', 'MDSLGSSLLFLVLSPLAWFHVKTGREGAAVDRWFDNNIRRVVGDGRKTFFWTDNWLGWSTFIYSNQTENIQDQWRWLLDPIHGYTVCDAYRFLTTIDDQVAAGVLKDVWHKLVPFNVSLFAWCIFQDRIPTRSNLVCRHVLQPSDNVCVGGCGS', 'MAQIEFLRHMSLVSLIVLILCSTPPIFATKNCDFPAIFNFGASNSDTGGLAAAFQALPLPNGETFFNRSTGRFSDGRIILDFIGNIIIFLSFQVHFL', 'MSQILTFVYAMILFISIFLVAAEVDWIYHLCDTDTDCPEHWSKFFIYKCVNHVCDSISKVTTDSKEYKNFP', 'MMSMLCGMSFFLRSLWDELVEEVKVLSWRWVLGRFNVPACMYYEWCWCPCECLLR', 'MIFRLLIFRMASFLYAEIHLNAKGHGDTIKERNITSDQQNAKTMKFLRRHLHDDLKNEYLTLKYPQVLCNSLKERCEHQKTIILPKARYEWTHMRLQDFKFVTAYNSIVFRITSQLKLCGEKVTEEDMMAKYILHFSCI', 'MASFGFLVLLVSLMVTAHWYVSACIDCEKSPPRFGARCCDPREKTPVENLPVYKPSIEKPPVYKPSAEKPLVDNPPAYKIPFEPPFYKPSIEKPQV', 'MMKNNLSCGLLYFLFCLLLPPLVFSHTHGNPATEIVDILNKNRTDQKLHNLNDSPGLGCMALQYVELCKGNCTDNNVVNCKPPEDDFVEVFAPNCGIELPTFGTITGHIVGCQRKYLEPSLAFSQILIKDKKSLSLLRNKSHTEVGVGLVGLHKKGPFFWCVLFTDGKANSTFVLENRGAGIKQKKGCYSGSNTPCSRGQKHGVSSSNFFFMCYVFILLFKLL', 'MELNKKTMMKLALLVFLLSFTSTMVDATTTACCDSCPCTKSIPPQCHCTDIGETCHSACKSCLCTKSIPPQCHCADITDFCYPKCN', 'MSNVSFLLLPLYLFMVSTSIFPATSSATGEHHLRWVAPMTTPTCQGSIEECIEEGEFGMNSESHRRILATTHYISYRALQRNTVPCSHKGASYYNCQTGAEANPYSRGCATITRCRNS', 'MTLTASLSLSLSLSLSLSLSLSLSLSLTLFVFSPSHWCFEESTPIMASFPHQTSLITIFTKTHVLDIWVHPKFLSVRKNTC', 'MEFSKVYWVCTLFYLFSSLVLGDHGFKMLSKDVSTLPSLPNKKPNVPPHSPPPPRAHYTNLNFEMLRKGVHVPTSELNPKSITSPPRLPHRRHSNFQMLPKGERVPPSGDSERQNDVPPDHKH', 'MPTTRWILFCLLKQVSLLDQGSQGKQKCRSTGARN', 'MSSSSIQTMIILVILVSTLCSSIEASTRKPNRRRFKPEHRHKAILTLNSFEKGGDGGGPSECDNKFHSDKTLVVALSTVLFNHKKRCLKEITIFGNGKRVNAKVVDECDSSKGCKNNIVDGSAAVWKALGVPKKKRGEMDIFWSDA', 'MAAIAMKISFILLVLAITIPCLEGRIGEFDDYLKAQAEMARQIAFKSYVPNPENITTEINIHVHLAMEAAMKAEAQSNDTGKELMSQKSRGRGE', 'MNIRPVIFLCFLCTLLLIASAATEPSKDGKEAGATVEPKDGWGGGGHGGSGGWGGGGHGGGGHGGGGWGGGGHGGGGHGGGGWGGGGHGGGGHGGGKWGGGGKGGRGASP', 'MYRRMPAQSVCLCLCVCSVVFADACAYEHACVLRIKFLVHFIHFNLTQYNTTTLNNAIQHNNSLGHLSPEQRNTTQTSHRYSFFFLLLFIDFKYGGPK', 'MAKKTIMLSFLVFLILVQNFGLMEVLGKNVEAPPTIPRVLLRSPQAPSIGFYTKNDDKDSQGDAFRPTSPGHSPGVGHDSPPNFP', 'MKRVGVTYKLVVACLLTVAEGEKLREEQETKSAAIFFKQGSEGTQNNFSLGDEWYSPWRVRTLASRVQPECTCLSVCAICVGRVDQERTPSWY', 'MKSTLFTFSLLLFSFTYFPLAFTETVEDINGNPVFPGGKYYIAPLISKGGGGGLKLGKTGDSECPVTVLQDFSEVVRGLPVRFTIIVKRGVIFTTDEVDIEFVKKPKCAESAKWVLAHDDFPTSWVGIGDNIDAFQGKFKIETLGSGSGAYKLVYCPLFSAPPGACSDIGRYRDENGWRLVPTENDPFRVVFIDATESEKAVV', 'MASKSTIKLLTFFYFMLFGFMNLSSTVISLSSNHHHASICNGSIAECNQEDEQLMESEISRRFLEQRRYISEGALKRDKPVCNGGAGGEAYSKSAGCIPPPSNPYNRGCSKYYRCRMLETIKYI', 'MAQLIIFVYALMVFLSIFLVESYKTKTPCKSLNDCPKAIKPIFVKCLGNICQYSIGRI', 'MSQYFSLLLMLFVLCVASSNSISAKVVDVDVICREASNPTYCSNLLNSKQGGAKGVDLVDLAEYTIDVLSENWTNTYNLINKLINNAENSMVANYYYRCSIDFMNKDSVANSLGDALLNLNIGNYPAMAKAITDVKQYLFDCIDSLRKNKTSPLLPKYVDVLQQVISLRIISFSSFGGTVLSNGH', 'LLNHHKYLKQSETPTSPLPFYVEPFKHPLNQSIKTILFVIRMKLLCWQKSIHGSNKHGEKCERVLNKNPTHSPSIFLNSLLCALAFGFSYHKGIMLFSEQKQIIKHFSPKDQKLLLHGKPASHKKAMDSFLESWLCINHSIYVALTLIEGASRACVCASIVIVIFLQNVIINIKMVLSSFEKLPTLCTSIHSICSLESVLVSVSADEGRLLSLIHVPFLLCCSLFPHECML', 'MFTLHFPFIFFFVINNTNSLSKRTLSLCSVPLLFILRASSSFTINRNRKAERITKELLSGLRLVVNESSFKACD', 'MATSLKMVLKMALLLIVIASALGGNTNGIEGPQEIKDRTEYPSSNSNNFNVEKFGCCNIEPLCCPRGLRNL', 'MILCVSLLLIEVAGGKECVTDADCDKLYPDIRKPLMCSIGECYSLYKGKFSLSIISKTSFSLMVYNVVTLVICLRLAYISLLLKFL', 'MEFLKLFIVALMPVLKVLLITVLGTFLALDRFDILSRETARKNLNTMTPNLVACDTIGSCMCLK', 'MKIILSVLFLLMISSAQALNQNDGFVESRNDHKHELSLTIRKGGGFGGGGRGGGGFGGAGKGGGFGGAGKGGGGGRKGGGGGFGGGFGGGVAGGIVGGYIGGSIANGGHNGGYNGGYNGTQNSATTLSAWPHFSVSTLILCLSFWL', 'MWWNLIIPSKLFVLVWQLFHNRLPSKENLSKRERETVKWLGIPVAFVEGGYARLKLFFKGLVSGNRKIRESLGVI', 'MNEIKLVKFIGVAVIYMLLGHCSCAVTATWNKFVTRKGVPQSTNPIKWWVPIEQRNAAEESVLFLLPNKANRDPIHGQPPHHRRSAAATT', 'MLFKGIIGIMKNYHFSHRYHSKPITKPTCAVCVLLVALLSTLTAVIVFMVWSIGIFTVVVQRWDDLVHQMKALLPCRHLQS', 'MKTKPCTFVFFLCEIIIISVVAIKSSIDEKQSGATKEYKTKIEVDMDKYWGRNGGGGYRGGEHYDVDMGGWLGRGTPRRRGNGGPGGEGGGGGNKGGSKGSENKEPGGGGGGGGGSKGSENKEPREGGGGGVVPEGPGGKGRGETKGPGGEGGGIENGDHVEHP', 'FGHLAGLLRFTHSFLQVIWHAGCWVVWKERNNKKFTHTAQDVVNLLDSVKFLSFSWLKAKMLTTIHIISFPYEINYIS', 'MSYGSNYSPTPFPCLTDKDCTRRKGFSVTCRKGFCVEFKHF', 'MSSGDSTMSSEPPSFNYLLWSMTAFLVVVTLIGFLIFLKMGTSMCKSCRPKTPAPPATAAVTPLKDVVVSAPAKP', 'MTYCANKFYNVLMFLCLAILLIFIWEVEAKVCEYPLSRTWFEEGQCFKDLCNTKCKEMIASYGSCQEDACVCAIYCY', 'MSYKVNVAFFLCLNIIFFTMVNCNYVPEIPVPIPDPIYNPSPKGTCPIDALKLGVCANLLNLVKVKLGSPPTLPCCSLIQGLADLEAAACLCTALKAKVLGLHLDVPISLSVILNNCGRNNSGFKCT', 'MASQPTFGMIRRAGLLSLELGLGGFSSSLSKWTGKGDWVWDLILRRPLFVLELGLLTDLLVVASRLSRVEREDEWSWTNSPDGRYSVFAELAAAFGIRPLR', 'MVIFFPLRLHLHQFNFQLHLHLHLLLLFNPNSIVSSKPHNNPFKHNNKTKQKNQTNALSYHQHSKTNPLSSSKQVPKNNNLKHMNHCSNDSDPEHSTRLSDKISYHQTQFFVNQFNANVYLQSFSGVHRVQVKPPLQKPLLIHQARKVRVKTNQTTVLLVDEVHRFTKSQ', 'MGVRRCVVRPLMRRLISRLVAVFVNFILKDLHQSITNSTNDLNVNVANPEACVHSINNKEYWFKIEKADNDYVLSFCPLFLYVVCFARKKQ', 'MTKILNFVYAMIIILSLFLLVTNIRALNCTTASQCVNNRCYLHGKPSCLNGQCACV', 'MLTVFPSFVIIMLLFVCQRIQFYIQEPSILKSNTILSETMFKKGILDIQFIDTEHQWADIFTKPLSVERFDFIKKKFRTCILCLIEKLLASE', 'MDVLVFVSWWFSGMFVVILRWIWWWLVVVVFRPGFGSRSRSVLWLLLRKRGGGCWWWIEKGGLLVGDAGEGEFYG', 'MAIAQNKRFLVVVLFLICFISIQARARTLKEKSNDVTSNTHNNKEDHMFKAKEEHNVGDEVFSMDYTPARRKPPIHN', 'MPHKTRPMTAILLFTTLNAVLCATITPVYDFVCFLPYWERRRERRRQEREASNHGRI', 'MAQILKFIYAFIILLSLFFVVTNSSSFTRCRNDVECPQYSCLRGLKMKCICFKCMCV', 'MNKTRFGFFFILLILLASQMMVQTEGRHCESKSHRFKGMCMSDHNCASVCHVEGFPGGNCRGFRRRCFCKKRC', 'MAGNVFLTLIFSFLMTQVELFFGQELGNISSYAPSGVRSEHNTSSLSVKGVARNGSPPSELMTNYGHYTFSLGENLTSRCFMREASCKKLKHEGGDVFMDEYGVFGMGLVVMEGVWKCFGVDRAVE', 'MRTRNNLCVFFLLFSILALHVALSSSSTLDFTDNQLSSFFLPSDSGCRGSIAECSLLAGDEDDSEFMMDSESNRRILAARRYISYGALRRNTVPCSRRGASYYNCRPGAQANPYRRGCSAITRCRR', 'MFSLKWKLFIHIHLLAHVIAPALSLLNSICCLTFIVRKTLDTRYRSNPCQITTLIILLGHEGSLEDKDIEPKATIQIRLLDGTATTRCQTSNGRKTLTNGVCCSDPVRILSCVHEVHGFRSEVVTGFKIFVMLEGFMAGKLLETIKFWKPFPQQNGFGVQLTETISTAKWFWHSGTVMMMNDDVDDEQR', 'MLITNQIIMLCLLFWVLEEEEAPPELDIDEIENLLYRGDSAIPIVGDSLRNHQVDEDSGVDGDDGIEDIVIPRVIIDYGATSLDDYND', 'MTAMKRLSGVKCLSWATLAVLVGEEEGVEEEAAVEGLELQKEKPYISRLNLNLCTSIQQTSKDIHNKRMSIDNGSEPKELSKENIAAHEESTQSLRNIDLNANLRVNEEKNNTSMDIPTHAPLPEPAATTDMQHEEIPGYSFLMSTR', 'MLLPFHLFTVHFLLICQCLFLICIVQEKYLLSRKFQSDPLAATVSSLLQVLVAIHRSLGREIRVVVDELVSICSYAPSCCKIFQIYSNYPFYFV', 'MAFTTNIVHVLTLLLVIITTSMFTVTMANKDWPSFGNFNYTDWWSRFGNHHHQINKTEQQPKNIIVGGSQNWHFGYNYSDWAIKNGPFYLNDTLVFKYDAPNATSFPHSVYMFPTWQSFMKCDVKKAKMVANHTQGVGEGFKFVLNKWKPYYFSCGEKNGLHCNVGQMKFAVMPMLRPFWSSWP', 'MAILLCLLLSLSIFLMKKDEDIEIMYSRFQTPVSGLQVLKKSYAAPEHVKRILRSLQMT', 'MRNINLVPEFVRRPSKRSLRPSALLALLICHLACCVVLYTWQEGSHVVLEVRLKKVSHKNNVVSGNVETAGNVVANARQVSSPRRYSLRGPVLSVDKPKQTPMRVLHQLIQLLLA', 'MEGISLSLSLSLSLSLSLSLYLYIYIYIPPVI', 'MICGLAAWFGQLCAVTGLSSTFVRSFEFSLCYKGSKPIIVWHAESELKSGSPDCFAIADTTYTNGERLQI', 'MFNLLKMLVLKCLILNVVICKPLWKVKRLREHNMLPHRIDSYRMSVTEGNAPLRGYFELDHDPLSIETYQLAA', 'MVAEQLVSLSLSLSLSLSLSLSLSLKITIVQRNLFVECSKYAFWFAN', 'MASKYSLVLLILGMLVLTTVVSDKRNAIGYVPILELEEDWPDHFFPPPTVMVGRKPISYGYPSHNR', 'MWLQYSAMLCLVLVRSMCGYMCMILLLAHVFSFGCLCLKYYFSSSPGRSEFVFINPNSKGQCGCGESFMTKPSTGASKQGNEFSEADGLRQQYATRITVSMS', 'MAEAQPHNRGRRCCCCLFGIIWKLLLAIIVLAGLIILIFWLVVQPRTFKFSVKEAKLTKFNYSDDTNTLHYNLVLNFTARNPNKKLNIYYDVIEGHASYEGTRFASTKVITWLNSFRQYTKSTNPMSGVFSGQRVVVFDRDQVSNFERDEKDGVFHINVKLNFDMRFRLGDYIGPHTKGNIKCRLDVPFVANGTKVMKAFEPTTCDVNF', 'MTSPMLKILIAILLASISFQASGKYMEWCIADGQIPDDVLQRAMDYACHVDGVDCSKIQVNQPCYLPNTVKDHASYAFNDYYQKYKHKGGSCYFNYAAITSASDPSHGSCKFEYNP', 'MSSKHFLTLFFFLLLSCNTLTSTSSSENEETFDFVRPIDRKLLGLNKKEKLSHFRFYWHDILSGNNPTTITIVQPPLKLHSTGLNAFGLVRMIDNPLTLGPELSSKLVGRAQGFYASASQEEIGFLMTVNFAFIEGKYNGSTITILGRNPALNKVREMPVVGGSGLFRFARGYAQATTHWVDLKSGDACIEYNLHVFHY', 'MKRNAIVMMLMIMLIVTHVECNSPSQDQEHGPTELKGVKRVFCEIKCTMSCRSLRRIAHKYKECFIDCVNKKCIGAGKNKKKKTLL', 'MAEIVKLIYVMIIFFYVFLVSMNVDASDECVKVSDCSPTKYCLPGRRMICSKEKFGGDQDVPNNVPTY', 'MPKFKFLHVNGLLGTLSVSLGQNDPEQLGAYLNGGSLSSYKVSTREREAIFMFGGWRFSK', 'MSLTFAWTALLGFQSIETLFFCLRRLFSFDGQDAPVGEKSQWKLLHHPLIQPRIQGTQGREGVYD', 'MECCIINRLRTQPLWFVLVFLVGLFTILRFAFIYFRCFAAKYLKAPNKVKK', 'MMLYVFAQFCVVLFNVVVTMVGRQVVNIPSFMVRVDSQKHIDFSLTSPFGGGLPGRVK', 'MKLHFPLLFLLLTLFTTKPLQGAEQPEEVRDTSGNLVRNSINYFILPSSIQCGTRCEMALLNTNKTCPLDVVEEEEAMQFSFVPFNFKKGVIRVSTDLNVIHSFPTNCSTSSVTVWKVDKVDVATSQRFVTTGGVQGNPGRETVDNWFKIERFESGYKLVFCPTVCRECEVVCKDIGIFLDENRNTRFVLSDFPFGVKFQRACCE', 'MAKTLKNIYSIIIFLSIFLVVMNVDGELINCITDDDCPKSEFRKWVCINNICRKMCRTKV', 'MNVSVKPELSGLWLVLVAEWLLIDIVGGVKLRWLLCPSCRLSDGIKARERVVLLGRETTLWYIADLEYFPIPLINSTNSCGSLWVPLLNAATASSVFLRVA', 'MENTKRLQIICVLILFLVLQQEVVIVQGRHLRSKLCRDCTKPHKRSIAHHGGKSSRRVGYEVDDFRPTSPGHSPGVGHSIHN', 'MTKILKFFYAMIILLSLFLAAIDADVVNCTSVLQCFTTYCYLHGTMLCLNGQCLCV', 'MIIWFLDFVIGSILVVLGLYSVLWGKHKELLENKVAPDDIPLPMKGAQVNGNIGTFCESIDQLCEAKSDQKVESNISI', 'MALTSWFPIFSAVSTAVSPDLLTALTSVSFCPRSIFTICTNKFTYFSLSETDVNNFPFPLSMTDFKIKLQKVAYGYVISCFHKLFQTVSNDLLLSALPLPQDFVPLLPCEVLSGPSYQSLLQLLALSQEVLSQPPNVHFLQQNEEPFSPMVAKLLPASNRNFTISICPCVAAVCKAVVFDGSTVIDNSG', 'MASKESITVPAACRRTQTKLYDHDLDCYIRVFNIDATVGDVASIAQKSLRLQHIVFTFTLKSILIKLFHDTSCKLLTLGILDVVLMIYIHLHCGWFASARINNPRLFKRLLWLVW', 'MMLRPLLLVFLLLMFVITSQFEWKQQLMVDVDSTSTISQKQQRFSKGVEIVKEKIILVQEKNIRRLNEVVRHLQQQLQQCRSSNDTRNGTNRAVKEYEQLPVYLSMSCFED', 'MSFTANTKMLLAYHNYAGVVWVFLLVVVFFCSIFGYFAAFSGYHNHNTLFAKITGAIHGIEITFEKGGLSLWLKCDSRNSLSTHAYQKGYVL', 'MVTATRLLFLASVWCGSPHAGCSVCASSCVSCGVYCLLRGSCHDSYGVIDKCAQNGRNLVCRKQLKVAETDQKTSPEMVPKAGLQNDWSLEAQLW', 'MYLLFTQVACALLMAGENLDNHVDVKNILVEMGTYFQVQVLFVIQLTIHTLEFSLSLSLSLSLSLSLSLSLSLCQSLSAGNIL', 'MALFKLLVTSLLVTFVTLHLVHALDHPEPQVHAETQVSLQQQIDCNGACAARCRLSSRPHLCHRACGTCCSRCNCVPPGTSGNQEMCPCYANQTTHGGRRKCP', 'MAQNDKILCFRICLSLIMKFYSLPFVVSFSHTLREGKKCADWLAKFGAANTESLKMWTSPPQLDIILLEDTSRLGMVKFAVTHIEKIKASPTFVEAIKLDPSFKSLGRNYH', 'MIGILHAAAELLLFPCSTCKCNIGASLSINSNRILIGICIWDHHGQFMLPKTECFTAICDVDIGEALGLLTALQRVHELHLCLSLVKSVDNINSFIVLFIETLR', 'MMMRERTLIPTFVFWAFLTLITPTLILLSENSKADLELNGNITETMKVRRMIGHTQSNIIRTAPLPAKSVVEDELAPAPAATVSLLPLPTSSHSHHNQTDLILSNRNSTDYDASKVQVQVKQMSNSQLLELSETTPKSNGKNM', 'MCFFLLIIDEKIVQPKVEAGACSQIIDKCDLINCSSVCKTQDYEGGLVSWKCDEFNLCTCFFNYVPSKHPNQPTRRCEIGLGPCSTATPECNSKCAIKFKGVGTCLDSTILKAKMCICDYNSS', 'MDEILKFVYALIIFVSLFLAANNVDANITNCQSTFDCPRDMCSHIRDVICIFKKCKCAGGRYMPQVP', 'MKCTIMLALLLLLALSSQPLFVSSKTSLNQVLDISGKKLRTDSDYYIIPANGGDISLESSIGESCPLHVVVVKHRQGLGFPLRLAPVKGDIRVSTDLNIMLGNYDDRCPNYSVVWKIDPYSKEATFVTTNGILGHPGSNSIHSWFKIEKYEDAYKLVYCPNVCPSCNHVCKDIGIYKYKNREMRLALTNVPLKIKFQQA', 'MACFIVLVASAVVVFVSIGCAAVISLEAVLCTVHIDNGSVLCVGFTTFGAALKQWHFQELCNGLVCPLPKAQKTVTYSIGNGRIFVKSWPKRLGNGYWATSHDFRSLPKAKNVVVNEDCEVQST', 'MASKLSLILFVLAIWTLDSSQGALTHQAPAPSVDCTNLVLTMADCLSFVTNGSTTTKPEGTCCSGLKSVLKTAPSCLCEAFKSSAQFGVVLNVTKATSLPAACKVSAPSATKCGLSEVTEAPASAPAGGLSPQSSTASPTSSGAASGLNGPVSELSPVPAPSPGNTASGLFPISMGSLLVCLLVATMSLF', 'MPPHHHHGDPEQPKPNPNLLSLFLKPIIMLLLTSLFFLFLGFAAFLLLNLFLLAGALHRLRFRPTPTRRLQSLTDAFLPREINNLPNFRITKGSVSEPDSRCAVCLDGFRNGQWCRNLAACGHVFHRRCIDTWLLKVATCPTCRTPVRSNPQTSQGSVVEQDEEGSSRFWNCSNNNNGFTLL', 'MKKQLAPLVLFSLTLITSLIHSTTAAAATSGNHNITHILVKHPGFSTFNHYLTITHLVDEIPLIQDHTMNIRSYMT', 'MSHFANWHPLYWCKFALSRCKVVHQSLLLMTCLVLPLLSLLLGMREKTNLTRASFPLHLLLP', 'MFLYALITFLFLFLVETSTTNTKTTIPCKFDNDCPEISYPLILMCIDDFCEYLLA', 'MGKVALFVLMACMMIASSHAKATLTCEQTTIYLTPCIPYGMLGGSVSPLCCQGIHSINAAYKNGDDRRVACHCIQDTAAKIPLIDYTRINQIGELCGSKCPFKVYPSTDCNKVK', 'MAKTLKIFYAMLLFLFRLLIVKEVSGKSKLYFHCFKFPFSSKFYRHICHNNRCKIVMFMPPNV', 'MTHISKFVFALIIFLSIYVGVNDCKRIPCKDNNDCNNNWQLLACRFEREVPRCINSICKCMPM', 'MAQILFYVYALIILFSPFLAALVIIDHHKPCVSDTDCAFYLDIPPTVKYCSDGLCAWYFPDNPLP', 'MEVSMIRKGVVMMLIVTHVECNSPSKNLGQLSSKDSQEHGARAVVHTECDSSLEICNNNK', 'MKKLQEIQTVITILKMSFAHVMMLYFVFLATVATTAALAPAITPSVYTSDAAVWIT', 'MELLMKTLKIGVRNIIDYTLRRKKEKRYRYKVFKKRYAMITHHNQIGNSGSKLGFNDFADLSCVELAHLGEGHPSPMVLGYTWQLIKGEQCHIIQGPFTQCHITNRSKLSLSILLDQRKQIHQEHKNQNRKFIRNSQRFVYESSSSNRFHRNTRCEFLTGGEELKHRSRWEKKKGKIESVQKQQEREATAKNAKQQNVKQPQKREATEREATAKTRSNKNVKNAKNTKQQRQAKSHRNREVTAVVLPLFLSVFSSFFHQGSSLDLLRFKALLKNLSSDSSSI', 'MEVYGWHVLWCMQGTLGVVHALQGTLGVLHERQMTNVFMHVRQLWLASLINRTMSKHSKTPQEKSKREREPCSREKSSS', 'MMSAVSKIVFLISMLVIILVALQFGKTNSYLPTVYEHIINNMSSTVLGVHCKDKHHDAGFRRINFQEIYTFNLRPHPIVSVTLWFCRFEWNNDFQHFDIYVQKRDAKICEKECTWFINSSGPCRIRGTSLDCLPWNPKVAIVYEHRQLGPENNTLTM', 'MLFTNSVVMWWWWFGWRSLCGGGDAVVVVVEGLPFSPTVFVFVTLTFRGGCVVASCAARRLVEAVEVSNGDFVVVNGSFGGGAAVGFCGCACGG', 'MSITKYINIITAVYALVSFLVSVSAAPTSKDTAIKSPLFLILSYPMVSVWVARYEERTNTRTPNTQILSRQQR', 'MMRLMSLFCFLWFLLLVLFLFNTSFSAQEYAKSQNFRTKSMSKFSPNFQAKGGSRQNSGEEGNEDVLGDEKRKIYTGPNPLHNR', 'MKIKSIISFSFICALVFISGVATESSKDEKQFGEIEEFKSKVSWKLGAWTWPKYVVKGKGGKGGSKGGSGTGGNGGEGGAQGGGRKIRGGSDKESGLDARGGGGGENNKIGWGYLKNWEGYSGDISSKSEGNQGPS', 'MRINMTAAILKFVYTIIIYLFLLRVVAKDLHRRQLNILPILCMCCY', 'MDCKTHCRNYPGGGAIGSSCSYYNQCTCIFDFPKSQGVQTCSVGLGLCKGICDSNCCNSRCATRYKSYKATGKCFHYLNKKYCICFYET', 'MVVAVSILLIILRQRLTVPSCKTFVDGIKVKILSSAKIKISKDLNFRIPSQTPSSETTWVSN', 'MSATTKAWIVVASSIGAVEALKDQLGICRWNYAFRSMQQHVNTRSPYTQAKNLSNSANASNKGKRNKEESMKKVMDLNCWGPPTIRF', 'MKVIFFVLFILIISNAQSIISNEEEGRSIIQINEEAHEFNGNLYGNGNIEVSTRLNSKNVIVASKNEHEHKHELSDAISFSTGGGGRGGGASEGGGMGGGGGVAGGAAGGIIGGGIVGGTVANGGHNGGHNGTHNSATTLSAVPHFCVSTLILCMSFWL', 