PMFs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PMFs of Basic Amino Acids\n",
    "In this notebook, we plot PMFs of membrane translocation for neutral, charged, and titratable peptides from all-atom and CG simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giulio/miniconda3/envs/pepmem/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np, pandas as pd\n",
    "import os, sys, json, shutil\n",
    "from math import asinh, sqrt, pi\n",
    "import pandas as pd\n",
    "import glob\n",
    "import mdtraj as md\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.optimize import least_squares\n",
    "from scipy.linalg import svd\n",
    "from scipy.interpolate import Akima1DInterpolator\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "try:\n",
    "    workdir\n",
    "except NameError:\n",
    "    workdir=%pwd\n",
    "else:\n",
    "    %cd $workdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 12,'xtick.major.pad':3,'ytick.major.pad':3,'figure.dpi':80,\n",
    "                     'xtick.major.size':6,'ytick.major.size':6,'legend.fontsize':12,\n",
    "                     'xtick.direction':'out','ytick.direction':'out','axes.labelsize':12,\n",
    "                     'axes.linewidth':1.2,'xtick.labelsize':12,'ytick.labelsize':12,\n",
    "                     'xtick.major.width':1.2, 'ytick.major.width':1.2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <th>AminoAcid</th>\n",
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       "               min0       max0       min1       max1\n",
       "AminoAcid                                           \n",
       "H         -4.743200   7.607264  -5.029907  29.600420\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('allatom/allatom.p'):\n",
    "    allatom = pd.read_pickle('allatom/allatom.p')\n",
    "allatom[['min0','max0','min1','max1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "                xmin0       xmin1\n",
       "AminoAcid                        \n",
       "H          [1.334198]  [1.562473]\n",
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    "if os.path.isfile('alatom/allatom.p'):\n",
    "    allatom = pd.read_pickle('allatom/allatom.p')\n",
    "allatom[['xmin0','xmin1']]"
   ]
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  {
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      "text/plain": [
       "                         min0      max0      min1       max1    min0/1  \\\n",
       "ah at  aa R   pKa                                                        \n",
       "25 -35 2  2.0 6.04  -0.427600  10.63620 -3.884218  28.182034 -1.750360   \n",
       "              10.67 -0.427600  10.63620 -3.884218  28.182034 -3.829730   \n",
       "35 -35 3  2.5 12.10 -4.632900  17.54420 -5.700900  28.497400 -5.800047   \n",
       "44 -44 2  2.5 12.10 -3.264447  17.33774 -8.285777  27.928793 -8.439418   \n",
       "\n",
       "                        max0/1  \n",
       "ah at  aa R   pKa               \n",
       "25 -35 2  2.0 6.04   10.766543  \n",
       "              10.67  19.507451  \n",
       "35 -35 3  2.5 12.10  27.252389  \n",
       "44 -44 2  2.5 12.10  26.456655  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('coarsegrained/titr.p'):\n",
    "    titr = pd.read_pickle('coarsegrained/titr.p')\n",
    "titr[['min0','max0','min1','max1','min0/1','max0/1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "                      xmin0   xmin1 xmin0/1\n",
       "ah at  aa R   pKa                          \n",
       "25 -35 2  2.0 6.04   [27.0]  [24.0]  [24.0]\n",
       "              10.67  [27.0]  [24.0]  [24.0]\n",
       "35 -35 3  2.5 12.10  [23.0]  [24.0]  [24.0]\n",
       "44 -44 2  2.5 12.10  [24.0]  [23.0]  [23.0]"
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     "execution_count": 6,
     "metadata": {},
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       "                         min0       max0       min1       max1     min0/1  \\\n",
       "ah at  aa R   pKa                                                           \n",
       "25 -35 2  2.0 6.04  -0.817180  17.849382  -7.987683  32.196941  -3.233740   \n",
       "              10.67 -0.817180  17.849382  -7.987683  32.196941  -8.157177   \n",
       "35 -35 3  2.5 12.10 -9.126450  21.117003 -11.975425  29.488504 -12.055345   \n",
       "44 -44 2  2.5 12.10 -6.210522  23.332893 -17.898750  21.232503 -17.624440   \n",
       "\n",
       "                        max0/1  \n",
       "ah at  aa R   pKa               \n",
       "25 -35 2  2.0 6.04   17.986058  \n",
       "              10.67  31.151626  \n",
       "35 -35 3  2.5 12.10  27.584449  \n",