'MAEILKFVYTMILFVSIFIIVVNVGGKCVSDAECSGQYMCPTLTVIKCIKDECVCIHYDHDKQ', 'MNTSMIASFFVLLLAFPYAFATDFTVGDANGWNLGVDYTKWASGKTFKVGDNLVFKYGSSHQVDEVDESDYKSCTSSNAIKNYAGGNSKVPLTKAGKIYFICPTLGHCTSTGGMKLEVNVVAASTTPTPSGTPPPTKSPSTTPSTTPSTTPSTTPSAPSETNSTTPSPPKDNGAVGVSNGVSLLIGSFFVSAMILGLMG', 'MVNVLSRLLLFLISLHCFVACLATNTKNITTDQSSLLAFKSLITSDPYDMLSNNWSTSSPVCTWVGVTCDERHNRVHSLNLHNMSLRGIVSPNLGNLSFLVILNLSSNSFNGQFSKEIYWLHRLKVISIAFNEFVGGIPEALGDLSQLQIVHLGANNFRGFLPQSIGNLRQLKSFDIS', 'MGGNSGGSDMLVKLVCSFLHITTSLLCLCHRWNSPCDCLPPPRRLASLSIAVIICDKLKAKTENLGSALTRLKACISLQVLNLGRRSSSSDHDPWNLEHEASNSYGAAHGTYHKAAQY', 'MGSCKFLILLAVVYILLLRMTVNAKGSFFHRYRDKICREVECGKGKCGATSTHKNPFSVFCECEPGWQQIKVDPDYSNKFPPLPCVIPECTINDDCKQALPLVPEKDFQIPHNMSHSDPCYLAFCGEGTCIKNSKKQKHNYKYRCECKPNYFNLLNMSGLPCYNKCSLGSDCSKLGIKIVNSIADNSVLSIASSTRAKKVPLDI', 'MIETLKFVYAMILFLSLFLITSEVGGLYIGCETDRDYPPLANKTFYLKCIDKKCEWTVTDSLSTRSGRMQKLSI', 'MEGLFKIRIARVLFVVWSFAKDGIEKGEDTWRVADGYVIWYTRVSHPQILPPIPGDLPRPANEEQIIADQWERYEARSSPDTYDMVSGAVAYADAQLGQEEVMSMTP', 'MLRRILAFSTCVTLQSFHGLVLGASDFPDCISGFESMAALSKNLSKGNFILDLSRGIYI', 'MTSFSSYLLVFFLASLILIPQGFASQPHHHPNHPPSTQEVPRHHHPKHPPSTQEFPHQGSKPHPILPTNLPKPHPHHPPKEDNTHF', 'MAHKFVYAIILFIFLFLVAKNVKGYVVCRTVDDCPPDTRDLRYRCLNGKCKSYRLSYG', 'MAQFIKFVSVIILLISLFFVVVNGGFARYCTNDSECESYCFDPIYAMCLINKCICDYT', 'MVVTRMKIKYFIFLWFTVLLSSIVAVEPFQDMKQFVEIEESKTNIEVDCYAYFENIIDFRDFLDGGNMEDTRKGGDA', 'MEPFVIFIIIAVSMFVLVAILSLLCRLMGGRPAYGSGGGGGGGFTHHNHHHHRAHNAVVFGGGAGGIGVGGGAFDGGGGGAGGGGAGGGMGAC', 'MTSTRVALCLSFAFLFYVVGGAKVTFTNRCEYTVWPGTLTGDQKPQLSTTGFELGSGVTISVDLPSPWSGRFWGRTGCFNNNGKFSCATADCASGQLGCNDVSNVDGFNVPMSVTPQGGSGDCKTSSCPGNINVVCPSELQVIGTDGSVVACKSACLALNEDKYCCRGDYNTEEKCPPTDYSMVFKNQCPGAYSYAYDDKSSTFTCFARPDYAITFCPSA', 'MYSKFVPLSVPPLINHFSVSLPLLFCFRIFIFIFPLFLFRLEVILALPLQIGESLSSDFSSSKVNFGSFFSDSQIFHSRVSDMVIWGNLDISIINFLLWYAQFLLGLCRLSLDVVLVIDDTIELDSLLWMQEDDHLMLWNLI', 'MNQNTRLLCFLCGLWILVMVVTKVGTVDADLSFFVFRRCRRRRFVSSVERGIWWPMDKPAAVREKMISVVYDGWGERDEHDGGRWSAVGEVDKGKESGWMWRKREC', 'MMKMNALALAIFLCIIVSCHCTNINQKTNNATLIQQTCKQTPNYALCIKYLKSDPRTSDADLVNYALYMVDRIKTTTITAYNKVTQLLKGGHELYQTEALSSCAGRYRSILVDVVPKSIAALKQGDPKVAEDGANDAANDATTCENGFKGKSPISDENIDMHDVAVITAAIVKQLL', 'MVLIASVVLYFLIFYFDGQAHVVAGQTHVVHNLDKTIINTILVSIYTAQYAQEESTCECIICLDEFEDNDRLGTLSICYHTFHLDCIEVWLRKNPNCPLCRSNCSLNLSMIIDVTQRSLSTPQINYKFIGQYFGFTRHLFLLSTSPTINNFMLEHWWENKE', 'MAKTLKMVYVLVLFLFIFLSIIACNSSFITFRDSRCKTDKDCPNIPMATGRCRKGHCVLV', 'MGIATNSLHGLVAFIFYVLIGFLQICYPDNPTVFQLHPKTILSRPLGPAVLDGIKFDIEGESNQHWGDLILPGS', 'MIFVLCIFDYALFSGVLVCICSSAPVMPILYIYCAAGKKKYQFATTSAGGVASLNAQLF', 'MSSSWNNIFLLLQNLSVYGENIMDNKVVEKSLCNMLMKVDHVVTTIMELHDIDTLQGSIKSHINRILQKTEKAKEEALKSQVNFNNINESNQMGEDMKTSIKRENNFNPKGGNNFGSINYIWQRKKLLQPREEKLCFHREKFERKADD', 'MAYNPVLLLLFFFFMLFTCSCFDVSQTRTTTGKPTPEATTEKHPRIIGLGSLLYPKGENTSWPSQKVLF', 'MLLSLTTLPSLVSIYFFFCAINFGFIEIDPMISDPTKLAEIITVASKQNRNSCSLFIEPPEEDDCRFVGAVTVCEGGRK', 'MSSFSLICVLGLFIIIGHVAHAQNSQADYVNSHNEARRQVGVANVVWDNNLATVAQNYANSRRGDCRLTHSGGRYGENLAGSTGDLSGTDAVRLWVNEKNDYNYNSNTCASGKVCGHYTQVVWRNTKRIGCAKVRCNNGGTFIICNYDPPGNYVGQKPY', 'MTFVFMFDVFLRALWGEKWFKPVKNSVSEPKQLESAKGRECQINYEISRGKLQFEKINYKIVEFRTDIKGSC', 'MATATWLFVSSMPCLTRTCLFSLNLMLIGYVKNRRLIDAHNLFDLIGCVDEAKLVFDNMLYKDSISWNGLLAVYVQNGRLEEARRLFESKVCKRKMLGDAIRLFDHVLVRDVISWNTIIFGYGPDGDLFLAGRLFEESPVQGVFTWTTMVFAYVHSGMLDEARGVFDEMFLETMINGYGQNEDIAQARKLFDMMAQRDCASWEAIIAG', 'MTEIRKFFYMLIHVFFLFIVRKYGSECISDTDCNVLYPMYINRRLRCIQGICHTTTARRR', 'MLLSTHLSSIFSKVFFLGDLLLFRFSAFGHRQITTIHFFWDIFQADFLVVALVVVQDVGPLHIQGIFLADRDVMSD', 'MKNMLALSITIVLVYTISSQLGIAFSRASIALGETEYLCDAFAADCIRLSDSAESRALYYSPDDSAESTAPYYSPDDSAKSPAPGISPSATYLESSDWTKHTVTHNLQGCNKSVIKKKQDMEKTFRSV', 'MKSTFLAFLLLIALTSQPLLSSSLEHVVDITGKNLRANAYYNVLLSMPYTNSRSPEGLGLSNNIGQPCPLDVIVVSRYQSLPIRFTPLNLKKGVIRVSSDLNIMFRSNSSCPYHTTVWKLDRFDASKGKSFVTTDGFIGNPGPQSISNWFKIEKYVEGYKLVYCPIVCPSCKHECKNVGLFEDENGNKRLALSDVPYQVKFVKV', 'MLALNYVVILFALTLSYGLCDAFSVTINIHVDNLSGGIMLTCLSRGGIIGHLDPQKLYHFTYPADQSESCNVNWHGLQAQFIAYDPKNDQGHPIIYWNVEKDGLYQSYENKNFVKKVGWS', 'MAGIPCYFYGSIIFLSLFLLAAFFEKGYMIPCATSDDCLKNMCRPPLTPRCIEHNCKCK', 'MYKFLYLLSLHAWLSSIGHSFSRENPNEIFPSHFPEKNPFYPSNLPEPSTFTPLMPPMRLSTIKFSIHITTDTTATIVQAATITTALRPKKDTGI', 'MASIFKAASHPLLLCLILFSASQILVINCTEFEVGGRIGWVVPDSKDKDDMYNQWASQNRFKIDDTVHFKYEKDSVMVVNEEEYEQCKSTRPLFFGNNGNTVFKFERPGMFYFISGVSGHCTRGQKMIIKVLDVEPITAASPQSANESAPIAQHSKAAQITPITITSFTLFTLSILGICLKY', 'MLVTWVCLIFREVIGCSLGGVLCLCERRSCYGSCWRIWKDILGLKKMMFSGGGWKIMVVEAGVCQVCRCLFYGVLPVWYMATSIPCL', 'MSIRPLTIFIIAHVWLFMITTSVAQIVIDTSGEPVEDDEEYFIRPAITGNGGGSILVTRNGPCPLHVGLGNSEGTLGMAVKFTPFAPRHDDDDDDVRLNRDLRVTFQGFTGCGQSTDWRLGEKDATSGRRLIVTGRDNGAGSHGNFFRIVQTQTGGIYNIQWCPTEACPSCKVQCGTVGVIRENGKILLALDGGALPVVFQKE', 'MKVELVHIMGNSHLMHLATKKQFFVAVFWVMLLLDIHLGWIWTLNITGAQLFYQNQRKTLISHFT', 'MLSSFEFIFILHLMFKIMGITNILCQALQSRSQDILNAMHLVSTSKVLLQQLRNDGWSDLLENVKLFCLKHEIEIPDMSAQYVLGRGRSRQTNVTMVHHYRIDIFLVVIDSQMKELNLRFNEQTVELLRLSTALDPSNNYKNSTYIDNICRLACSFYCLNFIVYREFIVFTNMLH', 'MAQNKYLFCAFIIFLSLFFVLTKSSIPCKTRTQCPEKMCRLPKFVWCIDGSCVCA', 'MKTKYFVFVFSCALIFFSVVAIAPSKHVGATEKFKISVEWKPRPGPSAESWVRNKGGHGSGSRGNDGGKGERFKGGKHKETEHDGGGGGGGKMKK', 'MKAPSLSLSLSLSLSLSLSLSLSLSLSLHFSHIPFNKS', 'MFWSCVVKWIGVVIVLPMKVSTLLEIFSFLVRKEKGEKGALLDCCMTLWLIWKAQNENFFYR', 'MATGIDRVLFFIAILCVASILTPGGATNEDKLIPGPTPKLAHCFRGVSCPVGCIQKCAGLGLGQPGHCQGNQCCCAAI', 'MFWTVSMWNYIGMTAPDYYRLNSFSQQLSTLGPTLTTLALNRPFPSFHTLPSYVKASSLSSLSTLPITRCKTNNFSSTPLYLGSIKHFKVTSSPIRLVEANLEGAQKNMKFSILLLVFFTVLAATRSCFVNSRILQSPTNIEKITHEPIKELQHLKASMNNRILVDNQYHTMTSGPSRRGAGDKVMLEKYKFAHCITIFKLGIY', 'MIWKPKIRPIYLLLMRMIMRLMLKTLMKIFTRLMIKFILPHEDDSKTDAMPNGTPLLVVPHVEEEQNGFSLYLNKSHLVENQLLLIRWIRLYLPIKIIKVRSTIMIWKPKIRPIYLLLMRMIMRLMLKTLMKIFTRLMIKFILPHEDDSKTDVMPNGTPLLVVPHVEEEQNARLLEE', 'MTQISISFYALIIFFSLFLVVTNGRSACNRNMYESTFVSSQFVGGY', 'MERKTLASLCFFLIFLLAARNETPNQHFRGACIGSSRKQECDYGCRRGVGWRGGYCKKQKCVCDCTCIGACINYDHLLHK', 'MFPHQAEALILLVSPLLASPQGTDTMSLYRRCWFLRNGPAASVVPFVEPGL', 'MAKILKFVYATILIIFLFVIATEVNAFRNPCKSDSDCTKLKLKCDRHTILTCFWRHCYCIF', 'MKLGSILLLLWSFFVLFSLTKESDVGYPNIKYCARPLNGLSGTCLTGKIDCYEAFRRKFPGSDPRRCRCDTNGNTHNCTCCIVCGLKYEKILVNQDDYQC', 'MAVVPSKRLLIAFLLICFILTTTQARNLRDTNDGDEKGQSSLFKLNHEGTQTQEKTQTQEKNDDLLDTMDYTPASKNPPIHN', 'MSSVWQLFFGLLRLFTLEVWCHPVPSQVWQRPQQVRLKYNVDASFSQAGNRTCIGMCVRDDDGAFVLAKTMSFLLYVEFNRQQANEVTHALVGEVALSASLVIYFHIPDCINDIIIKEML', 'MCDIYIMTHLFLFFCRFLIISSSSTLFLSCYSKNLHMEPQRVVVIVTVITFFLLSSLGLLAGLGFGGGGGARTGTLMSLSYLLGFEEEGIDNNNNNYTLIHTPHRKLLPHSATERLEPNRIWGDKKCSKSDIVINQGSTAPLPSGIPTYTVEIMNMCVSGCDISAIHLRCGWFSSARLINPKLFKRLRYNDCLVNDGRPLVNGGTVSFQYANTYLYPLSVSSVVCV', 'MAKVYMFVYALIIFVSPFLLATFRTRLPCEKDDDCPEAFLPPVMKCVNRFCQYEILE', 'MDDAKVLRKRKGFGFSNKCTSLVKEQRARLYILRRCATMLLYRDDNEPNQLDNSSELDS', 'MLGLSCFNLAWKSLCGVLFRLGNILVNGKHLNLNHIPEEVEVKGLDFYFKFQNGELEIEQLHITKTKKDKWCNIIAWEHHKMFRDFSRFTPSYDVVDESTSTTVSGGTFTWAALIFNGLICCADDEQPRGDGVFPHNSIWG', 'MTSSASKFYTIFIFVCLAFLFISTSEVEAKLCQKRSTTWSGPCLNTGNCKRQCINVEHATFGACHRQGFGFACFCYKKCAPKSILLTLFFKTIIKMPKLKYNNINVTINHYDRCISPPDICGTNNQTIYINNCSFSNPYFCVFNFSYFFFFLYVEVEPKLCERRSKTWSGPCLISGNCKRQCINVEHATSGACHRQGIGFACFCKKKC', 'MSPSIHHVMLPFKAEALLLTCIVVADFSAVERHNESLAACTVVSLSAQQTGCLGSTLRRRRF', 'MYRWWSAAAYSSIFLCRKCCQGTSLQCVPLLWLELDREANACYSYQEANKCVDVLASLGCEEDSELIFYE', 'MTTIVNFVCGMIIFLSEFMVATNFKRKQIPFYFFIREFYPCFIDGNCPRNMCKVRWKLRLQNSLSPKQKFDDFDKNGIRQGCIFDNTRFKFSEEARLNVHTRGKDAKDVERI', 'MFVYAFIIFLSIPLPPARSDFPCKTKDDCAQQIDYIAECIIGFCRYFTPFEHPF', 'MKNHSLEFFLLLTLFLHANTGDSNECVYTIYVKTGSIIKGGTDSKISIRFDDAKNQSVWIPNLKSWGAMGEGHNYFERGNVDAFTGRGPCINSRVCRLNLASDGSGYHHGWYCDYVEVTSTGPHKPCSQTIFYVDQWLAKDVAPYNLSVIIDRCRLTAPHSSSSA', 'MAEILKFVYNATLFFSLYLVVYNSKLWCDTDADCQEKFPGPSKYPIKCMKGICKCVIN', 'MSKQKMNTAVFLNLPFCAVVAGALLLVRFYGRDFWKRNEGGCWLWEMEPRGRRPQSWEQDEKSLESEAIEIRCGRT', 'MAYNHLTTFDFVLVLHLMRNTMGITDILCQALQQQHPNVVNVKHIVRSTKALLQNMRQDGWDKLLKTVTSFCDKNGIHVPKVDAPYVPISRRARRQQEDQITLMQYYREQVFFITIDKQIQELNSRFSDQSMELLTLGSALVPKDTHQAFNIDNICTLVEKYYPMDFMSRRRLLRDFNFNISLLMLVKIQL', 'MSLSSFVLVLVRIITITNDSDFTKTESSNDLDETKKYKHLWVECENCYGLNYKNIEIKNEYHLKMSGSN', 'MSRNNWIFFFILRYIILGNVIAYPKLFPSEVLTGKSGNMYYTKGSALEFQKTGNKLRDLP', 'MVTMLVIIFTIHFVCDRIYVAAVFTSPTYASVSEVILEMKCKSDLIHIIYSLSNHAVNCVRKMSSFGLVKLNICLQSSRTLLKRHEKFTEDLKRLKLVDYM', 'MSSIFKLLSLTVFLALVFQAYGMCTLSDISIKHYETHDFAHGMPVWRVSITNNCGCPQSQVKLNCTGFQSYIGIDQALLAVSDTECLVKQGAPIPAAQSVFFRYAWLPKFKFEPISSKIGCT', 'MAMAEKNLNYNNNISGSLSPSSFLFVFLLLLVCVSLSSHLEPRKGFQEKRVSREEVGFSEEEIGKN', 'MAVTVFLLACSVYVTASRMTFSKKIFKTPLVSS', 'MANPYSALLKLITTIFVIIVSGCNFDTNVPGNPPATISDFTLGEPDFYDVSLVDGFNLPIAIKAINGSGNCSTVGCDGDLRQNCPSELALKENDRVIACKSACGVFNTEEYCCRGSYENPATCLPSNYSNIFKQVCPAAYSFAHDDAAGLETCSGATLL', 'MPLVLALLSDFSIGLYIFFVWNWSIIYPTLAINDKALLYAFNNGTTNVKITRLSA', 'MSSPWLLLRLCLVRTRSLFKECNGSIWIGFQSKKMSKPMKSLFVRFVLFSKKEPNQTNRFDSVWISSVDQFI', 'MAKMLKFFFAIILLLSLFLVATEVGGAYIECEVDDDCPKPMKNSHPDTYYKCVKHRCQWAWK', 'MGTKVPIILFVFVMLCALSSAIPSKRKSYTPCKNMVLYFHDIIYNGMNAANATSSIVAAPQGANLTILAPQFHFGNIAVFDDPITLDNNLHSKPIGRAQGFYVYDTKNTYTSWLGFTFVLNSTYHEGTITFAGADPIMQKSRDISVTGGTGDFFMHRGIATIMTDAFEGEVYFRLRVEIKFFECW', 'MIIQFSIYYMQRRKLNMVEILKFSHALIIFLFLSALVTNANIFFCSTDEDCTWNLCRQPWVQKCRLHMCSCEKN', 'MRNQKIIFLALISLLLMFLGVNGKATGEKCKIQQPCGMKNCNAQCQGLCDASGGYSAYCDPPNGVCKCVGWRTRVFNSLS', 'MNVSTNVVFTFYKMRMQPVPILSLISFFLFFLSTSLSFSLSLYLLWPLSSFRSAGLIGIRAVLTNS', 'MTKIVVFIYVVILLLTIFHVSAKKKRYIECETHEDCSQVFMPPFVMRCVIHECKIFNGEHLRY', 'MKIYFNPHLLLKIILAIRGVIPTDDIFCVSGCGHVETAEHLFLSCPTFASLWKQVRDWIGFVGVDSNIIADHLMQFTHLTSVGKAKSSFMQLIWLLCSWVEDSSYDDYKLGELEIVSHETSNEFHSEVDSRQRLVKNLEDVNDENGEDDKLLSLLAPMTKMMAVRKYIHDSVKLGD', 'MASSMLIKVTCLSVMCLVLAIPLANAGPYCRDVVETILPCIEYITTPGASTLPAPCCNGMKSLNGEPQYVCRCLKETFFVLPGLNLAALAALPKNCGVNLPYQITPDMNCDKYISHYHPSFFNFI', 'MYCIIKTHLILLVFVVGSSDFRSEMVDNVVDDDVEPKPAKNEASGEGSGDVKPTVNLKSFKVQIPIQARIGKIEEPKPIPISVICEARDTASFFTTNARWREREELLTWVRRQGIKAGFGVCIDKSVLKRPYLMMQCEMSVIYKSPKTRKKPNI', 'MRNVTAGGLAGFCRPQLTITKAVEEHYNLSQSNNDCIKKGTGSLCAYYPNSELQHGWCFTSNVEAEHYFEVLSNPAINNFLKNISYSNGKGFLKMPVEIAT', 'MNMASSSPILLMIIFSMWLLISHSESTDYLIGDSHNSWKVPLPSRRAFARWASAHEFTVGDTILFEYDNETESVHEVNEHDYIMCHTNGEHVEHHDGNTKVVLDKIGVYHFISGTKRHCKMGLKLAVVVQNKHDLVLPPLITMPMPPSPSPSPNSSGNKGGAAGLGFIMWLGVSLVMMMFLI', 'MTGGGIVCILHSLSALCSLGHVVAIEKVSIIQHIFGSMHTMQLSRILNSNLLHFGYVNDLV', 'MAKTLFKVTTILFSFITLTLHYQIAFSTSTFELDSPFPHYKSRSRFLATTIKKGSKCNPIGKNICNGILANKGTELLQCCKKKCVDVIGDMNHCGQCGKKCKQGERCCGGVCTNILYNVNNCGKCNKKCKRGIPCRIGFCGYA', 'MTLNLEFVGLFVLTSVPVYALSFFKALSGIISLIESIFNKIFWAGCEVNRKIS', 'MENSNKVPISKIGLIMLMIFSTFFMSPHARRLEGGSNIDSQRLLHELMVDRIKQKRSRTDLEDKAVPGDRLSPGGPNHIHN', 'MGISAVFTVDETRAWKSNYCRHASGMAVGKALVYFLAYIIMLFICLMWLAIAPTLAVFGLHYRSTIVKLQQSEAAQLQKKVKEYVRGYVREKARPYARGLAPRFSPRGVAPGKTFAMSAFAIASAPGFFPHKRPFCQGFCPFDRDFAPDKR', 'MKTTTTHSALSFTLLIALLIISSDMCMDYVEGRMNSEIVSSLIHTNDNGNCSSCKSDSDCINCILPDCNRAPGGVTTCFQHECVCQVFPPKQY', 'MKISIISSVVLTTLLVASLFQPHQHISAAPQNITDQGANGCIGLHCLIAVDDEADFLMDQRATRPSRMLANAFNYNIAKTNDANKQSPDCGVMGLNGQTTCLGSKQLVNKQGRPCDPTNRVYPYCTA', 'MAFETNQSYLLGVLCIVLIFGSGFITSSADLPSRKNCTIPCTATCDKDCHDKGFQKSGQCIGNILLNGCCCLS', 'MIKTSLLGLFLLISILNFLAHEITATQQVEGFSTSKQQREPKYMYGMILESNKPTSSTQKTNLGLHVVQHAKVSKRARTVYGGANNVKSPHKGKNSASTNSIKSSSLLMASLSLPAMILVGGFF', 'MAGRNFRWLPTLFLIWTDFGGLCKETRLIITRMKRYVLEVKVISGSCVGDIVFIPRLSLEPRDMRISLKNVGVYLPTHVFSHGKLYVAISSVTSIEGLKILITDW', 'MDRNRIKFKKLTLGFMSEYLQRFIYFLIIFLSLFLGEAAFERTETMLTTYAIPCVSNGDCPTRLNLIIQSVLMVFVGYIFA', 'MWLLLFHLPQFHTIHLMFGMEMGNWLKRYGEDFEIVKGRTNWWWLMVVCGSRFRSGLVITLQNCCLSGIMHANRLPWKCLMCFKKLLEVNEKTDLFRLCGGGDVSRLRVVLFKSAIFAPPLILFYYGDENILLVTGDRLFMTWVVVPLQTGVAALTVVVMRGGDAVVVEVIQLWWGW', 'MLTLQRGLRKISANFVLLVSFLPISRSGSALQPGQGVAITETEDNRFIFQFFHLWDMERVSQSGPWLIDNFMLILRKVAFREEPKIVPLNVLEIWTQIHQLSFGFMMEHIGILESSHIRKFVKYDEYNNNCAWTFYPKKVEPGYVEGLRGISPSKIRPQIVSPSLLSLHVAWLNFLRTESFSVTQNTTHTTEISNLLVLEMDSHHATFTSFTTPAFLLRFMNIHQNPHS', 'MYSFLVLMAIYVNHHFLLHILLIFEITWALVARSSFQRRTRGIPGSISRGNNAWPVRMPLRMSRISCPSLVGRKLVRKPKKNKNNNFLNNCAPSKCDN', 'MELKPTISINLILICWYYWYDLSEAKYFFNCKRESEGSPRRSKRLAGEKPELTIDVTSKRTLEYTIKKTRAANANLQQTEATKDTQSEATQSFASPHSSSDSCLESTTNETGQEVMLFGVVIGFSSNKKLPHGVSEVSSANEKQHIENTTSNQRPFVVIYDE', 'MASKSILLLGIFLMVATKVFPYNEDLKIVVNNANPSVASVAAQLVKQPPSLVVITAPTPPSPVDAPPIAEAPTQTPLGQSPESAPAPLATPVVKSWEDCIPLCYVRCKLHLRKVECMTQCMTCCDRCQCVPPGTYGNREKCGKCYTDMRTFLDEIMCP', 'MKHLLSLTLSFFIFVFITNLSLATSNDVEQVLDINGNPIFPGGQYYILPALRGPGGGGVRLGRTGDLKCPVTVLQDRREVKNGLPVKFTIPGISPGIIFTGTPLEIEYTKKPSCAASTKWLIFVDNVIGKACIGIGGPENYPGVQTLKGKFNIQKHASGFGYNLGFCVTGSPTCLDIGRFDNDEAGRRLNLTEHEVYQVVFVDAATYEAEYIKSVV', 'MRKTITIVSSFAIHIILLASIPFTSSQSNDLIDQICKKTPFYDLCSSILNSNPLAPKTDLKGVALVMVNNILTNASDTLNYIESLIKKTTDREMEKALAFCAESYIPVVKYTLPQAADAINQNRFGFASYCISDAVKEVNSCNKKFSGVGMSPLGDRNGIVQKLVDVASAIIKQLLKG', 'MARSFSIVALIVSAFCFSYVLAAPKVAPKAATDKVNFFVVYGQIYCDPCGFDMQTRISKPLGGVKVTFECTKGEKNVTLVKETTTHENGIYNFQIEGAHEEEVCKVKVNGKGPCTYNIDKEYEMTVPTKNLGVTTSSLQRFVNPIQFSPDANRINSDHCGKLGTELGLDKIDVPEDED', 'MVEILKCFYVMITFLFLFFILTDELLVCESDEECPKSTCLPPQIPKCLRMICECV', 'MSTEFNTVSHGVTQPSLKNCDEKGNQPVGLQPKRREKYEWELKRFEDLEISDFSMYDDDIKPFMFMCGRFTYKNKAQIQQDKEIRIARAKHKKLSRNLSPFDAITPPKNANLHFSAIPISLTDDLRLRLTSLCKFALDKYNAENQGANFVFADIVKTTWRPGRTYYITFQAQKQEDPSNIPAIIFQAQVQNKRLGPPVVKSCSMKPT', 'MVKIAKFLYVLIISLSLFLFAITVDGAYVTRFWCYRDLDCRKDMCKPPFNPRCHNHICICRLWGLYEK', 'MASKLALFIQGLLVIVVLISSMGEARNLVEVAMKQNDVDNVKYYAKKSLTNKGDSSSDCFIPGFPGIPGIPEIPGIPGIPGFPGIPGWPGTPNTPNTPASPSPSTTPETPDSPTPSPDDSPAPNTPASPNPSTTPETPDSPAPSLDD', 'MADVLKFVYIVFLFVSQFCAEPDDNQKNCVSDSDCYKKFHLPRHFIMKCIKNRCTFV', 'MAGMPRTTHLFLTVIWFATVWVIWKERNNRVFQNTISNPSILTEKVKLNSFLWLKSKQVALAYTYHD', 'MTINRCLKVMGKNVVSLLLMWLLLPNVGGPQGSPRDPTVVSDLRFDEGFDRLPVSKVF', 'MASTRARFCCVLFFIIFLVYSFAWIYIAAGSAKPWCPSRDFFFTRLLYDNQHH', 'METFRNHGVVCLLLLLIAIVGLKGDEEHTNVNAPNKGNATFVTLSNSSQGINEEGKEKHDNIYNKDVKVSSYNRGGGGSGGGGGGGGGGSSGGGGGGGGGGGGGWGGGGGGGGGGGGWGWGWGGGGGGWWKWGCGGKPRHGKRKGEGKRISHYSNPYPYKVEDYIVGQFAQCMTKTRCKGMRLDCPLHCGGPCYYDCHHMCKAHCRRR', 'MVNICAYFTCLLVFPLIPQDFKLRENCCQVKVCDVKFVFLSSSFKYMFEVDSPYLPLINLYGCLSLFLSPLLPPLSVHPVDGTLPLPCTTPPRCHCRSHGGDGTVTVLSSFSHHLHG', 'MELQREKGKEYDINNSSNQIKFQHFFKNQLSFFFIFFVLLLLNRVTSTMKEKLSFSLPSSKSSSKSNKPSKTFNEDSSTNQQQKQLITEFDPSKPKPNS', 'MALILFLVLSLCITCCRVDARSKTLIDEQNQFTLSKSIMHSMDNNFDCVDIYKQPALQHPLLKNHKIQLFPTFAKNVVRSRSSYGKTTDDCPLGKVPIYNRTRSHQTITNSSSKLHIDDFRRYSQSSQRYHVSGYF', 'MSLFIQKVVFLFVLTLLSVHIVLGGVTINLVNSLKDNLDLTLRCQSKDDDLGSHLLHHGNNYSFHFVPKLPLFGETLFFLFLCMEWWVSSF', 'MSSRSQFFAGVIILIAAFIGSLFLSLFIISLDREFPSFRVESASLNQLTINGTEKMSASLNITLSVSDPNQYLSVSYQETLNADVFYRDEYGDVTLKTSTIQPFNGSMIELKLKVDSYEPLHLAESIVRRRREHGRVAFGLKVFTSIGFKSYIFVSNRKHLKYVCYPLSFGISKNVYNSTPAALLQGVTCTNT', 'MCHNRSLKPLSFVALLCCSSRGSLEFSRSTSWRRKSRFCQLHWSHRSSGGSNLYGKLEKKAKVLLVSLVFAAGFNRNPAFITCEACSALILHSLQVVPLIRNDFYSLSMTNLPQQNSNKMLRIL', 'MASSMLVIKVTCLAMICLVLCIPLANASLTCDEVKQNLTPCLPYVTNPHTLSPPDQCCNGVKTVNDNAQIKPDRQDVCRCLKSLLTGVPGLNGTVASTLPSDCGINFRCPIGPDMDCDKYISHHQPFFSINGFSALLALHETFLMKNLFLDNK', 'MAEIVKYVYVIIIFPSLILFATNIEAIIRCFHDADCVHKICHPPQIRKCVSKICKCRLMITQKDYVLT', 'MKLLMNVAQSMFYIFVMSWGMFMWCNFVVVNRYGVWVFLLLSGQHDHAPGKHGPCRSLDIEWYDHTPSEHDRATSYVLGNFNFPSFMVSNCESIAVSNGLKHDHEKRESEKKRKNAQQRKQQKPVRNNASNRSKHERVVSKEKKKAVDSNATSQAKTKNHVREPKATKSFELRRGGATAVRRAGKAMPTGEDDRKVREKLELLSLKREKKEKEEYQNEKNRISPLYIET', 'MFVHDLIICIYLFIVITTRSMSHFTPFFNFPNLFYRIFYHLLVTFFIFFYNAETDIRCRFYYDCPRLEYHFCECIEDFCAYIRLN', 'MAIFDIHIQILIFFSSSFMVSSSSLASFEVAEKRIYDIEGHDLDGDGAPLPTPKSLLKEPFVKMFLIFEKKSRFLQAANADLKWKFWDAGDAFGRSRFVKIDCKGLDPGICLLVEEMSHESIQR', 'MNPIRRRFPTWITGAFVVLGGIATLNVASTLTLKGLHFASENKRKKTALPCKSCRGKGFYICKLCSGNSTIAWSPMYDPVAINPCVCPTCDGNRVQRCLNCLGKGYD', 'MKKIEIFKIMAMALTLLAIVPKIESEGRPTTPTNQRPLCASQFALVNYACGRLPFTPGVPPAPLEPPPAPDDDGGDDDDGGDDDGGNDEGHKNHHRDHGHGHGHGHKHGHKHRRHQTAEQENCCRWAREVDNQCVCELLVRLPPFLVRPLHLYTLNIGEDCEITYSCGGPI', 'MVEALKLVNVLILFLSIFLSIIVSTSSFPWKLYPCVTDKDCPRKNRHVVKCRKGYCVGVQII', 'MASINFWSFILSLLLITFSSMTSIEGARNLVELPPLPNLPVPLPITQPQLPTIPNLGVPTLPQVPTVPQIPQIPTVPGVPQIPQIPGVPGVPKP', 'MSLSEKISLLLSLQIAFTTTAKVVVNQFELPSPISLVVCGHFPLKPFFISLLAAANSSAGGSGQAATYAAAANSIASLSLRFVASSYCGLTLVGCGMYCYRRLISDIGFHNGVTNVPVEKISEMAKAEN', 'MSSESSGKLLFVMISLIMAGNSSRDSEVAHKHFWVGKETLEAVSLFSTNGAARIPRCLIGRKEEWEITLPTTSDRICSRFSGNRIPMYEAVFQEVGFRLPFPPFQVSVFEWMELCPSQLSPDSFAYMIVFELVCRFLRLPVIGELFFAIFTIQRGLNKDDGYNRVSFR', 'MNDSGRGAIIRTCMLYFLSIGAVEAAEPNCVRAESSQEIFECSRGAREGAVRAVTLSYKNLLNRIQMQYVNDAALGVDFSKLVASSHKYWVIHTEKDCRIEAFDIEPGTQAYETTVNHCVARKSVGRATYLDSL', 'MAHSQGKALAQWMIGALLFAMLAGSLAVQICNIDPNDLKQSCSKFVTGRNPPRADEACCGVLRRANLPCLCGYKSALTYYGINAKKALALPGQCGLQTPSNC', 'MAMVSKFFIFSKFSCLLIHNLLYHFINILIFFFFTTVLVECIENNDCEKGMCKFPFIVRCLMDQCKCVRIHNLI', 'MTQILKLVYIVILFVSILFVAEAEQCVSDADCQIKFPGPRQHLLRCTQGNCVMLVGQDPFEKEHVAEDDVFPSTVHAYHVES', 'MVGCFAATPKNLAMAIACSLSGAVLLAVGAHISYVNVEPQRARTLARDKLVLDTLRKKYGYIPPSEVRRMARNDSNKEHN', 'MVKSSNLVYVLILFLSIFLSIIISNSSFGMIFDRACKTMKDCLKL', 'MILTIVLLLTAYYKTTYTPNVFLDLLGCARKLDGSWNFIQDMPIKKEITVCGDCHDGTKYISLVMGREIGSIILPMESVLVAIIGE', 'MSPYVPFLLALVFVLFNSSTDANHLENQTTNVVAISFKSLMPTTTPMLKFNCTTASFFLRPQETHFLDVSIDQGVSCHAEWNDHLEADIIAFDPKTDLLLGRGVYWMIRTDGLLKSLNNFNFEKKAAWGP', 'MKVPSGSAASLLTKLWIIGWIGFYGAVNSLYDVKEYTGLLFSTTLLVSKTSTNNHIKSACQNYLGMAFQGSLPLDHCTTGCALPPSHHTLCCGQSTHAEHIESAALLHIGNSLHRLCSEKYDGSNNIVVRGWKAETNELKIRKIRYSSGKPITCQTIQ', 'MNEGVEDDLILFGDLSFFSLLLLLLIGLTDQLRSTLLETLSLPADFPKEINPQEFLPILLFLLLWTCFMRSLSPHENLQGNFCHSDQKVSCGYCSLFLRKVPQEIFLRFLKSSGNNYPRRYYLGVLIFGRRKGVFCGIFTRFRRKIRRKFERFY', 'MACILLVLLCASWLSNARLHPQGGVVEVNGISSTKANVTTAIDGKNESVNSPSRSSFHGKRSLRTHLSDDSGFVAFTTDYHPPRHHPPKNNK', 'MAKHISQYFLVGLLFIALALTLGPTPGLSSCFPSDCTEPRTSELTAAGSYFGS', 'MAPSFTVSAVLMIIMVFMPSPILSSYSQSDPIPELPTLPTTSSATKTDPSPSSISPFQNLSPEIAPLLPSPGGALPTPTGSDIPTIPSNPSPPNPDDVIAPGPFYAFAPYGSIQATSNGHRSVAFDIATAAFAGLAALFSLQYMRV', 'MKGRLFFFLLTLQIVSLVCVAKRPFLSTHGISHSAQENASKSSPPYTIESKKLKGIMGKEGMSKKREVENDREVSKIGSSPPSCEHKCYDCFPCEATQVPSRTSHLGIMYANYEPESWKCKCGPSFYSP', 'MFSTVFTLSFAVSFFTPLLLKVSLSSVFNLLVLLPNDSLSKFERRWDHICVIDCVASQYNAFKI', 'MEACNKVMIVGMLFAIANAMFANGELTVCNLTRKERMACESYIVHHDPSPTCCYAITKADFQCFCEYKKNGWIVFYGILPRITLEIPVKYCDPLHLHVDLWAMPLDDFEHDVADCHTGDVVGEVPQSSCFERGGPSVPVSIVKETREYVVGSGELGRVNGPKVPWLISPSKVDVVPIMERTPSVVASVGQTLMSLATSMWMLQCGSYLL', 'MPIPCLTDKDCPKKNGYVGKCRKGYCVGI', 'MLLMFFIYGVVALLSEKTRLVPLSLSLVYRVFVAFNHSSIFTTCLNRINYADIV', 'MEKKKGNSTSYLLALGFILALTFPTLLSASRFYAIKDHLFPEFIKWHVSVVNSLNYNQILFTHCKSSEDDVGINNLSPGSNITWSFRTDFFHSTMFWCYVTKDSASLKFEAFRYDERLFNKCDWKNCIWVAKDDGVYLKNLSQRLDELVYKWDTGM', 'MERKTLASLCFFLIVLLAAQVVAQIVPCKTRNRNFKSACIAVSGDDEECDHDCRRVGGWYGGSCKNQKCVCDC', 'MISTGINLVMTVIGFAVSTMFIVFVCTRLICARIHMNANRRRSFLIASRSNLSLMERGCQGLERIDVAKFPVKKYSDKFFAAEENSQCTVCLSEYQDEDTLRILPHCGHSFHMACIDLWLQQNATCPVCRVSLREFSETTRSMQPVYSSHHGMESFDTHHYHCIMADNGLLTRTPDNRGVNPIQEDQFPSEGGGAVDMDSITSLSQGDFIKDEEKKHVESPSNL', 'MFLVQAMDCFVCLVIIPLVKICGYVCGTLPCVKCPRSWAIFGTFGFGYDNASDLTRVRVLSLRDNVWKPIQDFPAVLLPFQRTHSDVSDDVYLNGSFNWLALQREFRPGLYDWKNINVKEFVIVSLDLGTYTCSHLMPPCVKSILVKHQYIDYPYRQKHVDKFCVTMCK', 'FGDLGSFSYQMCLAFNIIYLLVVWTIWNETNRRIFQHKEEYLEFLGKKIKPHSFWWLESKYSIFDFDYNFWRHNPIIYLSVVPWFLFSLFS', 'MEGNLSLSPALSLSLSLSLSLSLSPCFNIFQLLSF', 'MGSPMASSLLTTLMAILSLNFISQISANNSVHSSSPPPPKFPYHYSYPPPPPKKPYKYPSPPPQIYKYKSPPPPLPKKPYKYPSPPPPIYMYKSPPPPIYKYKSLPPPPPKKPYKYPSPPPPVYKYKSPPSPSPKKTYKYPSPPPPVYKYKSSPPPYKYPSPQQPLQLSQQTTNYYYKSPPPPHRKP', 'MCLLPQQMWLLPSWSLLCMSFPNSSGVVRSRRTSLESIQRGTFGGSTVYPILSSSTLLHSLTLSCRDLCTRIFLLTRSGPDILPIHCRSSAALEAEWSMRCRFQRWFQIHSSSASWRASGPIIA', 'MPKIVKFIYVMIIIISLFLVATNDEEDCFVDADCVNLILCDFDEKPKCIINICQCFPWTVIH', 'MTNQMSTSFSFFVILVLVVAGQLIQVEGECTKIVGSCARQDCSEHCNSFARGVRVLGSSCSFYNLCTCTYDQPPPGLTPPGNCGIGMGICTGDCQDDCCNKRCVSRYSKTGVGKCVDALDMSLCLCSYDR', 'MLSTILILKVACVTVVSTAAVVGCIYLVSPMIQPQAQAVAVPVGGGGGVQVTEPEPVWTEPMIGWVKLNVSGSLFPQNRSAGCGGVLRDSSRKWLCGFAQKLKPNLKANEIEKDNKEIVNFVNNGPKGKSIDPLIIFLI', 'MAQNKTIVFSVISLALIIFCMQSIEGRLVKYIDESNLLKNVKHDGISDANEATLVNVTPTILPPSAVVGSNGVAAPPPSHDVGAFRPTTPGNSPGVGHSIHY', 'MLKSIFKGIMLFTQIVSLIVKVNVTNETSSTLIKHQRTDVKKRAWFTINPTDKLDELKAKLDDFFIHIGSNHRTYYVVVQVPGVDLGEDKEEDRWKTVYFPQLVKDDSDVDYMFRVMVENNLLHLYVRSVEV', 'MKINMVLIKYFALLFFTFEFNLLTNHVYALNIGAETTGVAVSVSKECSRQCESSFCSVPPLLRYGKYCGLLYSGCPGEKPCDGLDACCMKHDQCVTVKNNDYLSQQCSQTFIDCMEKFRNTKAPTFKGNTCQADDVIEVIKIVMEAALLAGRVLHKP', 'MAQILFYVCAFIILFSPLIAQRIVGMSLFTPFFYFNLYYSQNFITLYYCYALR', 'MAFPISHLFTLTLTPAVGFPPGSDPSAPITVITTKLFPVSLLTDCGNQAKKDCAYSRCRSCCKNKDFNCHTHIKTSEALGFLLIEN', 'MPTTPFMTGLSRAVGIFFAAHLFCIGGLFRTFRVPSMTTRRTGPTLSGLWLSLLMRSSGSLRPLR', 'MAKTIKFVYTMILFLSLFIVAKEVHAYPGCETDAECPKIYELYPLIYKCENKFCILSQVLPYIV', 'MAEIVKYVYVIIIFLSTILVATNIEGTMSCFHDADCVHKRCQLPQIPKCVGKKCRCRGQYQANPMG', 'MGICYCFLFLTYFHVISLPCRSNADCPPHMCKTGWPKCDQTHKDCWCFLPPSVHNNIPNVILQITN', 'MAKILMFVYALIIFISLVITGRSTINVMCYYDHDCPFVLDHIAECKGGVCEYTAFFYE', 'MAHLARICLFYVLLFLSHELLLTTTEGRSLRQSIQPPNIASTKMMSTSQLYHRSNRSLEGDVEAFRPTTPGHSPGIGHSINN', 'MPLSLSLSLSLSPSLLASPSSSSFFCKHVESIIRTVTDTANLNCGKKSRDCKNYRNSFEKEIRNQEIN', 'MGARWMFMNPIRYGVGVLFFTLVGIQDEYMKIAKHIALRNAKDKIPFHLTTLSCLIVDMSISKVIAN', 'MERIDICVILVFTSTTFLFASSWCLTSRESRKENNLKWCDWSEEIEKESQATEATKQMQRLTLKHLKKRRNEQRP', 'MILFIIPCPYSPIVSVIILSGDRCYPYERCVVGSEFCNHFCM', 'MYGMYVYAVIGHINLYGRLALLLFETLAVALAESGIWWLWFGWPGLEDQVTSVLLIKAKFPSLAKFEKRDPSSYKSLIIFMILLISLLNKLA', 'MVSIFILLLLFSNYFMSVHGRVVSLAESHISLPSEKLQSSTHIHFYFHDILDGEKPTTLKIINPPNESSHGPFGATYILDNPLTREPNLSSKLIGRAQGTYSLASQQGDFAFKIDINFVFTAGTYKGSTLTMLGRNVIVDEVREMPIVGGTGAFRFARGYALAKTVWYNSTSGNAIEEFNITILHL', 'MANITKFVYIAILFLSLFFIGMNDAAILECREDSHCVTKIKCVLPRKPECRNNACTCYKDCILLINDHMA', 'MIQILIFVYALIIFISLFLVVTTHIPCVHHDDCPKRPYPRFMKCVDNFCETWIIGWE', 'MASLNSYHLILLMVIIFIPHLLLYPASCFYQPHSAIPPRGLLFEEKNRLGSAPPTCHNKCNQCHPCMAVQVPSHEHVQPGHTASTSSTEGVNFFLQGNNNGYSNYKPLSWKCHCGGNFFNP', 'MERASQRVVGCGRLSLVAWLCDATSACAKDRGGGCGAGTAAVEDSAMGDGTTLEGVAVGNNNSACHSNGITKSLGNSKNAITEKFAIYVMRERKSMENNSESIASKEKEVESATK', 'MEKHICQSFLLGLFCIMVVLASGLPTGTDPDIDCTGHGPCVDCKERCINEGYKKGGACLGFPLCCCMKN', 'MKKWTSLKAYRWGKASKTTHTSFSLEHGVNRPCIIANDCPDSMCTFPEYPICYRGFCYCV', 'MKGVKISLNFIQTIIFLLILVSTLCLSIEASTCKPSGKIKGKKFKSRLCNRADQSECCKPGKSYTTFKCSPQVSHKTKATLTLNSFEKGGDGGAPSECDNKFHSDKTPVVALSTGWFNNNKRCLKKITIFGNGKKVNAMVVDECDSTQGCDAEHDFQPPCSNNIVDASAAVWKALGVPEKDWGEMEVFWSDT', 'MATNGYLSVSLLFISSFIAFVLSAMESQLVIKVKFENTFKRFNVSVDENNRMDLKMVELRAKIRSDFNFTDNANFSLRYVDEEGDLVNLVDDDDLHDMMRQQLKYLRIEVQYGNGFFQLRKMISKSVWKILQKEEVLYYGSTFCGRVVFSKSNIRSIGRPISKGWLYGHPPRQPVIKKG', 'MASKRCSLIWVVLILFMLSFSSKALARNIPDVPKLLEDSDEMGPNKRLLFQVPKCRRGRITSEIRADKPRCHHYF', 'MTQFIFFIYVLMIFLSLFLVESEKLDIRCATVDDCPKVTKPVVMMCTGKFCHYFFVRKQIL', 'MAQFLLFIYSLIIFLSLFFGEAAYERTEPIMHNGEPINLIPCVTVADCPRMDEPLHMTCLVGACWPCIRSLY', 'MNTILKFIFVVFLFLSIFLSAGNSKSYGPCTTLQDCETHNWFEVCSCIDFECKCWSLL', 'MTLSILILSILAQYLDNVFIRCSNPHLTSRFYGVVLDPTTISKIVSEPFQDPLGHLLSDFRYQTTNQLCPQTNLIVLVVRSVLRVSHQIQDGLLICL', 'MTYVKLITLALFLVTTLLMFQTKNVEAEFCSSVGSFCSPFNTNPCGYLGNCRCVPYYLYGGTCENPFGFEHNMKMIEEHPNLCQTHAECIKKGSGNFCARYANADVEYGWCFASVAEAERYFKIGSNTAVKSLFKIASKSKEQDYLKMALEIAT', 'MKIKHFFFMFLLCLLLIISIRAIQTSKDGNQFGVMKESKTKIGIDGWRDWGGSFPDVEKENNGGGDKESGQGKSAKEKGEAHESVTVGWRDWGGAFLHVEKENVGRINNEGEHGKFAKENGEDTKNESLNHLIK', 'MLMVMCSSIPTQEILVVLFVIIHVIFCMVSLEKLVDLAFFMQRFWVYTMV', 'MAEIFKFVYSVILFVSLYLFVIYAEKECDTDADCRKKFAGANQHLLWCNNGYCECHTH', 'MTYAKLATLAVFFLATLIIFPMKKVEADRCAMAVCSDSLKTCGSVYCVCIPNEYRCLPASYKDLVKIPGKNPNYCQSHVECKEKGRGSFCARYPSPNVDYGWCVASISEAEDFFFKLASKSTVTKDFLKMFEIA', 'GRKCITTMKLLTQFGIQMFLLKVSICAWRLLRNRWPTKDNFLCYNIIPFDSQLCVSGCGKNETTNHLLIYCATFGMLWQHIKTWISFNSVDPQHILDHFLQFAYSSGGFKPHRSFLQLILLCSVYLLWNEKNNIQFSNRAKSIIQLLERVKIVSLN', 'MSYAAFIGNLLFILYKVFEVGLFVYCEYAIGNCRSCTEVDALGFDLPEALLCLITH', 'MDDQFYTSNTQPKSRWIRYCCGLCGIIGLILSLIVLLLLIGGTLYLIYRPEAPKFAIDNVSVKGMSLTSLSSISPEVDVSIKADNVNKIGIYYEKNNTVEMFYKDDQIGNGLLPTFYQPKNNVTAFNMVLNGNNVVLPDADQKALVNDIAVRNVTLTLKLKTRLRFKVGFINMGKVEFMIECNMTVDELTEQAKIVDKNCWKKNYNYLFGRY', 'MDLLWTACLSVWMGRRCAGLVAWVLQCYGKQLGDRMLAPRENQQPQAQPAPKKKGPAKKKGPSKKAGPPTATTTEASPGNTTAPPTASTTLPPPSTIVPPPAATNIVSLASNNVSTTSNSVPIVKPQIVGENILMVPKKCRNIGLKRKSDKMEPIGTQQSVYKK', 'MSHLISVLVLLLAIITLTSAARNINPKYESARSNGFISYQPPTTDTQKLVGLNHRRRRHLAPPPPPKKPPPIHVPGTPQRQPHQHPIRPPPKM', 'MLCLILISCILYFVDEIYSNSWSSLFLFCRLMVNLPDAANREKIMRVILAKEELAPDVDLEALANMTDGYSGSDLKVVFQYILIIHSCLLENVAHNSR', 'MLCYIIISILLANQWTVKLKVTIDLDVYITDDVGRTRLGKETLYNSVCREREANAPPRRECGCGRSAREGAECTSSRVRLRRMVFTPLQRQNLALRGILHVEARSCIDGLFESATFLFPSNLAPVCLDFVD', 'MGNLVVTPCFHPQNKKLLSSSSSSSSSLLPSSAKLIFWQGSTRLLNGKHIAGEILLEYPETMVCHADSFFIGHPIPSLDLEDELFQGETYFVLPIHLFSCKTLSVSSLLSFRLHNNDSNNKSTIKFGECPFEYLKDCDGKVLIKVMPEFITRLVNGGDRSGDDCCISSKSSFLCSTPELKKHYEMLVKSKDQVWSPKLETILENKVRLRKERKREKAQPQTVSAR', 'MSQIFMFAYVLIIFLSLFHVETNIRINILFFSFDNTDKIGCKTSEDCPYLGKCIEDFCQFKK', 'MISMLCFCFLSTSGVAFNKVNFTALRFFKYINQLADIKTKPLKLEVFFKLAEKDVKHKLRYSCDTEICVGARHDTYSTPGHTPNPRRIVLHG', 'MKLSLLILFFLSLLISSSSSVTTAEEPICDPNNVRFIRILYCKNSDCLSKCLEKYGTSGTKNPNCNGRDTCVCCNDK', 'MMVIFVFWVPLLLTALFVLKQHRRVADPNKVQFVLWNPATDEFVLIPLAPDEFGAGHPDRYDTPGTQGINLVGLVMTKLEMTLSLKSNSWKILDIDMPILYYGLLEQGLDVYMNGMCHFLDTTDNEQCLMM', 'VQWELLMCFFFGTKLIVNGDLPDVAAYMEGMKNASLQLTQSVSQISTNSSASLSDDLLNTKRMTIESMIESTQVCQGSVLATICEIETGVEWFKLHVIEMDDTGSTTFVLFDRQVTQFIGRNVEDLIKEHDKGEGASDYPTDFNVLIDKQVLFKVE', 'MTTFFKSVYTMIILLLLFLFVTNASKSANCTSTVNCVHTPCMSPTLPLTGRSTQDNTSFKNNCILYFPM', 'MASSMLIKVTCLSVMCLLLAIPLANADPDPKCKNVAESIIPCVEYIMTPDASNPPAPCCNGMTSLAGQVQALPERQFACRCIKDGIFDLPDLNLAALAALPNNCGVDLRFQITPDMDCDNLN', 'MMRQNSSLLLVLLILCVASSNSISAKVVDVDIICKEASNPSYCSNLLNSKPGGAKGVDLVDLARYTIDVLNNNSSDTLNLIHNLVRSAENDTVANYYYRCDIDLLNHADSVTSSLTDAQVSLSFRKYPAMAKDSADIMKYILECIDSLHKHKTSPLLATYVENLRQSAQVFQIITKYLNLEK', 'MGKSHHWAMKALTVVAVVLKSILCVVFDVVGDAYSLDLPYNYKGVAEANKRGIIRGCVCKRGEEEQVAYEVGSGLGHFKWETARVGEAMEEGEVDSMGVIKGDCNIHLPNILLNGRNF', 'MIVVTTYVAQQLLILFIVADKAKSWSNGNSFKWPTSGNSLKNFKLRMVVTASHNPWKKYENAADYVGEDGSFGDDKHNQP', 'MAKTIASLVLVFLVVAMLNGYGAEGAGRDNQHKKDDGVYKYQKNTLACVLMCKMCDFGYRPSCDEYDRFCTPYGLCIPQSPPTYNADVQTTPAQSGYSEVQNPNP', 'MVRVAKVVMLCIVLVIFCSNHVEGAGRLLKESVKHPETFNMGGGNGILPSSPGNFAAGVNYGPDGLRFCTFPGGCTSGNTFPTIPGNLGGTGTGNGIGAGFLPPHP', 