       "44 -44 2  2.5 12.10  22.548311  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('coarsegrained/dimer.p'):\n",
    "    dimer = pd.read_pickle('coarsegrained/dimer.p')\n",
    "dimer[['min0','max0','min1','max1','min0/1','max0/1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min0</th>\n",
       "      <th>max0</th>\n",
       "      <th>min1</th>\n",
       "      <th>max1</th>\n",
       "      <th>min0/1</th>\n",
       "      <th>max0/1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ah</th>\n",
       "      <th>at</th>\n",
       "      <th>aa</th>\n",
       "      <th>R</th>\n",
       "      <th>pKa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">25</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">-35</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>6.04</th>\n",
       "      <td>-1.282125</td>\n",
       "      <td>24.363690</td>\n",
       "      <td>-12.36338</td>\n",
       "      <td>29.756340</td>\n",
       "      <td>-4.492803</td>\n",
       "      <td>24.305644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.67</th>\n",
       "      <td>-1.282125</td>\n",
       "      <td>24.363690</td>\n",
       "      <td>-12.36338</td>\n",
       "      <td>29.756340</td>\n",
       "      <td>-12.573750</td>\n",
       "      <td>29.048859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <th>-35</th>\n",
       "      <th>3</th>\n",
       "      <th>2.5</th>\n",
       "      <th>12.10</th>\n",
       "      <td>-13.455513</td>\n",
       "      <td>20.509426</td>\n",
       "      <td>-18.18088</td>\n",
       "      <td>23.350256</td>\n",
       "      <td>-17.657407</td>\n",
       "      <td>24.275099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <th>-44</th>\n",
       "      <th>2</th>\n",
       "      <th>2.5</th>\n",
       "      <th>12.10</th>\n",
       "      <td>-9.037770</td>\n",
       "      <td>23.783691</td>\n",
       "      <td>-25.98574</td>\n",
       "      <td>15.219731</td>\n",
       "      <td>-26.995158</td>\n",
       "      <td>14.563340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          min0       max0      min1       max1     min0/1  \\\n",
       "ah at  aa R   pKa                                                           \n",
       "25 -35 2  2.0 6.04   -1.282125  24.363690 -12.36338  29.756340  -4.492803   \n",
       "              10.67  -1.282125  24.363690 -12.36338  29.756340 -12.573750   \n",
       "35 -35 3  2.5 12.10 -13.455513  20.509426 -18.18088  23.350256 -17.657407   \n",
       "44 -44 2  2.5 12.10  -9.037770  23.783691 -25.98574  15.219731 -26.995158   \n",
       "\n",
       "                        max0/1  \n",
       "ah at  aa R   pKa               \n",
       "25 -35 2  2.0 6.04   24.305644  \n",
       "              10.67  29.048859  \n",
       "35 -35 3  2.5 12.10  24.275099  \n",
       "44 -44 2  2.5 12.10  14.563340  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('coarsegrained/trimer.p'):\n",
    "    trimer = pd.read_pickle('coarsegrained/trimer.p')\n",
    "trimer[['min0','max0','min1','max1','min0/1','max0/1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min1</th>\n",
       "      <th>max1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ah</th>\n",
       "      <th>at</th>\n",
       "      <th>R</th>\n",
       "      <th>pep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">-3</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2.5</th>\n",
       "      <th>monomer</th>\n",
       "      <td>-1.320039</td>\n",
       "      <td>9.944678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dimer</th>\n",
       "      <td>-2.828783</td>\n",
       "      <td>15.728623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trimer</th>\n",
       "      <td>-4.050510</td>\n",
       "      <td>21.605620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       min1       max1\n",
       "ah at R   pep                         \n",
       "11 -3 2.5 monomer -1.320039   9.944678\n",
       "          dimer   -2.828783  15.728623\n",
       "          trimer  -4.050510  21.605620"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('coarsegrained/hu.p'):\n",
    "    hu = pd.read_pickle('coarsegrained/hu.p')\n",
    "hu[['min1','max1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_yt(sub,df,ah,at,aa,R,pKa,c):\n",
    "    x = df.loc[ah,at,aa,R,pKa]['x0']\n",
    "    y0 = df.loc[ah,at,aa,R,pKa]['y0']\n",
    "    y1 = df.loc[ah,at,aa,R,pKa]['y1']\n",
    "    pKaz = (y0-y1)/np.log(10)+pKa\n",
    "    f = 1 / (1 + np.power(10,pKaz-7))\n",
    "    yt = (y0+np.log(10)*(pKa-7))*f + y1*(1-f)\n",
    "    yt = yt - yt[-1]\n",