'MTHKLIALRFWVRVWCLSCLCGCSSLDVDDPLRSLVTQQWYQSPVRRRVRPRQPVRLQRSQPATAIQASKILQGGHDELMDSLEGECCCVQG', 'MAKIIKFVYVLAIFFSLFLVAKNVNGWTCVEDSDCPANICQPPMQRMCFYGECACVRSKFCT', 'MSSTSSLFFIFLFLIATLVCSSSLCGVVCLSYFAQVFLRFRLSGQLRFSADSINDFCVVNVANPET', 'MKNGSRRLNFGFLIFWGSFGAIQSSSPKIRFLTKNSYWISSINQDHVTFEIMNISEYNNMNYQTP', 'MTKALAFLLLLLQLTSFSAFAEDTSDTFNDIFDFDADDDALNAFSKEFETLHKPSHAHSLNHQSPASSPHHHFHSPSPAPSRHHQSLPPSLHQQPHAPSPHHHSPSPAPSLPHQSPAASPHHQFPAPSPHHHLLPLLATNLPLRPLTTNLPLPPLTTNLPLYLQNPQPITTLPSVMVVRGVVYVKSCNYSGFDTLKKATPLLGPVVKLQCNNTKYKLDETDKDGHFSLVGPKIITIYTAKQCDVVLVSAPHGLKPSNLHNGITGVILRPKRRFVSKGVPFILFGTQPLAFEPSCPH', 'MSSFKHGLGLYLLLLVFSHSLLTSPVAYAWFGRRGFWWIPWWRWGDKENDLLHDINIYRKVQNLPILEENYDASCLAIKFAYDLKDKHCEDFHDFHPLPSRNPSIPNFQKSVWKCNITINTTKDWVLMRVCVPGLDEDDLFSNYTRDSHFTEYLNNSKYTTGRGWFWW', 'MERIFVKNDLCLLIVVVALIVVKELEGCLEKEKLGLLDLKTFLISNSGSTYNNLTSWDDKSDVDCCLWERVKCNHSTGHVIDLLLGGVTIPTDTTDDWLLNFSYFLPFNQLVHLDLKANYLDGWVEIEGNFIVID', 'MATTTTIFTALIFLFSLSFTTTVRSDSDNDCVYTVYVRTGSILKGGTDSKIGVKLYDKYGYYIYIKNLESWGGLMGSGFNYFERGNLDIFSGRGPCLEGPVCAVNVTSDGDGPHHGWYCNYVEVTSTGPHVSCSQEQFTVEQWLATDTSPYQLWSVRNLCHYKLDQARPKTGAPVHHRKIGSEFSILDAPIVREI', 'MHNIYKVWIRTLNTTKKTNEFSMFTGEEWCPNIAIWSYIIECKTDNDCPISQLKIYAWKCVKNGCHLFDVIPMMYE', 'MCPLRFILIFFSAVLAAYFAWTTVSSTTPEIDFAAHNHDDKSSSNKGHFSFIQMIKNGFWVLVDMASGRYLWKNLRSTNNHVQRQNS', 'KFIMCCSSLYCVVLSIIYCLTNQTEDIDHLFLSCKFFRIIWLGISNWLGFNTVHPAHVVEHFHQFEYLGGFSKNIRATFQLI', 'MLLVIPLLYLICHLLRYLNITDANGYKAQVTGIGQASPLPSLSLNYVLFISGSPFNLISISKLTQSLNCSITFSSDSFLIQDRSTGKTIGT', 'MERKITLLILMVALLFCMTKVLCADSSVHIQDQFTHFEVVKGPNRRLLAFVDCGTRCNVRCSVHSRPNVCMRACGTCCLRCKCVPPGTYGNREMCGRCYTDMITRGNKPKCP', 'MSSPHSFFTNLGLGYSIAITLAFLVLLATLILSFYLCCRKNNNPNNNLNNNTNTLNYTEGVIVRTDIFIAENDEENAVVGLEDNMINSYPRFQFSRDSATVLGQNTTCSICLCEYKDSEMLRMMPECRHYFHLCCLDSWLKLNGSCPVCRNSPLPTPLSTPLQEVVPLSQYAADRRRRR', 'MDLMMNKKAMVMKLALLVFLLGFTSTVVDARFDGSSFITQLLSNGEATYEVKSTTTACCNSCPCTKSIPPQCHCADIGEKCHSACKRCLCTRSFPPQCRCTDTTDFCYEPCSYSSNGGSH', 'MHGLGLYLLLLVVSHSLLTSPMAYAWYGRRRGYLLIPLTHRHRRRYDPDDDLLHDINTYRKALNLPILEKNYKASCLANRIAYDLEDKHCEYFHDFHPLPGINPEIPNFQRSVKKCDININTTRDGVVMPVCVPEIMHENDLFSNYTKNSRFTPYLNNSKYTLAGVGHDLDKMVLIIGTNTASGDFSSATYLLHGASKGRHWLLMTTAFLSLFVFLFN', 'MYIHVKENFLHCLYLLHVAILIFIVLGYYSSTIESFSKICLETPRQNGAVKRKNRTLKEMIRTMINE', 'MYSLSTLSSLYPRLKKRMMKKLPLIFLAYWGILAVEAANIKRDCPEDDFHVQRPARDATIFLARILYFKLKPARAIYAKVKDHDKVTASGRT', 'MVIYTLMLLNLVLLEVSLKKSRTGFTVIWISVLFVIWKDRNRRIFQNHSDHLDALLERIKLQTFWWLKANYIMFDFDYPFWRNNPLLCIQAML', 'MTKIIKFVYFMTIFISPFVVASLHEISGYVLGLPAGYCTSNHHCPVYNCTHPKQPWCKLVRLQLLFHGSLIGLCDCI', 'MVAQIKDTNMYYNMLLQKSVVWVLGWFGSVAAVSSCCWLPRLLIVVKEPFVATFGNTADDHCAYRH', 'MKMGAIGGSKRRLSSSSSRGLGGVLKEQRARLYIIRRCVVMLLCCEDFVDDSKIGIASTAVTFYVVYGIYLFYPKTGSTYEVFAALSVV', 'MANSFFLSLLVLFLAAFMLAPQQHFADAVSFGPAVEALIPKIKNLIPKDKPVHIPHIPDFCKKHYPLLKKVENFRNKHCPPA', 'MARTQGVAAASPSAIEAIFVLLQSLSATLSQRLAAIFWSLWKHRNIKVWEDVTETCATVVERARSLVEDWQLVNSSATGPYDLQPAAVSAAAIDVPAAAQLRWQPPEHGRMRCNIEAAFSSHSNRTGIGICIRDEGGVFVLARTISFNGVYPVDIEEALGLYHALQ', 'MHLFLSCPSFFPVWYQVSCWLGWEFVMPLGLAQQFQAFMGLGGGRRVRLCLLLVWHVVISIIWATQNDLIFAGGVLHEEPVVDRVKLSAWKWFLAKCPASSCSLYEGRCSSSCV', 'MSWNTLLFKAVRAYALMNALTIQTAVRASALNMVPHVILSSRWWEKSLERSKRPRGLRFSANLRKPGRSDIHRSPRRAMNLASRVMNFIARLASPFPSPRERFWHRSPRRTTLPRLASLGREHVIFCVSTFLVGPWMP', 'MPLIFAWTALSGFRLSKLFTFFFFLTEVISQLLVMPTLQSLTVLETDVDVCFTCS', 'MFSQSKRTLGLPVCLLFSFLIQSNKTVNWSGFSCKNFECLNGKKLNRGCMHCFDLVNGYENQRVKQM', 'MELHNFFTNITFIFSFLFVLFKIVKTLSANNSTVNLPPGPWTMPLVGNLHQIISSSLLHIRVFVGGFELGLAKSKTRNI', 'MAKVTKFVYIAIHILSLFFIAMNDAVIFECSEDSHCVTKIKCVLPRKPECRNTQCTCYRGYKGSFTLHH', 'MAKLLKIVNNMEIIIFLFLFVTSQCLEVNGEVLNPRGKVFHSAWCNEDSDCKPDVCTPLSVICDAQRCKCHTS', 'KTCNYSSECLFHNCPLGWVMKCFTYFCACS', 'MSSLCRFFVGLFALIGAFFVILFLCTLIINRTIQFPTFQVESASLNQLTGNGRKLGTEKMSAAWNITVSLSNPKQYLSVSYQDTLDAEIFYMDEYGGIITLNTSTIQPFNGSMIEMKLKVDSCEPHVTNNIYRSRREHGRVEFGLKLFTSIQFKKDIFESNWITLKVVCYPLSFVISQNVYNITTSAGILLQGVTCTRS', 'MLFQMLYLWWFIEAITASRGQCSANSDEQILQWPEEEFTKLVFAFDSISKVKEEEEEEKGEEYEVYILMMMENTVVVTEDVVGGRRDENRKEEDFFFPGVVLPEVEESGKKFNVEGLQ', 'MACGLRCTKGLKICCGVTTIFLIVVLVILLILFLTDFKQKDPTITLQSVKFGRFHLDILPILDLNLSLAILVTVDNPNHGSFTYQNSSAYLNYRGNLLATAPLHEDTIPALKSHNISTILNVYVDVTEVPDLLGDYLGGVINFTSTTTLVGKVKIFNFIKIKATTYSTCDIFVYIHGQIANSTCDNKIKF', 'MAYAKLAPLVVFLFATFLMFPMENVEAQSCLSACTIFGSKPLCVSSRCRHCCKMPLNSFVGLCERRLSTDSERWKRNILTTSVSLIWIAQKKGIGSFCARYSNFDNEHGWCFASISEAEHIFKIASQYLVGRGGPKEPVGYLGMEIG', 'MAKQMQLHTLIMAMCLKSSLLVGSKIAVKIEIAQAINTDKGGYNPSPIRKKENLQSIIKLNSDCSGKEQSREKKDKDTSSYPGSFHNTEVVQSPLHFQGDFTIITKITTAQYFLSMRLHKNAQAHTQESSNAQALSKRLLMLKHAGKRLLIKQKIQRIEFELNT', 'MSGGSSIAFRLWVALCLCSVFTLLLLDFFLIPSMLVTKGWCMQLGRTLFTRLKVYSGPDHPHEAQQPIDLPIRDKRIQLQR', 'MTSSLSKFYTIFILILLCLVFLLIFTWEVEAKLCGKPNRTLSSPCIDSVCYAKCIDSDLDAVSGSCEGFFTSECFCYYEC', 'RFHRLFTKPLSLVRAVLISRHFWLGLVAILVSTGSVVFIWTTSTCRQHDAADQVLACEVKKLREDEEVPMPPDRCAWLWVPCILIHASRGSYVTYARKFLEVWGIYLEYWQ', 'MGNYLIAAMGLQAIWSFVLALMDAYAVVRRKVLHNPVLVCLYMVGDWVSLSQSVVLVGFICVD', 'MKVVLLLFILGFATTIDAHFDPSSLVTQVLPNGDASYYVKKSTTTATACCDKCYCTKSRPPQCHCADLNKTCNSACKLCACLPSPPVLCRCVDITNFCYPPCN', 'MVEILKYVYNMILFVSLYLLGIYVERECYTYADCRRKYRGANKHLLWCNDGYCEYHTQ', 'MDKTLKFVYIMILFLSIFYILIVCDSNAFGMTLRPCLTDKDCPRMPPHNIKCRKGHCVPIGKPFK', 'MAEFLHMTYVMIIFIFLFLSLIDAEAVHRCIEYTDCPEDMCHLPLVVVCHDHICKCLRLIKIRSY', 'MAKVLKLVNVMIIFLALVLVAMNVNVTYFCFFFIIADVINCTQDSDCQSIGCLSHLKPKCTMLGFFFNAFVGICECDQVM', 'MATSMFVKVTFLIVICLVLGISMTNAALLCPQVQLTVVPCLGYLRNPSPSVPAPCCNGIRALNNQAKTTPDLSGLNFAAAAAVPRKCGVNLPYKISPAIDCNTYVHLSQNQPSFFISYIYIHFVKFCQIKKLA', 'MTNSIKFVYVMMYFLSIFLISTYFETKLNCIDDSDCPYDMCDPGLLPRCLNGWCDCSRFQPWPMDSMSSNLREFTLPN', 'MNLETFTNICPSLVLCSILHILTVLFGFIIFGGSILNDVLANFDTDPVILAYLIEIYSTSLFESATPFISCLFSRLSSTWVKGNLIGCFWAKSGETHGRKTRENAHFRGAASEVGRWAPTHRANTALLASWTGQHACCYVFGCVGAGV', 'MYPWKFQALKQIVLVFSFSQCSFELFCTSIKTVVGNDSLKKQRGVSEVKSGSVCFESLVVRVESEFGSLECLRFFVQFDRVQICHWFICAEIAAAVPNLFGDRDRSCSVHSLLFHRRGAGVISLEVGRFSRVAGLKMAWDAIILSAVFLRPVCYHVSAVFLTGVRPSDASLMFSYVVLLGAQGCSVSSRSCGLTELHSFELAWLLVL', 'MASSILVKVTCLAMICLVLSIPLANAVITCPDADITLKSCLPYVAHPTQWPPLECCTAVLGLTARAVTREDRQAVCKCLLGLMNGIPGLDLTAFAAVPILCAANIGYIIRPNMDCNSYISHHQPSFSISYIQYAVLIYIYNIWYPSIEQLEGLVLNE', 'MALLSLTLLSLFHLSCSKHSTYILKYYSLLCRTLSNYVLDMMNSSIKFMTFSVVLHFHRFVSVG', 'MKLGLIISSLLYFVILMLIQNQGCNGCLEKERIGLLEIKHYILSQDEGYSYHSTEEYSYNIKELGSWVDDRDSNCCSWKRVKCSNTSSGHITELSLYLLLFETPDSKMLNVSLFRPFEELRLLDLSYNSFQGWIGNEALAIRLLALQETLHFARGKIPEKMTESLRKGTFMREFARGGNPGKTAHR', 'MDKLIHIICFFLLTTLLQTLHCQPHQPQTNLNYSLCREESYNYGDQLSNISYPFWGHNRPSHCGGGDLFYLNCFEDQRKNFTSTILLGSQNFTVLNINLTTYTIKMRRTDLADEVCTLKFNDTYLSPNIFQFPT', 'MGITRISSLTTIFLLTFLIFAPDMYVISIHCRTNADCPRNMCKIGLPECDQTRKECWCFLPPSVDNNNIPNVIPQVTN', 'MFLVLDRLCLWSSSQGRVLWLLCVWIVWNERNNRIFKNTQTTTTDLLEKVKYQSLWWLNAQNANFVYGNYNWWSDSYYVWVSTGLSFVIIQ', 'MTIINYTPLFFLCFITPLALSNVEIHSGPSTQILHSFNEDRNGSITLSVKAEHAHSTSTKFSWHGSCTNKDISISQSRETLTGIPKYVVQIVNTCVSSECAPYNIHLHCGWFASARIINPKLFKRLSFDDCLVNGGKPLTSSQIIRFTYTNSFVYPLALKSAKFC', 'MVSFNGFYDVVTIMIGFTLLFTTSLCSEISQPPSGGPFNDVASAPKPLSPYEKYLTNCASKLKPAECGKQIFSGVFVGNQIISDYCCHSLVNDVGKSCHYDLTRNALQWPAYAKNKTEILKRNDKVWNDCIAVHPILP', 'MVPESKFRGPHLIVIALCLSLILFARRTSRRRCHMLCHLPHDQSSHTCTLKVVAVTFSHCCHRSAITGKGRNHL', 'MCVCPVQSKLPSGYCCLLFVALITVLAERWCLRLCIYSAIWGQGFVTKIADFRRLRRR', 'MGCLWDCRLLSVVSSMWAAVLCFSLLPLVFFYSFGRVCCCRTLIYVREHFFFGLGMVVGIIVGMEDQQVVSRAILFDCGLTIERVNVTSKHPKGEYNRASAFGGENFDGDIKSVSMEV', 'MVKTPKLVYVLILFLSICFSITISNSSFGRIVYWNCKTDKDCKQHRGFNFRCRSGNCIPIRR', 'MLWKKTFLLVCYFSVLYIYKQLGTFYHKFSSTGTVVDTNIVHPRNYDFYMCAHAGMIVSFSI', 'LGIHNGECRNIVRIRCIGACPGNYFCDIDCKHKGYTRGECSPPPKYHDCCCVLN', 'MPPNFIVLGMIGAINFVIIYLVVKKCGSYLNHKRCLFNPRQSLIAKCKHFYQPTLLLTILPDSVTEVA', 'MTMIGNKSLSMSIAKLAIAFFIMIMVPKSISAQNCGCEEGLCCSQFGYCGNTDPYCGTGCKQGPCYAGRTPPSLPSNDVKFTYYCDESNAEYTCAPNKGFYGCGPIQLSGKDNGSDGLNSPETVANDPVLDETCVSINGRLECDGAKPTTVQTRVGCYTQYCSQLGVAPGDKLTF', 'MFVYVLIIFLSLFLIEASIKTKIACVTDNDCPRAIKPVVMWCINNYCHYYLYGYQ', 'MATRYLHFTTLVLAVSILVVGISGQFECGGDLNGIVYHCKPFVLKDGPTLPPSDLCCNALNGVDVSCYCQYVTPRLMQNISIDKALNVARNCELQDIPTGKCGSKLIFLKSLIE', 'MALLPFESLMWKMLSRVLLHTIVSLWLRKDSFVKQYVDEFSYQSCMVEESIFLHRWPSVSILHHEFLIYNELLETKRSNKEGVMSAKRTYLREAFGRKGVEKLISKIKIKLINSFAMLRMVWKVNPRELCSEILDWFHQGFHKHFEKIWLQMLG', 'MAPSTNLSLLLTISMIFISHAISRTSSPNLYKSVCKEIGKEPYEQPCLKLLETYPQITSAIDYLTFSRLFLRIVAIDNATKVQHKVKEMTKKYPSSQAIKGCNIHYDVIVDELTGALDEDKIYISLDVVYAFDALEKCSLVNEKIFNISSISTMNSEMELILAIISSAANHVL', 'MAKKCHETKAFRVPRQTMDVQQRRISTQEGLCRDINLSIFNCGKCGHRCPLDELCFFGMCGYGMGSSPFLVKPSKRMRPKPPQIPWIPSYHYPPPPIDPKEEF', 'MANPTTLFALFFLLSFCFAGTVTSDDCVYTVYVRTGSIIKAGTDSIMTLTLYNADGYGILIRDLEAWGGLMGSGYNYFERGNLDIFSGRGPCLDGPVCNMNLTSDGSGSHHGWYCNYVEVTTTGAHIPCAQQQFEVEQWLATDTSPYELSAIRNNCQYNNLGQAHHKLKTVDAVSSESGSDFSILASTVHV', 'MVARISIYLVYSLVVVLPYYSVLCQDALQSLIDGAKQSGISQDTLDDAQSALGDASVQQAAEGALADESLADWVQQADELTSKGSTPPAVEAPANLPADEEEDIAESSKKSPTKAPKLAPNSAPDKAPKLAPNSAPDKAPPAKPPELPRKYKAPSPAPQAHSPNAASPQA', 'MLHCNLFASVWNFILQWLGVATSLNQDVVGHFTQFRSLGGIAKSPQSILQVIWCATVWEIWKERNNKIFNDKHSSIV', 'MAPLWCWALAVAPLTYALRRLCLYAYHCSTNRVLTRKLVRVYVFRWQKRVNSEINSVFGEIGKFDEFDRVNTCKLGKDWAIDEALESKWRVKRHGDDKRYATPTTVFAFLCMTFCFESYMTNFP', 'MAHIFNYVYALLVFLSLFLMVTNGIHIGCDKDRDCPKQMCHLNQTPKCLKNICKCV', 'MQETNNRLLLIVITIVLPTVGLSSSKFDTIRITTDNFSNANKHGQAGFGPAYKRLYKNSAKGDIEFKRPHNITIFILSS', 'MAKFVNFVYSMIIFLSLLVVAMNAKRNYQCDPCFGHPDDMINFCPPGTAPKCFHGLIKCVPIMRGTNRMFA', 'MFFSFFSLNLLSPIFTLPLAPALYVFGDSLLDNGNNNFLPTLARANFLPYGVDFPRGPTGRFSNGRTVADFLGT', 'MALLFLVGVVYEHVPLFCSHCRIIGHNISNCKWIHPEKEHGNPGKKSMIEEPLLQRKF', 'MTDREQLMNWKCEETLMKILMLSLLLLLLLLFLEFIVVEIEFEMFVVVVFDGNEEIVVVSVNGNRFMEKEIVDGNEEFVVIICCESMVSHVCFLNPIVDQES', 'MTRIVKFIFISIFLVAMSNPNFNPGDPFLDVLNFLNYFIHNIFSHLNNTLSFSFFITTGRKCKQNSDCSKEICVFPWKPTCVEPYFLMILIKRYNYCTCT', 'MSLTQVILISLPTLFTTPSFSCSIKSMTNFEFPTNFSNELPNDVVFCGKVITRKTEPQANKDNTTVVAGIRSLSVKENRNRRSSSKSYTGMFGMVKFPLQMELSDIKMRQERRDPMKLPKFTEEEDRDGGSCWEMVRPLRRRWTIMRALKTSFGCIHIV', 'MAGHISQYFLLGILCIALVLASGSKIPSLSECVVPTPCVNPNSCNAECSTSGYQRGVCLYFPPQHSWLCCCV', 'MAGLITFLFLFKSQTQAYQSLVWPIPTPIGDARVIEEIQRDLTKKLQKNWAGAMTTVWARRTLEPPLNQRSRLKRFREPKFVEIF', 'MVLISLVHYILLSMAILSPKGMSCCLFLIILLVRPKVQENLQLHATFTITIQQAGVPLKFDERDKAFPKYQERSGKEDII', 'MKHFHFFLLLSLLFLTPRVYAIRIKFSSPSSTSSHHAFHIPFFNSPSRSKGKEFKSQKRKVPTGSNPLHNKR', 'MNMESHSKMPVTTFVAMLFALAFASSLCNASIESVNVDVNFSDGVFVSCNSESNGLTNQGSGNVHVTVPVGQRVICRASWNGSSALFDGYDPQYDQGHAFVYWEVRQDGLYHSWDDKNFVKKGVWKIPAIVEN', 'MSRGRSDSSSSWLRWSLVLFAMISALMPCLKKKEKEEENENENEGLDENEDGEEDNNKIRRTRWKELKMERIWELEFLWN', 'MALAFEARSGLTFSFFFFLLLCSATARNPIIFSSDEALNMVGRSLKVINLDDYGGPTANRGHDPTLNRPATGRGRRVGRKG', 'MNLLMMISLLMTSSLQEHFVFRSALSSGAVFFRRCYTRGAVTLAHLYRELNNAARWNCGKMSGMGSKDVWAGYRENRDLRAMHFIPRYGGGSGTVACRCSKTEAQPKQMASAFVLMQFGFHNPDSFNTFRIL', 'MLYSMDKKRGASGVVLVLLVLVFLSFSIANVNGDSVNDEDQSKKSAFWVWQRLRSAYSMYSSVFPTSIGQYWHMVKAIVNHTYTYFFPPNIDFRRGEEGEAVVDNNGAGDKVKEALAKSLGTSKATLEDAAKSAADKVKRSLSDDDDREKKHPKEL', 'MANMNVACVVLVICMFVCTYAEAIIYCYDTADIIKPCFIYLLTDFDFPEIEPDCCNAVKKVDDAAITKRDRRETCSCLKQLASNIFHLDVDKGDRLFGLCEAKIHYKINPNAECAKIKAEEIVANARSGNILTTI', 'MEIEETPYIGFPLGLALLVFLLFFICCFFCCCLYWDKLQFLFPSSVVNVIINPQPRIQPEFASSHHKPGFPVLMMKENHDQSLPVLMPGDMVPKFIALACPCKAGSNG', 'MKTPPNMFMLLSLTLFFLSSIYFSKSNASDDFFCNADDKAALLKIRDHFGGPNGRLDDWDNNTECCDWNFVGCGRPYPGRVTGVTIGRGWGLSGTLPAEFGDLPYLSMLSLAEMPKVTGPIPNSFSKLKRLENLDLGSNSLSGPIPEFLGKLKRLQEVDLSNNKLSGAIPASLDNLQSLSQFNVSFNQLCGAIPTGLSKFAKSSFEHNKCLCDAPLAPCKGGLASTK', 'MALIVLLSLSVQLIDVFSLQHHGAWKINVYAFNRLV', 'MSFLSLSLSLSLSLSLSLCFYPLQVCPEHHFRSRTLSASDQSFQLRRKRRKSQHNSTPLLVFFSPSDCGVEEDRDHLFFYCESYDGQ', 'MLEQVMILKILIALQCGGAVLGGCRVHGNLELAIKAAEALFALEPQNTYITHVDD', 'MSFVLSLSCLQFVLSLSCLQFGEGRVGVEKKRRFAVEGRRRRIYCVGGIAINRKCSNNNGVATIHVSLSLLDLPNEKKI', 'MGFEGVTSWYQSHVVLVWAKIWMFSCLCLESNDVAIANAMAQALAQVSANAMQGQQVQGRADELRFDHFMQNHPPRRNLKYYVWEIFIG', 'MSSCTCHHPFVVKLCILQAIFVWRIRFLGWIIFPLLTKFILWLFKKKVIAKVVPAPSSIAESSILINASDASKPFGRGKGYRFGTLCNRLGHTNEHICSSIHCFSYFYAIQTVSVS', 'MKSFVYNIFFVFFFNLLTKIQVSLADCDNLQDTCPSISPNKQTIFINGLPCKNPANVTAQDFKTNELSNASSMDIFGASVKLVTAAEFPGLNTLGLSIGRIDIDNDGLVNFHYHPRATEMIFVKKGVLLVGFIDTQNQVFQNVLKVGDVCVFPKGLFHYILNQGFEEAIVYSVYNSQNPGQVSIIPTTFDTTLESVEKLKKKIISLSASQVHGDVISSTS', 'MAMALNSALYIAKMAWIALSGWITSCLTIADEFATSLRSGDIGPFHVGPPHHKAIIAKQVYSYPDIDFKRMKSYKLI', 'MTSFSSYLLVFFLASLILIPQGFASQPHHHPNHPLSTQGFGDHSHPIHPTNLPKPHPHHPPKEDNNHF', 'MAETLKFVYVILFISLFLMIIVSDSFNPLIRQYCVTDKDCPKFKKYNIRCRKGFCVQVNGG', 'MVQTPFFIYAFIIFLSLFPYVINGVDIPCIVDDDCPILFFPRYSMCIDGLCLLFFLY', 'MMKKLPLRFLAYWGILAVKAANAIIKKYCPEEDFHVQRLARDATICLAGILYFIHSSAGAAPPPSLSIQYKGSASCGGQSPVNGSSNES', 'MISDGILSVICFLILMNLPEIYMFVSDTPIVLLKLDNIVISDSDPRPQNSAEFVSATEFQHS', 'MTKIIKFVYALVLFLSLFIVSQAAQNDWMKCKTDDECPKVSNPPLYFKCIDRGCRIVIKMRF', 'MYCYCYLPLHKENKCICKVMESNKNTFVKLSVLLLLLCFTATIVDARFDSTPFTTRVLSNGEDTYDVIYHSACCDKCECYVKEGHFFPQCICRDDGCHSACKKCDCIRDSESETPSCYCADVMFFTCYDLCKFKL', 'MFSAASVRGAKHGFSFTFNRYSLFLLCLVEDVPTVASSPSIASRSFTPYKSIHPRTYRTQRHDSRAPPACSPSALVPQQRSNIIRSHGNQGNWLPSRYTSNESVELKTQNDVVRFSLYKPDDIGSANKNQNKKKKIKKGQAK', 'MYNCYVSFLSLSLSLSLSLSVLSAASSPEHHFRSRTLSASDQCFQLMIKRRKSQPNSTPHSCVFSHLHRETSGCLKEIN', 'MSHHLSVLVLFLAIITLTNIAGNPKHQIHAQTDGPIIYQPPTTDLQKSVGLNKHRPRHRKLLYNNPSIYIPGVPHIGNGGDPFHP', 'MGSRNKELWLLIMFAFSVGGGVNTRTDSDCKFGALRFKHGGGRRQHGGQTVWVGFCIGLGLGNFMKKISMGCQIEFCWPEN', 'MTLNILSLNLMFVMLVLLPVNYVRKCGRTEKLSFQTHAQFNQSSQSSCDASVVSRKLYNLDELNYKAHDNI', 'MAVSYSFLALSFFFFLFTLSPLPVSATDHIVGANRGWNPGINYTLWANNHTIYVGDYISFRYQKNQYNVFLVNQTGYDNCTLDSAVGNWSSGKDFILFNKSMRYYFICGNGQCNNGMKVSVFVHPLPSPPPSSSQHNHSSPNSAAPMVLEYLGHKFLMLSFVFVMFGYVLV', 'MFVVAIEHSKEGKQFGETRKSKTNIIADGYGVWPSGFGIWYKPGGNTNGGGGGNIINFPGSGKRGGGANINRERKSSLEKVCIEN', 'MSMTRLRLLKLYFSVFLFTRHDYAKERRKHGVKFRSIMMESRPPHYVLRGQDGLKVTPKILSFLGHLKVIMPKRDYTRRYITFKRPPLSGVISHEPKHETTLRMTL', 'MWFNLSSFSLLTPATNTTFQWLLLLPLDFNPTPKSIHFISILFTSSSFGNFIPDSPTQDPDFIFIPKSHFHHENSLNHHKLTIQRNLQLRIEEQGRYLQMMFEKQCKSGVEPFKASSSAIEKCVELVKIDQKDQRCKGAKLQVFES', 'MGIMYSLTLLLFRMTMLTTLMFVLRQPIEEEPLQHKPSQIYKPTLLKPKFIKASFIDNTILSPPPRPPRPPRIHPAPVPVFHSSKKT', 'DFGEVWHFIHLWLGVCSVIPNVPADYLNQFGFVVGNCSKGWLSIMHLIWYATVWKIWKERNNRIFNSKECSTYQIIDKIKSLTYV', 'MQHSLPTSSEAGVRPAPAPAVTADTVADTNTSTFKDEIVMVKDFSCDRVLTKEEKKLKAIEDKFVYFDDIEDEECPTLVEDRGHRSISELGTIVDIFDAKYLADTKNGRPMPGVPVLDFKTDRPLVDMLTKCAKLAIDDYNLKMKTNYRFVDFEMAVWQLVNGVFYHITFRARNAEKECQTFQATLFANRLMREVVEFRMKGSNTWYDGTLSELID', 'MAQVIMFVGALIIFLSLFLVETKKTDIPCDSRNDCPQQILPRYVLCVNGLCRIYFP', 'MAYQNYFTFFILFFFFYVLITKTFAIQNPVVFDIRNDLPSNILGDLAVGCNQSDHMSHINVGDHYNRTFQVGQSWECDASWSRYFTFGWEVYKENRDEGHSTIYWSVRQDGFYHSLDASHWKWLAQWYTE', 'MIIQLNKNPLMCFSLTLLYFLACSRRHLLYLVSLAQVVRA', 'MRVGLLFCVDVIDVEQLMVMVMMMLLLLLLMIVDVFEFFWKNNEDDDGVHECDIEDDDKSKEEDEEKEN', 'MTAILKFVYTIIIYLFLLHVVAKDLPFNICEKDEDRLEFCAHDEVAKCLLNI', 'MARPAREPRINCCTCCTSFITSTGLTILFLWLTLRTQQPKCFIQSLYLPSLNKTITSNHKHSINNTIIFNLKLANQNEDKGVYYDAICLTFALFLDVNTTRPLANSTLDGFYQGHGKTAEKWSSAEARGGGGGVNRTVQGSVFLRVEFVTRVKYKILFVYTKRHRLSGGANVEVNVSSGQKVNPKGIRLGNDPVRVGSEAAPVRGLWVALSGTFVSLLVLSAFT', 'MLGWWIVQTTVLPVSTVFLTVLITTAAALASSPEVGSSMNIIEGLATNSTATVSLLRCSIERPQTPGSPTNAFRSGFSSTSSMTSSTNIFNYIVNNNYS', 'MAKSSTFSITLLISLNLTFLSIISLATATNYYQNLSPTMLGFQEEKFTHLHFYFHDIVSGPKPSMVFVAEPNGKVKNALPFGTVVAMDDPLTAGPERDSKLVGKAQGIYTSISQEEMGLMMVMTMAFTDGDFNGSTLSILGRNMIMSEPVREMAIVGGTGAFRFVRGYAQAKFYSVDYTKGDAIVEYDVFVFHY', 'MAKIVKFIYLMIIFLSPFLVSTKILEKHTNKCAATVGLDIYEKDKCVTDFDCVKNLWLCPIDQFVRCIDETCKCILF', 'MASKKCLLVFVVLFFCILSFSAKTLARNIVDSSELYAFSDEKGLNKRLLQENRRAPCKRGKRDQMRQARDNPRCHNYL', 'MVEIRLEPFLILAILWIYAVFPVKKVLQYSEHFQGFAELFFLDQGVYQEGWGWSDFNLVKVQAFIQLRWWCTGVDYRTPPLTGGLLRPTRRRTATILPEIQIFRRFAAELRRDHGGGRWLSGRAPVAMDVDILLTFFFLIEWTDSYGPNI', 'MLFSIRIIFFISIATCMFIVANQYSWNNGIVDPTDATRKCFLFEQCTGGVAGWTETYGDILTFTIIRGAGHAAPTSQPRRSLRLFKSFIEAKPLTGNVTVPF', 'MARSVPLVSTIFVFLLLLVATGPSMVAEARTCESQSHKFKGPCASDHNCASVCQTERFSGGHCRGFRRRCFCTTHC', 'MARLSFCLFTLLLLLSFSVSETRPLRNHGPFSFHFPATSSLFLGTVKHYLYSGSKGRSLAKIDHKILKTSAKVSFSIGFRESNATRNRYFKPLRVSPGGPDAHHHFKVTATGGHGRR', 'MASSMLTKVTCLAMICLVLSIPLANSAPSCPEVQQTLAPCGPYLIQPGPPPPPEPCCNGVKTLNDQARTQQDRRDVCGCLKSIVANPKLNLPAVASLPKDCGVDIGYVISPDMDCSKVN', 'MPCQIFWCAVQLLQCCIAAKDRVQPCKHVPISGCASELRTAALSFLDRNDTPIYSVDEGLLRDWWNFLVQFMVLIMARAGIREVHFLPFNPVDKRTALIYVDNNDGSWHRASKAAPEQIMNLCNLTEYEKKKVHAIIEKFAERELRSLDVARQVQFHRIHTGAGKIVPEV', 'MERKTLWFLFMLFLLLAADIAVKTAEGRRCESKSHKFKGPCSRDSNCASVCRGEGFTGGDCRGFRRRCFCTRNC', 'MRRGDFGGLWANFFVILIVGGEGPFFGTFNRSGWLLYLLGLEDWGAVLGATRYILRDSGMCGIDSDFDNALDGLRGTILDFGISSFTNKDIVPPKAQHVLANAVFFSKSVQDMEVKFDMITRHNAVLGCLQTTHAHDFLLAISIDGLKSAYVTSEVAYYP', 'MKSTYLFFTTFLAMTSFAFALDLSPLQDFCVATNDIKTGVLVNGQYCKDPKLVTADDFFFSVKEGVVSSPVGTQVTLVTVNEILGLNTLGVSLARIDFAPKGINPPHTHPRATEILMVLDGTLNVGFYTSNQESSTPITKDLNKGDVFVFPIGLIHFEHNTGDGNAVAISGFSSQNPGIIRIIKDTFEFNPKNYSELLTKAFQMDNNIHT', 'MKFIQLCFLISLLVLLNTKALQGAKPDRVVDRHGKPLESGKGYYVWQFWAHDIGGLTLSSTRNKTCPLDVIRNPKELGSPVYFSAPGFKHIPTQTDLSIKIRFRSSSCNQSKVLKLSKEGSGFWFLSTGGVAGDVVSKFKIEKLEGDTGIPIYIFKFCPSVPGALCAPVRTFTDTDGTKVMAVGDGNDLEPYYVRGFPLLLQRIASRNSNMADQDSIDPSKMIGGRRAMTQSHRRERQSGHIPNRGRGRRVDGSGSSQEASQATQSEESQVVDPSQQVEYLNYQHQVHHDVTDD', 'MRLLTHILVALSLELVTNLGVGARAVTPPSPVEKYSQWTTHDSILLHIGSHPSSSVQPYHLSSRIEFMTSNSLYLNSLLTWASEHVQLHHPLLWRSTRSGQHKTQSFYTLDLTHLPPFNHIIFRSELNEESDCSGKEQSREKKDKDTSSYPGSFHNTEVVQSPLHFQGDFTIITKITTAQYFLSMRLHKTMLKHTRKSLPMLKH', 'MCGTVCLCACDIIPCCLRLPCAMCLYAWKRRPLLNFLHRSSLKNKQNQLPTRSWAPRRLESHTGVPKAARNVVPGAHGTSVWAPEAAPQV', 'MAFSYVLGCGLDLLLVFRTNSVLYSKIHTETQHAVAAKLLQAVGLGVIHDCRAGGSVGLLERDIGATVQRHKALDG', 'MEVGRKIIVCLLMIITMGSYRIEGREPVLHRVGGGRYTWDPKINFTNWASNEHFYQGDWLYFGFDKHIYNVLEVNKTNYEKCVDKGFISNITRGGRDVFQLLEAKTYYFLCGRGFCFHGMKVDINVEPLPPDNASPIVPEKACSTRKMNQIKLSSVLLVLAMTWIFL', 'MAKVLNCFYHMIISVFTFIIVVDCVVICDLEKDCRQYLCIPPEFPRCIGGICRCK', 'MFDHFLSFCSLFCCLFYSILISLCRPSLKGHMQYCLTHPEEMSKLSKLKAQITEVKGIMMDNIEKVHDTKPNNTHMHSFCFYYMFPVLDRGEKIELLVDKTENLQFQVLTIIQIQTFPNSETIY', 'MASTLFAIASSLFVELAITSRVARLTSYSVALQVLVKVAKFWKNGVLKAAVEVCHTLPSNQIAIQFGLSIDLADCIDWTIRFERVLESIVQIRKVMSYKIDLYKVEFENGVFEELDQSFRKSDGENEGIDF', 'MVVQISLLFPINALGANVLESISAPVRGNGVNDGELKTLFGSGCRGGRRCKKERRCGEVRVLLVLGLTDVEGAEDDKRVREKTSKHREKNVRES', 'MYEVVKFVYVMIIIFSPFHVAMNFEPFIRCLSANDCPNDMCELPQKPMCLIYSYETLTAGKCICF', 'MGSNNMIKLFLLTTLILLVLSIENTSAARTLLQRGPVKGSTKNPCSTVPGRSKGRCTLAEIHVAGHINVVHSSPPPFPADIIPKFGAAVINNEQKRELIKTFTESDKHLQFKK', 'MASSTYLSILLTISMIFISHANSPTPSLKLYQNVCKQPGQKDFEQRCLKLIEGYPPITLIKDYLTFCRSFLKIAAIGKAIKSQQQVKEIMKKYPSSQPIKECANDYNSVVGQLKVALIEEPDLIDLDIRYASDALGRCEDSLAREKNVNTSAIATLNHEMQLYNDIVLIAAGHL', 'MLLLFCNSGQWLSFVLEQLALLALLLSVFLAGSGGFGCFGAVVSREYAAVSSNFCAGWLLSSAVFGVGRPVFAVFWCYPPVDQFVSVLEMISELYESFI', 'MELILIFGVIVSVEYVPDGFEGWRGAWQCHRRLWAWEEKLIEECRDLLLTVTLQDDSSDMWLWLPDQSGGYTVCGVYDMLTVQNLANRGIVSMDSRLCLAGCGQVEDVNHLFLSCPVFGALWPLVRDWLGVEGAESQVILNRGVRFFHQIWLLCVWVLWKDQNDKLFRNTHSSLPHMLDKVKSYSLWWLKAGNVAFRFSTQNWWSNPLVCLGIG', 'MFALLGLLVILKAYAGIGICIRDDEGTFVFAQTTPLSHVHYVAVGEALSLFHASQWLSDMQLEAFHHRQVDVTEFGQVISACQSLFNTHFSNSKVEFSKRQTNEIAHTLAGVAALTVSPTIYYRVPRCINSLIINEML', 'MLCKCFKANLWHLILLFRGFHIENKVVNCTDVAPCRLYIKQPSSNTSVSGDDGDAEWAEYKIKETNMFTVDKYQQVRLFSVAGKLYCKPYSFFVDFLVLSL', 'MIIFVSLFLVVTFSKEECTYAADCYKRYPRWSLLPNYCIEGSCYSDFLNSGKKYLSP', 'MTEILKFVCIMIIFLSSFIVSQNIDGNTGGNRKCFRDSDCPKFMCPSYLAVKCIGRLCRCGRPELQVELNPK', 'MAGLPRYTQSFFTGIWFAFVWIIWKARNERIFKDALTHPYALVEKVANATLKNLMRVMVTPGLTMYHLNNHLQTFNFVFLLSGSHLHSLCVVNHSC', 'MRQTSLLIINFILTLSLLLVLNMSELAILLKRMRAMLNWLLYRSPYFVGTLKCRLKKDLHQQSHD', 'MAFQNPIVTFILLTSILCVISTRQANEPAPKSSKTEDNIGELEQQAQDAIITTVAELKYAKEFMEFIPKLKTLANLNDEQGTQVDQCVGGLTTAMNNVLMEVEKMENSWPSMSWGTETLQNVMDVVLTYNVICDHALEGVDGTIKLMVQRKIEDVSLLTNDVILRVAKIVWH', 'MANSLPFLLIIFMLVFSANTKATSLVAKDQVPCTMCAECENPCQPLPPPPPKVLECPPPPSPSPPPPTPPPSPPLPPAIVECPPPPPAPKSPCPNNCESPPMTPYPPQNSYFPPGTPFPYYVPPDYYNKNSGETLVPFQAIFFSNPNSHFVYSIIQCLINLLFLCLPYV', 'MHHFPLATKLALLLTGAPQTTATTTAVLGDSPPPISNLTRQIPTSLHFRGEIAFLGNGIKS', 'MSLTTLMFRSMLRLLTLLICEPATSIPTLLYYSGLLPHNMVLLRMVRHELLEQENYLFRLLISLMSCFL', 'MQIGKNMVKTPKLVYVLILFLSIFLSMIVSNSSFLGTFISSCKRDKDCPKLYGANFRCRKGTCVPPI', 'MINNKVTILLVLMCFIIAVFCSDPDPVMDYCIAKSEDNKFSCKNSSTATVEDFTFSGIKLPGNFKDTGFSSMGVNSNVFPGLNTLGVSFVRADFDAGGVNVPHFHPRATEVAIVLEGKIYSGFVDTKNKIFAKVLEKGEVMVFPRGLVHFQMNVGDVPATIFGSFDSQNPGLMKIPNVVFGSEIKDELLEKAFGLNSKELSKLKKRFSPS', 'MKLLSVTKPTMMVPSHCLIFSLIFVSILANEATLLHEANGSFPMVPVMEAGKMMMMMNESRRKLGSFRICAICTCCGGAKGICIASPCCYAIDCNIPNKPFGLCSFIPKACNCFGCHL', 'MKLSILLPIVIIVLAVTRPNTVNCRGYPSQVKAIDEPINELKLKVIDEPIKELKIMKEKANNVFRVLIGGQVHTMTSGPSEKGPGH', 'MEFHLFHIITFLMVVLVGTNATVSPPQLYWKSMLPNSPMPKSITNLLHPG', 'MKIKPFISMVFLCALIIISVMEIESSKDGKQSDAKEEYNTQARIDAWKAWNYEPSRFISYGKKKGGNGSGGLGSGGNGGERVTHGGGEENEGKKG', 'MAREFHAILGGGQGDYNSGILYLLGIIFLSLSVLSIIIFSCGDDNHHKHRKRRGGRGGISGIGGDGGGHGGGGGVDGGGGGGGG', 'MSIESNKENISPVYTNKKKKKKKNSTERKMKRSPLADITHLFDNSSTLTTTREEHYLKASFAKSEQKFRNIYDSKRLSPQGPDPRHH', 'MKRNVARLLIILPLLWDGGGDYSSCAQGLHLCNSGWVKLNCNRAFKENGNCVGCCGLLQDTNDKCDSKLFINMVTNNCKTSWTTTSLIQRIKNLLDVVDTLRFIILSDKTIEVRLT', 'MNIKPFIVLLFLCALVLINVVAIEPFKDENQIGVIEESKTRIEIDWFYGIFCGPHGYIWGWGKEWKKDIEGGKGERAREEYEHNGGEKNEGIERDQKSERDAENENKGWLSYFTFCKGWEGRY', 'MAKTLNYVYVLILFISIFLSITVYGYIPGIVNKPCKTDKDCPKKPPHNIRCRKGQCVEIL', 'MSFSHMFTAFLFSLIVCLVISECHADVNANASHISKLVIDARTRRPIPDTFFGAFFEEINHAGAGGLWAELVDNR', 'MSKFLKFIYVIIILSFLFYVERGVSSASPFYCVDDDYFCFGLCLPPMIDHCTLRGQCICITISTEVES', 'MAKIFNYVYALIMFLSLFLMGTSGMKYGCKHTGHCPRKMCGAKTTKCRNNKCQCVQL', 'MARVISLFYALIIFLFLFLVATNGDLSPCLRSGDCSKDECPSHLVPKCIGLTCYCI', 'MTTVFGVHARALFFSVLKLDILFFNVIVCGSSRNDSPSSISLYIHLRRNTNLAPDNFTYAFAFAACSNDKHLMLLHAHSIIDGYGSNLLVGEERVFGRKSSLTAPWRRAADGRKREGEIE', 'MASKKCSLIWVILFLCMLGLSVKAIARNIVGATEMYAVSDEIMGQNRKLLSPDQRCKRGKIHYNLHDVMTDRKASNNPRCHHYL', 'MTIDHITSFLILPMCRVGLAMHFSVMETQHPITNGWGRVSNLGHPSYSPYKVNSNHYVT', 'MASISIPTRSIKNACGEGEWFGFASVSCNDEDYHTGDVDSYREGDDDDDGDYDYAPAA', 'MKNDILTSNFQQLIETTLFHTLIIFLFSLPLSLSLSLSLSLSLSLYCHCPIRIEEKGCPNKLLKIGCQYIILHT', 'MSTQKNLWLLLSIFSLNLAIITLASDPDPVQDFCIPNPILASMIKTHHTFHTILPCKNSSEVITNDFIFSNMKTSGNFSETGLAVMPANPTNFPGLNTLGMSFARTDIEIGGINPPHFHPRATELIHVIQGKVYSGFVDSNNKVFARILEQGEVMVFPRGLVHFMMNVGDEVVTLFGSFNSQNPGLQKIPSAVFGSGIDEELLQKAFGLSSKQIGTMKRKLDPKQER', 'MEGTCIIALSLSLSLSLSLSLSLSLSLSLFYLLLFSSGNHLRSRTLFVYDQCFQLRRKRRKS', 'MTRTWLSVHFCGASSCEMLVLAFLDRFVGLYVVPEQALSKIDMNQTTREHSSELDSTIISLNLVHESNELNSG', 'MFILVFSYTHFHLLFATYLHFHFVVPTQAQKKAPPNKHCIDKFLIQSIVAWPGATAKDISEMFEKKSGNEVKDFELKKQISCSLQLSNKSDNHMVLRIR', 'MGDIIKVVFAMIIYLYMLTIVTNAVTICDSDQDCRRYRCDPPEYPRCLGILCKCVYVSG', 'MLYLHIETMSFFASNVYIVFMFLNLILLLLSTCEVEAEVCRKKSITWLGLCINNLSCSTTCVSEHALYGACQEFSSSFIHFFLLWIDSCIAWLLDGGVDI', 'MLKKDLSLSLSLSLSLSLSLSLSLLLGIVYVINNNNKFRI', 'MAEIIKFICLTILFLSLFLVAAEEDIGGHLECVEDEDCMEESCPIFSVHKCKNSGCECDEMFR', 'MSQVVMFVYTLIIFLFPSHVITNKIAIYCVSDDDCLKTFTPLDLKCVDNVCEFNLRCKGKCGERDEKFVFLKALKKMDQKLVLEEQGNAREVKIPKKLLFDRIQVPTPATKDQVEEDDYDDDDEEEEEEEDDVDMWFHLPDVVCH', 'MKGIKLLVSMSQAMMILLLVCTVLVMLPVASAKRWIVGDKKGWTTNINYSTWIEGNNFYNGDWLFFSYDRNQMNVLEVNKTDYETCNSDHPIYNWAAGAGRDVVPLNVTRDYYLISGKGFCFGGMKLAIHVKNYPPPPVARGVHGLGQPKKSRKTHPKNQKKRVGSGNCKKQR', 'MCVLLKWMLDVFRMATLLGGCVFKNNSGDISMIVCKKESIATDPCMEEALGVR', 'MELFGMFQKLQLLLLFAKSLLSLSNCGWATGLSQNPKFRRRDHKHQEYLEHDEAEDAQDDEFPYVEKEEEAPELDSYPCGPSHIFHGLLLDDHTCYPSFYIFLPDNYTSKLNFESLLLFPLSSKLLFDLRFENF', 'MAFSHKKMLFTVQQILLILFTIFIASPQSGVACRPLLVHNDQWSSEYGLVWQLLANSPAPPSGGGEPTHP', 'MQTQHFITLFFFLLLSCNTVTLTTSSSSSENGDTFEFVRPIDRKLLSLKKEKLSHFRFYWHDIVSGKNPTSVAVVPPPMKLNSTTAFGSVRMIDNPLTLGPQLNSKIVGKAQGFYASACKDEVDLLMAMNFAFTEGKYNGSTLTILGRNAVFHKVREMPVIGGSGFFRFARGYAEANTHWFDIKSGDAIVEYNVYVFHY', 'MKEILKFFYSMILFVSLFIYCCNQCLCNISVFLFLIAGKCISHMHCIYLSCGFPQLTYCLQNECYCLNM', 'MTHSPTQSQSRKFENIMIIVIVLFMIVMVALFIKDNTGYMSGIKYKVVDASLIEFNLTSDNIIVYGLKVNMTATNSNEEAYKPKSEYFAHFSYKCNRFTELSMEPFVIPSKTTMLLEPTVLGGYTTIKKLKPLQLDEYNEETRLGIYNLNLYIDPCISCTNLRVMLISKGILKPDTTCKRNLWF', 'MQQSLALLSMIVQGKFLIYFLGALIIFVYSLNSVTIHMFIKLSTLMLQG', 'MPTILLRIFLLYNVVNSFLISLVPKKLITFFPHSWFTHQTLTTPSSTSKRGLVFTKTITMDPNELKRVFQMFDRNDDGRITKKELNDSLENLGIFIPDKELSQMIEKIDVNRDGCVDIEEFRELYESIMSERDEEEEEDMREAFNVFDQNGDGFISVDELRSVLVSLGLKQGRTVEDCKKMIGTVDVDGNGLVDYKEFKQMMKGGGFTALS', 'MQKKLPLLSLSGSWLPLWFVVRFPPVRGSLFRFGDSSENHHHRFVPFAIRVRIRHRTQARSRKACESEKKKGKTGSVQKQATRKQAARTQASTQASKHASNAPATRTQASSAHAGKQQASKHASNAHASKQRARKQARTRANTQSNKNVKNAKNTKQRRQAKSHRNREVTAVVLPLFPSVFLNFFIKVQV', 'MLDAILMALFLPCIGMGVILLVYMCLLWYATTHPSTITIILKPVINTGLSPSDLQKLPTITGKDLSAGPECAVCLDDITQEQSARVIPGCNHAFHLECADTWLSKQPICPVCRAKLDPTLFIPSSHDHQNPC', 'MAKTSTVSLVLLVLAVALILNGYSASAEGEVRANQLVKDEVGTLNSAAAEGDGRVNQLLVKDEADQLHDDDDKSHKSRKKIIIKLIFIICINYNKLCGIDKSYCSQYNNLCRNIHTESIVAKSGNPNAEILP', 'MAFSNIAMVLCFFLAINMALPTLATFYTVGDSLGWQIGVEYSKWTSEKTFVVGDSLVFLYGAIHTVDEVAASDYISCTTGNPISSDNSGETTIALKTAGTHYFISATFGDCSSGMRLAVKVEAGSASIATSF', 'MAHIIMFVYALIYALIIFSSLFVRDGIPCLSDDECPEMSHYSFKCNNKICEYDLGEMSDDDYYLEMSRE', 'MLMWPLTATWLINYRWQVAAAVYYSWDSSREVTLPKVEVKPNINGAFVHVESALYAGEYDDDSNIEEMSYLTKRFQYKKRRFLGRRNESSGSSFINKKEDQQGGCFNCKKFGHFMADCSELHKGKSKKESSMKESYGSKLRKSFMATWDKMDKESDFDEEETNLGMMAITTSDA', 'MKLITIFLEMATFIILIPSVQSTLSHTSLLEQICRQSPHYHLCTMTLRSSINHRSKEDIAGFARLTLEIVKANASLTLEHVQKGYIQQTNGLEQKRAMRDCIALYNMIVNVHLREALNAMEKYDYKIVKQRVYAASIQAEICENKFKNSLTKPLKDTNRYVQNLCIIAASIVNKLLLQNHLTSAY', 'MNLSNGVRLPSSLRMIRSSSHRCKFQILDVGVFHRTLDYGVFRVIEGGASLSPSDNSETNPNSSEMKEAFVHGDANFFELFESWCEKYGKTYSEEEKRYSFKVFKDNYPVKAPDKSYLCLAFLNEYGAIDHPNGSSHPLDFMAPVTIVNPLNPIFHRAPCICIMTETL', 'MALKVETILLVLVCGAIFSGGNLKNVDAQKICPQFCYDTVAYMTCPSTGGQHLYPKCNCCLASIGCILYEADGTPICTAV', 'PTRSRTILPSAVALDAFLSALAKPIFLRSSCDLIMLPSLSVRAFLQSKSFQLLFRPEVASQVLGFQERELDRFLLLHLSIEDSSVRVRICATLLATVAEEPTNELTNDMETSSAAGESGASADTSVSVADMEYLCLRLKTEIVFAFDLLSLVDFFLCEVVMTCEVNDSCSDCGCGCVSSSIAKILHFEAKDFS', 'MTAVSGVACYGRGHILVPILVLVLLCAVVAYAHFAGIWEAFSAWPFVPLAV', 'MWNLGLFLEAVLWWIWKVRNSTCVGLEPINDGTRHSSLATASYESRLASDELHRSPREPFPFDSQAFLASLATANNPSSPRELRQRDQIEPRTTLVWKMSGTRHGE', 'MQDVQIVIFNVALYVQILVIMANAKEINVVAMRSPLCKINKKRMEIIVNVVARISAELERERQKNVEIMEKYRCSNL', 'MCAKQLFLLLLLLLIHFHILFSIGLVTEKLSELASSNSRSYSVISLIVHDGTKVHLGATMYGLSISAVNLLSLDKQL', 'MARPLLVSNMHTLLLFSSMPTTTILFVVLSVFSIFSIITFLCGSKNMKKLHTEAEEEATVSSTKETKLISKLNSKISTRAISMVRMLSWRKVQAEGGLEEGNQEEEVLWRKNILMGEKCRPMDDEN', 'MDHKPPSIKPLSTHIFTMLCLRLNLFGYVTRPHTQESHTYGRDDKVPIVTQSSKLDASRS', 'MFEIFKFVYVVVIFLSLYILSIEVGGALIECEIDLDCPKSYIKLWDRNYAHRCVNNICEWVKKPRIY', 'METSLKKILKLVILLLAIVSTFVGRTQSTRHDTPIHDIQRSSQKYPTYVGSINDLHRSSKPYPSYPEYHPANWCCNIVIALCCNS', 'MMPLHSLPYFSLSLLSLPYLSCSKHSVNIPVTYHSRIIEYVGSGGKSIKDNLEGIEYQLICTYNPIQHTIISTFADVNIIAAAATNSNKNAADIAS', 'MPFVQGAVSTPATGCCSNLEQLYSQEPHCLCFLFNNTTFTSFPINRTLALQIPTFCNLHVDNSVCPGVKMHVPPSSSPDSQVSFQTRNNSTVAASPLFSVPPRPSIIGFGLGRSEAINLNAKNGIIVIMAITIFMFI', 'MANHFSNLFNILVILATIAAVQLTVEGGQCSEIYDNCSVGNCNDYCKVLYGLNGLGSYCDDFNMCTCLYKKESSTYNCNLGLGACIKGNCDSVCCSARCASKLKHSFGSCTNGNPDRCICHYVPE', 'MNEEKITHISKFAYALIIFLCIYLVVTDGEKNHILGCNMPKELPMCLSAICICKKTPL', 'MAMNAKTMIVFGFGMLLVTFWCMKVTEAHDAAHAGCNSKHSDAGEECTDEDDSLGLYSDVDDTFRTAKKHIKAHIHEGMVGDHDDSPDVAHNNVNVLGH', 'MYVLPELLVFLVLFHQQTTEIIKNHFLLLLSLKSQFLTAQKKTKSPPATLYSDLRGPFQSLQHKRKQNQHQPFLGSDGVVVKIGIGKNKRGQDTGFPMETKNP', 'LVHLQGNKFPYSYGMICIWIFFLCSMGDLVSFCDGVGGVDGPPSPPVLHIPPEPMGAPEFVPAPPEIPILDQPLLPITQREAELYRRFLVNSVGQNPSIERISETVRIQGVIERHIEAALVHSGFSPEDILRNRHFIRGYLFYNNGRALSLRTYRAYLNEIARLGTRDTRPYQRIMAAMRNYEIFL', 'MWIVCRRRFHLLMVWFMVINFLVCFHLCQVEAIRVKFNHGIVDKKGKENLLHKYLYGNSNFGLNHKPFDESKRKVPSCPDPLHN', 'MTFLRFSVLFFFSSILLFSHSVHCDHDEEDDLYQGVNMYRTSLNLTSLTKNENANCFAEKLADQFKNKPCTNTTGANTIPGTEPQFPNYEDLLTKCHLNISNTRDGSVMPACVPGLVPSIVLSNFTQSLYSQNLNDSKFTGIGIGSEDNWIVVVLTTNTSDGSFVPESSSGDETSNGENFITKIGLVYCSLLLLVGNVILF', 'MEALSLCPFCFLLVLLPFDGPKPTPVLLGIESDGSDGFSDLIVKVGENECCSLVVKEVTNCVCSAIFVLLELELNFTT', 'MDTTRCATFFAMLVFLKLLGSLQGLSNSIPNFYSGARLYPPPSPRVSSQVPALAKNCDLLGKILGLVSDHGSIGSDSEALTNFDTSIRQPRRPKQSTEIYDIAVAPSDD', 'MAEIFKFVYKWILFVSLFLVIVAKEDDIECVTDADCYEKLPALQRAVMKCIQGFCKIHI', 'MANVTKFVYIAIYFLSLFFIAKNDATATFCHDDSHCVTKIKCVLPRTPQCRNEACGCYHSNKFR', 'MDTRMNSSVTFIALILLVSLLVSEARPLLSPLQGKEAVIGEVNGVFRTLKGAGPSPGIGHRLKKLQIIGGMKDSGPSPSSGGVGHIKIQNIEGQKFNTVQVIGVIKHSGPSPGEGHKYIISNNHS', 'MTKTLKFVYSIILFITLFLVAKNVDALKKCITFEDCPISKTRVYKCLHGECRYTIPYIPKVPKVK', 'MTVILKFVYIAIFFVSLIFGVVSGKKKCASESDCYTMFPVPHFIVMTCIEKKCHITGIYY', 'MGYSWTPDWTAQLGILLSLHQLFLSVRSRDYTAMKYMLPKQLYQCVCKALPGRVMGINVGTEYLGVAMSDLENKGLEDSRTSLLRLNDKSGKPKSDAVLASELKDLIDRNNVKGMVIGKLDLDELDSRLPSSDVEAAKEFVQKLDKTDMFSDMNYTYFPPRSELILEKFLDGYRGKSSS', 'MVVVVGLMVVCGVVLNGSLGVSVGFRGRSRGGCGGANGGSRLFYDGSFGSRFGSESLVAGGCGGGEAGRWGSGCVSLVDQGQGVPSDLVLKSGGPIAFDSRSGVSSLSLAMALEYECGAGSSCIPEEEISVWIWGFHN', 'MASVKLALAVIFLAAFIIFPMKKVEACSGGLCSVFDKNFPRCEENPDCQCIPWGILYGNCIYLPTKESIVKVVEEHPNLCQSHVECTKKGSGSFCARYPDSKNHIGWCFTSNAEAERYFEVGSNPAINNFFKNMISNSNEKGFLKMPVEIST', 'MQCCVRSFLLYLLGCTLFAGKSNKHMELIFLEAMEDLARMDHWLWGGMTLAHLYHYLHESTLASTKTMCSNITLLMVLTQEYLIHSIPHIGHALADEFLQGTF', 'MIYKFKVPSFQSYFVTIIVLSILLQIMYSMRGPNTWKLIAILLEKSCMQTY', 'MAAPLIIRSPATLTMTLMAAWLVLCSTASRMVGGKTEVANVRTNDEVQELGRFAVEEYNRSVKIWKEGEGELRFVEVVEAQQQVVSGIKYYMKIWVTQAKSDGVGDPTMFDSVVLVKPWLNSKHLLHFAPSPQ', 'MAQSLIFVYALIIFLFLFRVEAEHLKIRCVTDDDCPKVEKPLYMYCGNHWCAYKLHFV', 'MKIKLIVLAIIIALILIIVLSVCHGFSC', 'MKPTKFSSSSSLFIICLCLVSYDVVPALGLKLYENLCSETRKYSQDCLDLLKRDKRIVASENYHELSVYILDFAIKEAKSYEVYLVGKAKKFPNDQAVKICATQLITTTVAAFESSFSDLDKDLQTSIADAVSAGIGADKCDKAIQNEKPEFDPKPIHTRNNEMLLLSVISVLAINHLT', 'MASTTIKFALFLTLTLLFLQVVSCTNIGLDDPEKDDILKWVSSKPSVITIPRKAFVIAIINNVESHEIVIDLRLKSIVSDNIHKGNGFPTLSVDEQTVAVGLPLKYPPFIASMKKRGLNLSEIVEYHDRVIEAVPTDENTEFRASDKKF', 'MNSKALLFLAMLLATILLICAESAPKDLDNKDEKIDTNGYYGGGGYGGPWRGGGYGGWGGGYGGRGGYGGWRGGGWRGGWGGGGWRGGYGGGHGGWGGGWPNEHHDAYTDAEPHN', 'MGSLYFKFILVICIYTTTNALIYDDIIVSHQWSPAVCRESICLRPPTAAFTLHDIWPGAKKGVVSQSNCWRLPQSSPFDKNVVSLFEPFLKEGKGRILKF', 'MVFISMVIALNCKQHAYGEGEVDWFGCTSVSCIEEDYHNTDHDSYWEGDDDDDGGYDYAPAA', 'MAPCKARADCSKLMCELPKISWCFHGYCECVQV', 'MDAVLKFVYTMILYLFLLHVIAEDFPFHKCEKDEDCLEICADDQMAMCILNVCFCY', 'MKLIIVMFIWSFYVQSMQFGFALKVAFLVIIKCLRWFAGTCISKNFLANFDGQVPKSTVDWFVRTVILRPKIQKCNS', 'MINVIEIKLTIEPCVVLHILTMDGKSSIGLLFLLLSLASLVVKCYSSSEDDLKAIALKLSLSNLCDSSFL', 'MAAEAISMFVLSIFSSLIGVDEAAVVATMEAKAATCAAEHYVFCFPLSFNHLLTVLGPEQVVLLPTAAVALPPLDNG', 'MAPYLTLLFLFTTIISATTAATGYKNHTVGGTSGWLFNSSTNTSATNYSSWASSQSFNLGDYLIFNTNSNQSVILTYNKTAYTSCTADDSDVGTFIYNGGTNNFSETLTIPVPLTIVGPNYFFSDTNEGVQCQHGLAFQIDVQRGIGLPPSLNQPPPPPYAEPPGADSVDSPPITIPQTPSGGAFARRVDVRVVVYGFCVAMLLVKFW', 'MGLNFMILVVMVSWFWSFPTTCALAAKQQENLKLRFDQNGEFKILQVADMHYANGKTTLCLDVFPSQNASCNRSNNY', 'MFGGFGRRKFLSFPLVIGAVIIGVVSGKSIFGPPLDDYWKKKRLEEEAAAAASAIAKENDSTST', 'MSLLTQKFLLLCVLTLLSAHDVFAAHSDSFIRRTQHVNVTNYLSDNLDLTLHCKSADDDLGVHLLHHGGNVRWGFGLSVFGETLFFCSFKWNDELHRFDIYRQDRDQDVCRSCNWYIMKSGPCRFNGDKSDLCFSWNNN', 'MKMIQIVLFFFALISFTTNAIDSDDFCVANLFLTSDTPSGYPCKSEKFVTADDFMYSSLAAAGNTANPFKVGLTSVNVSNLAGLNGHKLSAARIDIGINGSVPMHTHPDATELLIVIQGQITVGFITPTKLLVKILNPGDVWVFPIGLLHFQFNSGDGNAIAYAAFSSSNPSIHVISPLLFGNNLPTSTIQKTTLLDAAPIMMLKGVFGGSG', 'MANHFSNWFNLLLILATIAMLELTVEGNTCSDIFARCDDIDCRSHCKSLHGAKSFGFICDDFNLCTCFFKG', 'MVLIINKSLIIFFLQFIHYTGGVRSRRSFLQLIWLLTVWTIWNERNARLFKQKESSLDQLLDKVKFYSLWWLKAKKACFVFGDHMWWSSPMSCLGIG', 'MKVIFLGLVLLLASCSQAIANKDDDFIMNFTSKERSLIHSNEPQAHELNGNLQFNGSIEVSTRFNHKVVLLESQNEHKDELSDTTRKVRGGKRGGRGKGRSSGNTHYYHHHNHTANSASATTVSARTHFGVSTFFLCMSFWL', 'MIPPRANVYAMIIFLFIFFVAMLVKVSHSHCVIDAHCPRNMCGFHFPPRCVEGDCVC', 'MTYVKLATLAVFMLTTFLIVQMKNVEARHCARYGTKLRPLR', 'MVMLKGRALSQLFIFLCSEGAQSCFVGDGRRTRGTTISNTTRDIKRRARPLTPDFSSIRRRVPLMPWHDTGECLHLKDAIEELLNKGKLKHFTKDDFQKRDRKK', 'MVQNLKFAYALIIVLSLFLVEFDWSKSYFHTIFKFSCITFY', 'MDTTWTSALKIMLLIFFVIASDMCKESEERGPEVEQCDDEQCQKICYCLPKCPKEMYGKCVDGKCSCYTAPPSVGGVPSLVP', 'MTTSLKFVYVAILFLSLLLVVMGGIRRFECRQDSDCPSYFCEKLTVPKCFWSKCYCK', 'MVLLGIFALLAILALVLFLKLMFSWWISPIQRYRKFQRCGFQGPPPHFPFGNIKEMKKKNGVDSSFESSMLTHDIHSTVFPYFSRWQKSHGN', 'MVSFLDFFLSPTLATKVRRLVSRTDKPNPKPQPSKSPLTIADRDFPRGHNY', 'MAVYATSGYGSMLWALWLLSWQFIHSRDASLKVLENPKSPLNFWVAMVGIGLV', 'MALWFVLILTLLAVGESQSTTAAFNQTDLQTAMSDMQSRSYYGFVILLKILNSLPNQMQNNDLTFLMPNDEDLSHFSIAPEELHDFLLSHSIPTPLLLNHLLHFPNGSIVPSGLPSKVISITNNARAGLFVNNARIVTPNVCQNSLIKCHGISAALTFENSLPFQRVPEPKDPSKNSNDTHPNPSMRKMSIKA', 'MWVHWCILDHCYLVGIFVFAWNQISVEVAFVLLYKEIIKRDFDYAINDLPFGSLQLITYDLSSFIHTFFSRLNITYNLCYGNGTGKKLRPFCSYLQHCWNQFTKPKINNASILDLCLE', 'MSNFQNKIASCIKPILLTFIVICIASVMVAGDVVFLPGKLCFVSTYCPTAPELCDNFCKVSLGNPKGGLCFKGQGVCCFN', 'MDLLLLRKTLLALFIAATIAIIISIICGKSFKLPPGPFPVPIYIYIFGNLLQVGDDLNHRNLTNYAKCMVTVLVRASTLETPIF', 'MVQIVKFVYVMIIVLSSFVVAINSDGYLECTTDYDCREEWLCPPDMEAKCFVSFALARFLSKGKCLCV', 'MNPNIRLFYALIIFLSLLLILTDGGLINGGSVPCLTSFGCPRSTCYPPSTPNCIRRICECI', 'MKTKLFVSVCFYALLLIFLVAIMPSEGAIWNGNKGKNNDGYGDGIEGHSGKEYVDNAWKKERSWGTGWWGSGRKGGGGGKGDKGGKVGGGRYQLPIPGGGKGGGGKGGRDGKL', 'MTRTTILKFLTIVALIAYSFQENIIKTLDKLYKNYFYVGGSMKEIDQSESDCCSVCLSQMCRGEKVRVLPLCDHRYHADCIGAWLKNNTTCPLCRSKISNHSYNLNLQKLVNPFEESLVDLIQSFSDALVTILYKILQSSITESEGFHVVY', 'MTNIIALSLSLSLSLSLSLSRQHYWAWFVDTNGGCSDSKNLIAGQTIISKMVSEPLQDPLYHLISGFRYRVTNYLSPRTKSNSASREGVC', 'MFLATTITIFIIIITRIMALHYGGSFTFTSVVSNPPGSDKFIQPIVTGTDHATTHWNIIIRFLLRNSNSNPLRVLIHIRVV', 'MSYLLSVLILLLAVITLTTVAGNINPKYQIHDDESARSNGFINYQPPQKPVGLNYSPPPPPRHVAPRSKPSEIYIPGVPHLQARRSRRPPPKM', 'MDKTLKYMYTLISFISLFFIAKNDAVYIKCKTDADCPKSESTIFAMKCNNYRCIYDYIHKRNSYAT', 'MASSMLFKVTCLAVICLVFGIPLANADLSCGQVLSTLYPCLGYIRNPGASVPAPCCNGIRIVNDEAKNTSDRQSVCRCLKSTIVLPGINLDALANLPTNCGVNLPYKITPDIDCNKIPY', 'MDTTRLIPLQLALLVVSSILVSDIFIEIVAGRINAEILSQNAQFNPTPECPYCETFKQCLHCCDSYRFDGYPACRDFAPLRPHCICDIRSTVPKNISSLTPTIAN', 'MWSIQRWPILLKFLLLLLFWHLVRSKFITWQLMWLQWLMINPICLEDLICLPPNAAAILGNIGPIVFCSNVSNDMELDRYIGSIP', 'MVKTLKFVYDMILFIFLYLVAKNVAESIECRTVADCPKLISSKFVIKCIEKRCVAQFFD', 'MASVRLFLLAILIIVSLSSIDNVQGGGTRKLLTQTFPDLGKIPGLEFPPFPPVTEWPEYRLPPPIFNIPDFFSPPYATTTATTKP', 'MRNLSIIFSNLSLFLFYITEECVTDADCENLYYGNKWPLICSNIGYCLSSYEGKIYLSSISKTFIFFTCYNVVTPIICLKLTYISSILKFM', 'MIPQQGVQSQVPNQYHFYPIFFLVLTIFILLFRLTNLIYIFSFEGGFGKCVRDADCVDEVCSPGCNKRCVGFECQCPL', 'MCCRPRWFWCFYCTIYVVIFFFMITAVIFWIVISPSSVKFHITDATLTEFNITNNNTLYYNLKVNVTVRNPNNNIIVYYRRITAIAWYKDNGFSYVSLTPFDQGHKNTTFLGPIEFKGNTKIKLGRQQLDEYFEERRLGIHNDLAVDFDVRIRAKFGSFYKSGRFNPPVVQCRRLSVPLVSSSNGNSSSSPSSSTFSFSDRRCSSGSFFTDRDADAGA', 'MTRQTLMKIFFVALKLGLPMPPSYFGVSTTERYQITSCINYASGSCGILNSTRNVRNYFTLLYIIQFKNIGH', 'MEYGLNGLVMALIGSLFSMLVVSNEITILLRLSYLYDKIIDIYSPSSSSINRIVGLRQGIRVSGLEFGVKYLIKGSISVVNVYDVQIIKASFLAQLYNRKTVEQLTEITEKTNSIYSVTCATSKIESRGPSPNENPSPYGNLSRDRNCTRTSPPPLKFLKHAYTIKTTYAERAFSKPAYAKSKSKETTL', 'MSSKRFFLIWGVLFLCVLSFSTKPLARNIPNVSEFYSGANEMGPNKRFLGQENKRPPCKRGIVTYAVRQDDPRCHHYL', 'MLRDMLIVKLMLIVHPVLELKGLSALKENVDGPECRTHSMSLILKIVQLHHALKNEIFHWKKQICNIHFAKLFYSYQSLYFIYFFNFTDLAVRGVHGLGKPKKPVKPTQKSGLGWVIERIWF', 'MAVTNFNSLASFLFQLQLIPSLLIDMKNTHCEVTVAVVAFIPKTRVVKNWTLRHGKKGRMRYELCGVVCDDEDEGDGGGCGAGF', 'MASYAAVQVFLILGLLATSCFAQAPGAAPTQPPSATPTPPTPAPVATPPTATPPTATPPTATPPPAAAPTPATPAPATSPPAPTPTSDAPTPDSTSSSPPAPGPGGPAPGPGSTDAPPSPSAAFSINKPIMAATALSAAIFAIAF', 'MTLSTASKFYIIFMFHCLLLISTWEVEATLCGRKSRTLSGRCTSNSDCSTKCIKWEYATYGSCGGFYIDCICFFNC', 'MAFSYNNKRILFTVLVIIFFILVVSSQLGSACRPLQDEQISKEFDGLLLQFLPRGTPSKPSTPSPIHP', 'MYNLSLFTWLTNGFCELTLDVNVVVSVMVDECGNQWECSSLFGRRPFLHFIIGGGFNRLVMARRLCEGSHVMRKRKLQSTSSSCSWSPSAMNQVVRRPFSLSNIT', 'MENEGKERKRRFIYTEKDIASHFNSHLWMNLSLWFAWVGILPWCIRQVCAHNSQFQERRVMIKNESQLIASTLWSIWKRRNLRVWQNTTETSQAVFERAKYLLFNWQAANSKKQQGAAAAAAVARPISGYLAENDVQPRLSWQVEM', 'MQWKKGGICLMIGVILKYSTVTYPVLWLVSYQYGLPFQKRRVWDSFRISTELGGDSLRNGRYSVGYHYYLRNELEWKSLRVTSASVERKTMNSWQQWYEVNRLQQTRAGTVRNQSSSD', 'MHIIFFFPSYLVLLFFILLWIHDIIPYFVQGNVVLVFQPAEEGGPAEEGGVGAKKILDPGILENVSRHVVGGGHGAIPHHVLNPILAASNVTISLQQIVSREVDPIDSQVKAYNS', 'MHSTHCSWFQALFFLALVWFFNCGSVHGVSTTVKVGNFSKVEDAVNFHIYYGQTFRVIKNAVDGQSYLLLQNNSRIASRTKYCTSRIKSFFIPLSNFSADTDSFPGIPCIISSMWQS', 'MVCLFCLLPLFLVPIVNVLPILFYYLMGKVYRLFGWEYRKPEIAPPACPYKPAAQKDSKVEAEAAPAPVEAVKAGGVDVKQD', 'MAQILMFVSVLIIFLSLFLADTKQTNIPCENKRDCPQPLYPKFVTCFEGLCRMHYPLKKI', 'MAYIRFAHLVVFLLAAFSLVPTKKVGATDCSGACSPFEMPPCRSSDCRCIPIGLVAGYCTYPSSPTVMKMVEEHPNLCQSHADCTKKESGSFCARYPNPDIEHGWCFSSNFEAYDVFFNVSSNRGLIKDFLPMFTLTLDS', 'IFFFLSLPLNLAATAVRKSDSGAESSPTVHSRNPLSSLDSDYVAIAVATSTESVSLGSISTSGSLICNFVAPSSLNVTNPIFVSQSPLQSEN', 'MRVRFLTCFIAVLCTQAISILGFSSEHRALNSSNTSIQTKQPTSNVGNYGSIKEGIKLHREDKKYIKEEKFERAYDSIKKARGKGANGGANDNRSEPRRSRSSAPSISMLSWISTVCVSFVLTFHAKLL', 'MKICFKILSVIFVSLVINIAAAGVATTAAVNKQYEHGSQRDGDTDMSSYETKYADRENNTLGSNSEGERKFDRSENNDSTKSGSALDAVEYDNLWEKITMDKLDFVFSYTLGSIGGGIRISVGMSQIWVFNRAHRR', 'MRFQFIFLILVVLFVSLASNQAKRPIQSSPVNINDPYVINITTFAVIEYNKQNTKAKLVFEKLLNGVIDTLNDGINFRLTLSANNGSTSNKYGAIVLEKAYKHFRKLTAFAPVQHA', 'MGKKLRMMKNVRFLIMLHLLSTNVLPSRSTAPFSFHHSSLQNQPHCPPLFFPLTSCVITIPRKKMGLRHGWTVVGVGGGGGVNGDYLVGGGCFMVKMREGELVLLWLGSVLYAMLKNYETEIPPLSLGSN', 'MEEENTLEETVYNWDYTSAFAAVLLLLIATAVVRCICCTTDLRFCCCSYSLLQSASSSPSISLQTHCVTVPSKLFSLSVLKVCLFPVTVIVVEFVAGGDETSNITTRCSAYFGNVSFSFTHTKALEPSFSIILSAIFIGELKVDKVKLIEVFGTYQSEFSSVIVVYVQ']\n"
     ]
    }
   ],
   "source": [
    "test = []\n",
    "for _, row in df.iterrows():\n",
    "    if row['label'] == 1:\n",
    "        test.append(row['seq'])\n",
    "\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mtzD2T4J0Bli",
    "outputId": "821e52a9-790b-4ec1-d6d0-b7ddeb4271aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original text: tensor([[13,  1, 17, 21,  1,  2, 12, 14, 11,  1, 10, 14, 11, 14, 11, 19, 10,  1,\n",
      "         22, 14, 14, 14, 17, 19, 11,  7, 22,  6,  1, 12, 22,  5, 11,  2, 21, 17,\n",
      "         12, 21, 14,  6,  8, 12,  5, 14,  4, 15,  1,  8,  5, 12,  3,  3,  5, 19,\n",
      "          6, 13,  7, 12,  1, 10, 17,  8,  4,  5,  9, 15,  4,  9,  8, 14,  8, 17,\n",
      "          1,  5, 11,  5, 11, 14,  3,  5]])\n",
      "baseline text: tensor([[13, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
      "         23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
      "         23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
      "         23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,\n",
      "         23, 23, 23, 23, 23, 23, 23, 23]])\n",
      "all tokens: ['M', 'A', 'S', 'Y', 'A', 'R', 'K', 'F', 'L', 'A', 'I', 'F', 'L', 'F', 'L', 'T', 'I', 'A', 'V', 'F', 'F', 'F', 'S', 'T', 'L', 'E', 'V', 'Q', 'A', 'K', 'V', 'C', 'L', 'R', 'Y', 'S', 'K', 'Y', 'F', 'Q', 'G', 'K', 'C', 'F', 'D', 'P', 'A', 'G', 'C', 'K', 'N', 'N', 'C', 'T', 'Q', 'M', 'E', 'K', 'A', 'I', 'S', 'G', 'D', 'C', 'H', 'P', 'D', 'H', 'G', 'F', 'G', 'S', 'A', 'C', 'L', 'C', 'L', 'F', 'N', 'C']\n"
     ]
    }
   ],
   "source": [
    "text = 'MASYARKFLAIFLFLTIAVFFFSTLEVQAKVCLRYSKYFQGKCFDPAGCKNNCTQMEKAISGDCHPDHGFGSACLCLFNC'\n",
    "\n",
    "input_ids, baseline_input_ids, all_tokens = construct_input_and_baseline(text)\n",
    "\n",
    "print(f'original text: {input_ids}')\n",
    "print(f'baseline text: {baseline_input_ids}')\n",
    "print(f'all tokens: {all_tokens}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "m2zoyXmb0Bqa"
   },
   "outputs": [],
   "source": [
    "desired_length = 299\n",
    "padded_sequences = [seq[:desired_length] if len(seq) >= desired_length else torch.cat((seq, torch.zeros(desired_length - len(seq)))) for seq in input_ids]\n",
    "# Apply pad_sequence on the padded_sequences\n",
    "    #data = pad_sequence(padded_sequences, batch_first=True)\n",
    "    \n",
    "input_ids = rnn_utils.pad_sequence(padded_sequences, batch_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "ErPbJYQG0Bvy"
   },
   "outputs": [],
   "source": [
    "desired_length = 299\n",
    "padded_sequences = [seq[:desired_length] if len(seq) >= desired_length else torch.cat((seq, torch.zeros(desired_length - len(seq)))) for seq in baseline_input_ids]\n",
    "# Apply pad_sequence on the padded_sequences\n",
    "    #data = pad_sequence(padded_sequences, batch_first=True)\n",
    "    \n",
    "baseline_input_ids = rnn_utils.pad_sequence(padded_sequences, batch_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y2X4kyKP2WFq",
    "outputId": "4ca7a872-a891-4c4f-b3ec-9f082b7391c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original text: tensor([[13.,  1., 17., 21.,  1.,  2., 12., 14., 11.,  1., 10., 14., 11., 14.,\n",
      "         11., 19., 10.,  1., 22., 14., 14., 14., 17., 19., 11.,  7., 22.,  6.,\n",
      "          1., 12., 22.,  5., 11.,  2., 21., 17., 12., 21., 14.,  6.,  8., 12.,\n",
      "          5., 14.,  4., 15.,  1.,  8.,  5., 12.,  3.,  3.,  5., 19.,  6., 13.,\n",
      "          7., 12.,  1., 10., 17.,  8.,  4.,  5.,  9., 15.,  4.,  9.,  8., 14.,\n",
      "          8., 17.,  1.,  5., 11.,  5., 11., 14.,  3.,  5.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.]])\n",
      "baseline text: tensor([[13., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n",
      "         23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n",
      "         23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n",
      "         23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n",
      "         23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,\n",
      "         23., 23., 23., 23., 23., 23., 23., 23., 23., 23.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "          0.,  0.,  0.,  0.,  0.]])\n",
      "all tokens: ['M', 'A', 'S', 'Y', 'A', 'R', 'K', 'F', 'L', 'A', 'I', 'F', 'L', 'F', 'L', 'T', 'I', 'A', 'V', 'F', 'F', 'F', 'S', 'T', 'L', 'E', 'V', 'Q', 'A', 'K', 'V', 'C', 'L', 'R', 'Y', 'S', 'K', 'Y', 'F', 'Q', 'G', 'K', 'C', 'F', 'D', 'P', 'A', 'G', 'C', 'K', 'N', 'N', 'C', 'T', 'Q', 'M', 'E', 'K', 'A', 'I', 'S', 'G', 'D', 'C', 'H', 'P', 'D', 'H', 'G', 'F', 'G', 'S', 'A', 'C', 'L', 'C', 'L', 'F', 'N', 'C']\n"
     ]
    }
   ],
   "source": [
    "print(f'original text: {input_ids}')\n",
    "print(f'baseline text: {baseline_input_ids}')\n",
    "print(f'all tokens: {all_tokens}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "wSsagvTH8sCx"
   },
   "outputs": [],
   "source": [
    "from captum.attr import LayerIntegratedGradients\n",
    "\n",
    "lig = LayerIntegratedGradients(model, model.embedding_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "K4PISaV528jO"
   },
   "outputs": [],
   "source": [
    "attributions, delta = lig.attribute(inputs= input_ids,\n",
    "                                    baselines= baseline_input_ids,\n",
    "                                    target = model_output(input_ids),\n",
    "                                    return_convergence_delta=True,\n",
    "                                    internal_batch_size=1,\n",
    "                                    n_steps = 1000,\n",
    "                                    method = 'gausslegendre'\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XxquPNUK9HVl",
    "outputId": "859b20de-ddcf-4da9-f738-bb282ca676da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 299, 512])\n",
      "torch.Size([299])\n"
     ]
    }
   ],
   "source": [
    "def summarize_attributions(attributions):\n",
    "\n",
    "    attributions = attributions.sum(dim=-1).squeeze(0)\n",
    "    attributions = attributions / torch.norm(attributions)\n",
    "    \n",
    "    return attributions\n",
    "\n",
    "attributions_sum = summarize_attributions(attributions)\n",
    "\n",
    "print(attributions.size())\n",
    "print(attributions_sum.size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "a8ymZQJ-9Yjm"
   },
   "outputs": [],
   "source": [
    "attributions_list = attributions_sum.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "qT4m7wdb9kyI"
   },
   "outputs": [],
   "source": [
    "aa_dict = {'A': 1, 'R': 2, 'N': 3, 'D': 4, 'C': 5, 'Q': 6, 'E': 7, 'G': 8, 'H': 9, 'I': 10,\n",
    "               'L': 11, 'K': 12, 'M': 13, 'F': 14, 'P': 15, 'O': 16, 'S': 17, 'U': 18, 'T': 19,\n",
    "               'W': 20, 'Y': 21, 'V': 22, 'X': 23, 'PAD': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "SM2OkKGC9mzF"
   },
   "outputs": [],
   "source": [
    "def generate_text_from_tokens(input_ids, aa_dict):\n",
    "    text = []\n",
    "    for token in input_ids:\n",
    "        if token.item() in aa_dict.values():\n",
    "            aminoacido = list(aa_dict.keys())[list(aa_dict.values()).index(token.item())]\n",
    "            text.append(aminoacido)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qfkb4XAR9o55",
    "outputId": "bbdfc4d5-3759-4705-bd17-396e12e88754"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M', 'A', 'S', 'Y', 'A', 'R', 'K', 'F', 'L', 'A', 'I', 'F', 'L', 'F', 'L', 'T', 'I', 'A', 'V', 'F', 'F', 'F', 'S', 'T', 'L', 'E', 'V', 'Q', 'A', 'K', 'V', 'C', 'L', 'R', 'Y', 'S', 'K', 'Y', 'F', 'Q', 'G', 'K', 'C', 'F', 'D', 'P', 'A', 'G', 'C', 'K', 'N', 'N', 'C', 'T', 'Q', 'M', 'E', 'K', 'A', 'I', 'S', 'G', 'D', 'C', 'H', 'P', 'D', 'H', 'G', 'F', 'G', 'S', 'A', 'C', 'L', 'C', 'L', 'F', 'N', 'C', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD', 'PAD']\n"
     ]
    }
   ],
   "source": [
    "generated_text = generate_text_from_tokens(input_ids[0], aa_dict)\n",
    "print(generated_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "sBAW5QLK9rPn"
   },
   "outputs": [],
   "source": [
    "attributions_sum = attributions_sum.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 58,
     "metadata": {},
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   ],
   "source": [
    "attributions_sum"
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  },
  {
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   "metadata": {
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    "outputId": "ac6435d3-1434-4c9e-8e56-901fe53c6eb8"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt                #non tiene conto della posizione relativa, fa una somma dell'importanza sugli amminoacidi uguali\n",
    "\n",
    "# Etichette degli amminoacidi\n",
    "\n",
    "\n",
    "# Creazione del grafico a barre\n",
    "plt.bar(generated_text, attributions_sum)\n",
    "plt.xlabel('Amminoacidi')\n",
    "plt.ylabel('Importanza')\n",
    "plt.title('Importanza degli Amminoacidi')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "id": "qLap4YK1AEtv",
    "outputId": "f934bc70-9cd6-460a-a2cb-88777b263d2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 14950x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(generated_text))\n",
    "\n",
    "fig = plt.figure(figsize=(len(generated_text) / 2, 6))\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "plt.plot(x, attributions_sum, marker='o')\n",
    "\n",
    "plt.xticks(x, generated_text)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.xlabel('Amminoacidi')\n",
    "plt.ylabel('Importanza')\n",
    "plt.title('Importanza degli Amminoacidi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "f2sH_zFoCovZ"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "from termcolor import colored    #non funziona su colab, vedi dopo\n",
    "\n",
    "# Rappresentazione testuale del testo generato con colori\n",
    "for i in range(len(generated_text)):\n",
    "    aminoacid = generated_text[i]\n",
    "    score = attributions_sum[i]\n",
    "    if score > 0:\n",
    "        colored_aminoacid = colored(aminoacid, 'green')\n",
    "    else:\n",
    "        colored_aminoacid = colored(aminoacid, 'red')\n",
    "    print(colored_aminoacid, end=' ')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "id": "zAO2AkanDTi6",
    "outputId": "f3193f40-7e9a-42a6-a330-3e45c23f017e"
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "M <font color=\"green\">A</font> <font color=\"green\">S</font> <font color=\"red\">Y</font> <font color=\"green\">A</font> <font color=\"red\">R</font> <font color=\"red\">K</font> <font color=\"green\">F</font> <font color=\"green\">L</font> <font color=\"green\">A</font> <font color=\"green\">I</font> <font color=\"green\">F</font> <font color=\"green\">L</font> <font color=\"green\">F</font> <font color=\"green\">L</font> <font color=\"green\">T</font> <font color=\"green\">I</font> <font color=\"green\">A</font> <font color=\"green\">V</font> <font color=\"green\">F</font> <font color=\"green\">F</font> <font color=\"green\">F</font> <font color=\"red\">S</font> <font color=\"green\">T</font> <font color=\"green\">L</font> <font color=\"red\">E</font> <font color=\"green\">V</font> <font color=\"red\">Q</font> <font color=\"red\">A</font> <font color=\"red\">K</font> <font color=\"red\">V</font> <font color=\"red\">C</font> <font color=\"green\">L</font> <font color=\"red\">R</font> <font color=\"red\">Y</font> <font color=\"red\">S</font> <font color=\"red\">K</font> <font color=\"green\">Y</font> <font color=\"green\">F</font> <font color=\"green\">Q</font> <font color=\"red\">G</font> <font color=\"red\">K</font> <font color=\"red\">C</font> <font color=\"green\">F</font> <font color=\"green\">D</font> <font color=\"red\">P</font> <font color=\"red\">A</font> <font color=\"red\">G</font> <font color=\"green\">C</font> <font color=\"red\">K</font> <font color=\"green\">N</font> <font color=\"green\">N</font> <font color=\"green\">C</font> <font color=\"green\">T</font> <font color=\"red\">Q</font> <font color=\"red\">M</font> <font color=\"green\">E</font> <font color=\"red\">K</font> <font color=\"red\">A</font> <font color=\"green\">I</font> <font color=\"red\">S</font> <font color=\"red\">G</font> <font color=\"red\">D</font> <font color=\"red\">C</font> <font color=\"red\">H</font> <font color=\"red\">P</font> <font color=\"red\">D</font> <font color=\"red\">H</font> <font color=\"red\">G</font> <font color=\"green\">F</font> <font color=\"red\">G</font> <font color=\"red\">S</font> <font color=\"red\">A</font> <font color=\"red\">C</font> <font color=\"red\">L</font> <font color=\"red\">C</font> <font color=\"red\">L</font> <font color=\"red\">F</font> <font color=\"green\">N</font> <font color=\"red\">C</font> PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD PAD "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "# Rappresentazione testuale del testo generato con colori\n",
    "formatted_text = \"\"\n",
    "for i in range(len(generated_text)):\n",
    "    aminoacid = generated_text[i]\n",
    "    score = attributions_sum[i]\n",
    "    if score > 0:\n",
    "        if score > 0.5:\n",
    "            colored_aminoacid = f'<font color=\"darkgreen\">{aminoacid}</font>'\n",
    "        else:\n",
    "            colored_aminoacid = f'<font color=\"green\">{aminoacid}</font>'\n",
    "    elif score < 0:\n",
    "        colored_aminoacid = f'<font color=\"red\">{aminoacid}</font>'\n",
    "    else:\n",
    "        colored_aminoacid = aminoacid\n",
    "    formatted_text += colored_aminoacid + \" \"\n",
    "\n",
    "display(Markdown(formatted_text))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157
    },
    "id": "PihpQquCElw5",
    "outputId": "a5444f75-4b65-428d-c19a-b42cb41b2ef1"
   },
   "outputs": [
    {
     "data": {
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padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div><div style=\"display: inline-block; padding: 3px; background-color: rgb(0,0,0);\">PAD</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display, HTML\n",
    "\n",
    "\n",
    "# Rappresentazione testuale del testo generato con colori e box\n",
    "formatted_text = \"\"\n",
    "for i in range(len(generated_text)):\n",
    "    aminoacid = generated_text[i]\n",
    "    score = attributions_sum[i]\n",
    "    \n",
    "    # Calcolo dei valori RGB per gradazioni di colore\n",
    "    if score > 0:\n",
    "        r = 0\n",
    "        g = int(score * 255)\n",
    "        b = 0\n",
    "    elif score < 0:\n",
    "        r = int(abs(score) * 255)\n",
    "        g = 0\n",
    "        b = 0\n",
    "    else:\n",
    "        r = 0\n",
    "        g = 0\n",
    "        b = 0\n",
    "    \n",
    "    # Creazione del box con colore e testo\n",
    "    colored_box = f'<div style=\"display: inline-block; padding: 3px; background-color: rgb({r},{g},{b});\">{aminoacid}</div>'\n",
    "    \n",
    "    formatted_text += colored_box\n",
    "\n",
    "display(HTML(formatted_text))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570
    },
    "id": "HuJ0omK7JJmh",
    "outputId": "12ad55d8-6d2d-450e-90b0-6b5275fdfdbf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q+2jevLmio6NNo/TCw8O9z7OysnTw4EH16tVLhmH4/Yy4PBdffLF3dJQk7yikESNGmOZ2jI2NVX5+vvbt21dqDkWjdPv06aPs7Gxt377dtJ2QkBCNGTPGWw4LC9OYMWOUnp6uTZs2VTjfQPXt21ft2rXzlotG5w0ZMkSRkZHe+qL937lzp2n9iuT/f//3f2rRooWGDx/uXS40NFQTJ05UVlaWvv/+e1ObQ4YM8Y7UqojKnvOikY9FzjzzTL+fAhdJS0vTbbfdpi5duuihhx4y7WfRaEm3261Dhw4pIiJCHTt2PO519sUXX0iS6SfJkvxG8hqGoU8++URxcXEyDMN07Q8YMEBHjx71HuMvv/xSrVq1Unx8vHf9evXqafTo0eXmUhnBwcHeuRA9Ho8yMjJUUFCgbt26lbrPVXkNSVKnTp3Uq1cvb7no2jz33HN18skn+9WXvDZL069fP9OI0S5duigyMtK7rtvt1tdff63Bgwerffv23uVatmyp4cOH68cff1RmZqakil8Dn3zyiZo0aaIJEyb45VPWjXvcbre++uorDR482LSvp556qgYMGHDc/SzP0KFD1ahRI2+5T58+knzHLyMjQ+vWrdMll1ziff86ePCgDh06pAEDBig5OdnvXB1P8dfo4cOHdfToUZ155pnHfa0UvQd99dVXpp/RF1fRz4dffvlF6enpGjt2rGlOz5EjR5qu08rYvHmzkpOTdemll+rQoUPebWdlZalv3776/vvv/eaQHjt2rKncp08fZWRkeK+r4rKysnTrrbeqYcOGmjNnjvcmaJV9LZamZcuWppHIkZGRuvzyy/Xrr78qLS3NtOzo0aNNN2D75ptvlJ+fr0mTJplGjV911VWKjIz0vseVZ82aNTr11FN1yimnmM7bueeeK0nljr7/5JNP1KVLl1JHUhe9pqw4RgAAwD5MEQAAFoqNjVX37t396hs1aqRDhw55y8nJyTIMQ0OGDCm1neKdNX/++afmzp2rzz77TIcPHzYtV9ofsGUp+XPRoj/Ay6o/fPiwt0Pm999/1z//+U+tW7fOb5sl5w1s2bKl3w1DOnToIKlwTs2ePXuWml9GRoZpKoXw8PCAOglK7k9Rh0br1q1N9UVtF59fr6L57969W9HR0X4/3z711FMlFZ6z4op3+FZEZc55vXr1/DpvGzVq5LeeVHiDrdtvv10ej0fz5s0zdcp4PB69+uqrev3117Vr1y7THK6NGzcuN9/du3crKCjI76fhp5xyiql88OBBHTlyRMuXL9fy5ctLbatoio3du3crKirKr8OurJ+fB2rlypVauHChduzYYbr+SjtnVXkNlbdcyWuz6JoteW2WprSfgTdq1Mi77sGDB5Wdna2OHTv6LXfqqafK4/Foz549Ou200yp8DaSmpqpjx46VuunWwYMHlZOTo+joaL9Yx44dK9SBVZaSx6Cos7XoGKSmpsowDD377LN69tlnS20jPT39uNNgFPf555/rhRde0ObNm03zax7vzvDt27fXlClT9Morr+iDDz5Qnz59FBcXpxEjRnivh4p+PhS9z5Q8pqGhoabrrjKSk5MlyW/ql+KOHj1q6tAu3mEuyTt9weHDh03/qSVJDzzwgFJTU7Vs2TLvFBZFKvNaLE10dLTf8S/+3t2iRYsy2yw6liXfs8LCwtS+fXvt3r37uNtPSUnRtm3b/KZDKJKenl7muqmpqWWe7+KqeowAAIB96GAFgBrg8Xjkcrn08ssvm0bRFCnq4HO73ZoyZYoOHz6s6667TqeccooiIiK0b98+zZgxo1J3oy9tO5LKnOPRMAxJhZ0UEyZMUGRkpG677TZFRUWpXr162rRpk55++ulK5VCehIQEfffdd97yyJEj9fjjj1e6nbL2s6z6ov20U/HRbsdT2XNe1n6V5sknn9SGDRv0yiuv+HXqvfjii3r22Wc1atQoTZ8+XY0aNVJQUJAee+wxy45RUe4jRozQyJEjS12m5GhvO7333nuaMWOGBg8erKlTp6pZs2YKDg7WSy+9VOro0UBfQ8dbvyrXppXXdXVcA3Y43jEouu6uvfZaDRw4sNRlK9Nx/8MPP+imm27SWWedpb/97W9q0aKFQkNDtWLFCq1ateq468+YMUMjR45UUlKSvv76az3yyCN66aWX9Oabb6p169YV/nyojLI6fku+nxQds7vvvrvMOT1Lbr+i1//ixYu1atUqPfXUU35tV/a1WFX16tWzvE2Px6OYmBjv3KwllXzPrazqPkYAAKBy6GAFgBoQFRUlwzDUrl27UkeWFdm6dauSk5P1xBNP6PLLL/fWf/31137LHm/kVKC+++47ZWRkaN68eTrrrLO89WX9BH3//v3Kysoy/RFeNCqqbdu2ZW7nnnvuMY3Ya9myZRUzD0xF8m/btq22bNkij8dj6lwomi6h5Iiu0pR1vipzzivjww8/1OLFizVz5kydffbZfvGPP/5Y55xzjh577DFT/ZEjR/xGmpXUtm1beTwepaammkaAlZw+omnTpmrQoIE8Ho/69et33Db/+OMPGYZhOlbF71JfVR9//LHat2+vefPmmbZR8kZitVnTpk1Vv3597dixwy+2fft2BQUFeUeAVvQaiIqK0s8//6z8/HzvTaQqkkd4eLhSUlL8YqXlZqWi0ZyhoaHHve4q4uOPP1a9evW0YMEC0yjwFStWVLiNojvQ33zzzfrpp5909dVX64033tAdd9xR4c+HoveZlJQU06jJ/Px87dq1S126dPHWFY0qLfmLg5IjM4uOVWRkpCXHqsgPP/ygJ598UpMnT9aIESP84la8FlNSUvzeLyry2SP5juX27dtNo3/z8vK0a9cu07Eo6707KipKv/32m/r27Vvpz+OoqCj9/vvv5S5TF96vAACozZiDFQBqwJAhQxQcHKx58+b5jfIxDMM7nUBR513xZQzD0KuvvurXZv369SVV7GfFlVFaDnl5eXr99ddLXb6goMD08++8vDwtX75cTZs2VdeuXcvcTrdu3dSvXz/vo1OnThbtQeVUJP/zzjtPaWlpWr16tWm91157TREREaaO6LKUdb4qc84rauvWrbr//vs1YsQIv3lRiwQHB/tdix999FGF5qY877zzJEmvvfaaqX7x4sV+27jooov08ccfa+vWrX7tFE0PIEkDBgzQvn37lJSU5K3Lzc3Vm2++edx8KqpodGDx/f7555+1YcMGy7ZR04KDg9W/f38lJSWZ/lPkwIEDWrVqlc4880zvz7greg0MGTJEhw4d0tKlS/22V9ZI1+DgYA0YMECffvqpaQqNbdu26auvvgp4/yqiWbNmOvvss7V8+XLt37/fL178uquI4OBguVwu0xQKu3btMl2rZcnMzFRBQYGpLiYmRkFBQd6pBir6+dCtWzc1bdpUy5YtM01TsHLlSr/3laIRusXnh3a73X6vp27duikqKkoLFy7UsWPH/PKv7LGSCv/T6vbbb1fv3r119913l7qMFa/F/fv369///re3nJmZqXfffVenn366aXqA0vTr10+hoaF67bXXTDm8/fbbOnr0qAYNGuStq1+/vl9HtSRdcskl2rdvX6nvUTk5OcrKyipz+0OGDNFvv/1myr9IUT514f0KAIDajBGsAFADoqKidPvtt2vOnDnavXu3Bg8erAYNGmjXrl369NNPNXr0aE2dOlWnnHKKoqKi9MQTT2jfvn2KjIzUxx9/XGonalHn3yOPPKIBAwYoODhYw4YNq3KuvXr1UqNGjTRjxgxNnDhRLpdL7733XpkdKS1bttTLL7+s3bt3q0OHDlq9erU2b96shx9+uMKj3WpSRfIfM2aMli9frhkzZmjTpk1q27atPv74Y/3000+aOXOm37yDpYmKilLDhg21bNkyNWjQQBEREYqNja3UOa+oop+snnXWWXrvvfdMsd69e6t9+/Y6//zz9dxzz+nee+9Vr169tHXrVn3wwQcVmsvx9NNP1/Dhw/X666/r6NGj6tWrl9atW1fqaMW77rpL3377rUaPHq2rrrpKnTp10uHDh7Vp0yatXbvWO03EmDFjtGTJEt11112aNGmSWrRooQ8++MD7014rRmyff/75+uSTT3TLLbfo/PPP165du7Rs2TJ16tSp3M6Q2ub222/XN998o3HjxmncuHEKDg7W8uXLlZeXp7/+9a/e5Sp6DVx++eV69913NXv2bG3cuFFnnnmmsrOztXbtWl199dUaPHhwqXkkJCToyy+/1Pjx43X11VfL7XZryZIl6tSpk7Zs2WLrMfjb3/6mcePG6dJLL9Xo0aPVvn17HThwQBs2bNDevXv1/vvvV7itQYMG6ZVXXtF1112n4cOHKz09Xa+//rqioqKOux/r1q3T3//+d1188cXq0KGD3G633nvvPe9/PkgV/3wIDQ3V7bffrgcffFCTJ0/W0KFDtWvXLr3zzjt+5+y0005Tz5499cwzz+jw4cNq1KiRVq9e7dfZGxQUpEceeUTXX3+9hg8friuuuEKtWrXSvn379O233yoyMlIvvvhihY+VVPiZdPDgQV133XX68MMPTbHOnTurS5culrwWO3TooPvuu0///e9/1axZM61YsULp6emaPXv2cddt2rSppk2bpnnz5um6665TXFycduzYoddff13du3c3jbrt2rWrVq9erdmzZ6t79+6KiIhQXFycLrvsMn300Uf629/+pm+//Va9e/eW2+3W9u3btWbNGs2fP7/UOdolaerUqfr44481ffp0jRo1Sl27dtXhw4f12WefadasWZYdIwAAYB86WAGghtxwww3q0KGDFi1apOeee05S4Rxt/fv3V1xcnKTCn7S++OKL3jn66tWrpwsvvFDjx4/XZZddZmpvyJAhmjhxoj788EO9//77MgzDkg7WJk2a6MUXX9QTTzyhf/7zn2rYsKFGjBihvn37aurUqX7LN2rUSI8//rgeeeQRvfnmm2revLkefPBBS+/+bqeK5B8eHq7XXntNTz/9tFauXKnMzEx17NhRs2f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\n",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "\n",
    "\n",
    "# Rimozione degli elementi 'PAD' dalla sequenza e dagli attributi\n",
    "generated_text = [aa for aa in generated_text if aa != 'PAD']\n",
    "attributions_sum = [score for score, aa in zip(attributions_sum, generated_text) if aa != 'PAD']\n",
    "\n",
    "# Creazione della lista di amminoacidi unici\n",
    "unique_aminoacids = list(set(generated_text))\n",
    "unique_aminoacids.sort()\n",
    "\n",
    "# Creazione della matrice di attributi\n",
    "attributions_matrix = np.zeros((len(unique_aminoacids), len(generated_text)))\n",
    "for i, aminoacid in enumerate(generated_text):\n",
    "    j = unique_aminoacids.index(aminoacid)\n",
    "    attributions_matrix[j, i] = attributions_sum[i]\n",
    "\n",
    "# Creazione della figura\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    " \n",
    "# Tracciamento della heatmap\n",
    "im = ax.imshow(attributions_matrix, cmap='seismic', aspect='auto', vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "\n",
    "# Etichettatura degli amminoacidi sull'asse y\n",
    "ax.set_yticks(np.arange(len(unique_aminoacids)))\n",
    "ax.set_yticklabels(unique_aminoacids)\n",
    "\n",
    "# Etichettatura delle posizioni sull'asse x\n",
    "x_ticks = np.arange(0, len(generated_text), 1)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels(np.arange(1, len(generated_text)+1)[x_ticks], rotation=90)\n",
    "ax.tick_params(axis='x', labelsize=7)\n",
    "\n",
    "# Aggiunta di una barra dei colori\n",
    "cbar = ax.figure.colorbar(im, ax=ax, shrink=0.8)\n",
    "\n",
    "# Personalizzazione della barra dei colori\n",
    "cbar.set_label('Importanza')\n",
    "cbar.ax.tick_params(labelsize=10)\n",
    "\n",
    "# Titolo del grafico\n",
    "ax.set_title('Heatmap - Importanza degli amminoacidi nella sequenza proteica')\n",
    "\n",
    "ax.grid(color='lightgrey', linestyle='-', linewidth=0.5)\n",
    "\n",
    "\n",
    "# Ridimensionamento dei tick sull'asse y\n",
    "plt.tight_layout()\n",
    "\n",
    "# Visualizzazione del grafico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 262
    },
    "id": "r6P1vVXrOiUS",
    "outputId": "27407ed5-b330-49fd-f408-15afbef68c43"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1700x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "\n",
    "\n",
    "# Rimozione degli elementi 'PAD' dalla sequenza e dagli attributi\n",
    "generated_text = [aa for aa in generated_text if aa != 'PAD']\n",
    "attributions_sum = [score for score, aa in zip(attributions_sum, generated_text) if aa != 'PAD']\n",
    "\n",
    "# Creazione della lista di amminoacidi unici\n",
    "unique_aminoacids = list(set(generated_text))\n",
    "unique_aminoacids.sort()\n",
    "\n",
    "# Creazione della matrice di attributi\n",
    "attributions_matrix = np.zeros((len(unique_aminoacids), len(generated_text)))\n",
    "for i, aminoacid in enumerate(generated_text):\n",
    "    j = unique_aminoacids.index(aminoacid)\n",
    "    attributions_matrix[j, i] = attributions_sum[i]\n",
    "\n",
    "# Creazione della figura\n",
    "fig, ax = plt.subplots(figsize=(17, 13))\n",
    " \n",
    "# Tracciamento della heatmap\n",
    "#im = ax.imshow(attributions_matrix, cmap='seismic', aspect='auto', vmin=-1, vmax=1)\n",
    "im = ax.matshow(attributions_matrix, cmap='seismic', vmin=-1, vmax=1)\n",
    "\n",
    "\n",
    "# Etichettatura degli amminoacidi sull'asse y\n",
    "ax.set_yticks(np.arange(len(unique_aminoacids)))\n",
    "ax.set_yticklabels(unique_aminoacids)\n",
    "\n",
    "# Etichettatura delle posizioni sull'asse x\n",
    "x_ticks = np.arange(0, len(generated_text), 1)\n",
    "ax.set_xticks(x_ticks)\n",
    "ax.set_xticklabels(np.arange(1, len(generated_text)+1)[x_ticks], rotation=90)\n",
    "ax.tick_params(axis='x', labelsize=7)\n",
    "ax.tick_params(axis='y', labelsize=6)\n",
    "\n",
    "\n",
    "# Aggiunta di una barra dei colori\n",
    "cbar = ax.figure.colorbar(im, ax=ax, shrink=0.2)\n",
    "\n",
    "# Personalizzazione della barra dei colori\n",
    "cbar.set_label('Importanza')\n",
    "cbar.ax.tick_params(labelsize=10)\n",
    "\n",
    "# Titolo del grafico\n",
    "ax.set_title('Heatmap - Importanza degli amminoacidi nella sequenza proteica')\n",
    "\n",
    "ax.grid(color='lightgrey', linestyle='-', linewidth=0.5)\n",
    "\n",
    "\n",
    "# Ridimensionamento dei tick sull'asse y\n",
    "plt.tight_layout()\n",
    "\n",
    "# Visualizzazione del grafico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "m6qzSDZWQ3f-",
    "outputId": "5210bdc3-a616-4cb0-c8c0-8f17df80534e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-55-56f3e3bcde55>:21: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x='aminoacido', y='score', data=df, ci=None, estimator=sum, palette='RdYlBu')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "# Rimozione della parte 'PAD' dalla sequenza e dagli attributi\n",
    "#generated_text = generated_text.rstrip('PAD')\n",
    "#attributions_sum = attributions_sum[:len(generated_text)]\n",
    "\n",
    "# Creazione del DataFrame dei dati\n",
    "data = {'aminoacido': list(generated_text), 'score': attributions_sum}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Calcolo delle frequenze degli amminoacidi\n",
    "frequencies = df['aminoacido'].value_counts(normalize=True).sort_index()\n",
    "\n",
    "# Creazione del Sequence Logo\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.set(style=\"white\")\n",
    "sns.barplot(x='aminoacido', y='score', data=df, ci=None, estimator=sum, palette='RdYlBu')\n",
    "\n",
    "# Impostazioni dell'asse y\n",
    "plt.ylim(-1, 1)\n",
    "plt.ylabel('Score')\n",
    "\n",
    "# Impostazioni dell'asse x\n",
    "plt.xlabel('Aminoacido')\n",
    "plt.xticks(range(len(frequencies)), frequencies.index)\n",
    "\n",
    "# Titolo del grafico\n",
    "plt.title('Sequence Logo')\n",
    "\n",
    "# Rimozione della cornice del grafico\n",
    "sns.despine()\n",
    "\n",
    "# Visualizzazione del grafico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "29k-KWh6htfW"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}