    "    sub.plot(x,yt,lw=2,color='k',ls='-',label='0/1 Eq.10')\n",
    "def calc_ytAA(sub,aminoacid,pKa,c):\n",
    "    x = allatom.loc[aminoacid]['x0']*10\n",
    "    y0 = allatom.loc[aminoacid]['y0']\n",
    "    x1 = allatom.loc[aminoacid]['x1']*10\n",
    "    y1 = allatom.loc[aminoacid]['y1']\n",
    "    y1 = Akima1DInterpolator(x1, y1)(x) \n",
    "    x = x[~np.isnan(y1)]; y0 = y0[~np.isnan(y1)]; y1 = y1[~np.isnan(y1)]\n",
    "    print(y1.size,x.size)\n",
    "    pKaz = (y0-y1)/np.log(10)+pKa\n",
    "    f = 1 / (1 + np.power(10,pKaz-7))\n",
    "    yt = (y0+np.log(10)*(pKa-7))*f + y1*(1-f)\n",
    "    yt = yt - yt[-1] + y1[-1]\n",
    "    sub.plot(x,yt,lw=2,color='k',ls='-',label='0/1 Eq.10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histidine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185 185\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x480 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'figure.figsize': [6.5, 6]})\n",
    "f, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "for profile,ls in zip(['0','1'],[':','--']):\n",
    "    x = allatom.loc['H']['x'+profile]*10\n",
    "    y = allatom.loc['H']['y'+profile]\n",
    "    Ey = allatom.loc['H']['Ey'+profile]\n",
    "    ax1.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "    ax1.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "calc_ytAA(ax1,'H',6.04,7)\n",
    "\n",
    "ah=25; at=-35; aa=2; R=2; pKa=6.04\n",
    "for df,ax in zip([titr,dimer,trimer],[ax2,ax3,ax4]): \n",
    "    for profile,ls in zip(['0','1','0/1'],[':','--','-']):\n",
    "        x = df.loc[ah,at,aa,R,pKa]['x'+profile]\n",
    "        y = df.loc[ah,at,aa,R,pKa]['y'+profile]\n",
    "        Ey = df.loc[ah,at,aa,R,pKa]['Ey'+profile]\n",
    "        ax.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "        ax.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "    #if ax==ax2: calc_yt(ax,df,ah,at,aa,R,pKa,7)\n",
    "    \n",
    "for ax in f.axes:\n",
    "    ax.set_xticks(np.arange(0,42,10)); ax.set_xlim(0,40); \n",
    "    ax.set_ylabel('PMF  /  $k_B T$'); ax.set_ylim(-20,45)\n",
    "    ax.set_xticks(np.arange(0,41,10),['{:1g}'.format(i) for i in np.arange(0,41,10)])\n",
    "\n",
    "ax1.set_xticks(np.arange(-5,32,10)); ax1.set_xlim(-5,32)\n",
    "ax1.set_xticks(np.arange(-5,32,10),['{:1g}'.format(i) for i in np.arange(-5,32,10)])\n",
    "\n",
    "ax3.set_xlabel('$z$  /  Å')\n",
    "ax4.set_xlabel('$z$  /  Å')\n",
    "ax3.set_xticks(np.arange(0,34,10),['{:1g}'.format(i) for i in np.arange(0,34,10)])\n",
    "ax4.set_xticks(np.arange(0,34,10),['{:1g}'.format(i) for i in np.arange(0,34,10)])\n",
    "    \n",
    "ax1.annotate(r'A',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax2.annotate(r'B',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax3.annotate(r'C',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax4.annotate(r'D',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax1.annotate(r'AA',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax2.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax3.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax4.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax1.annotate('Histidine\\nMonomer',xy=(0.18,.82), fontsize=11, xycoords='axes fraction')\n",
    "ax2.annotate('Monomer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax3.annotate('Dimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax4.annotate('Trimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax2.yaxis.set_label_position('right'); ax2.yaxis.set_ticks_position('right')\n",
    "ax4.yaxis.set_label_position('right'); ax4.yaxis.set_ticks_position('right')\n",
    "ax1.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax2.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax3.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax4.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "f.tight_layout(w_pad=.1,h_pad=.1)    \n",
    "f.savefig('figs/histidine.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lysine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "187 187\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x480 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'figure.figsize': [6.5, 6]})\n",
    "f, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "for profile,ls in zip(['0','1'],[':','--']):\n",
    "    x = allatom.loc['K']['x'+profile]*10\n",
    "    y = allatom.loc['K']['y'+profile]\n",
    "    Ey = allatom.loc['K']['Ey'+profile]\n",
    "    ax1.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "    ax1.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "calc_ytAA(ax1,'K',10.67,7)\n",
    "\n",
    "ah=25; at=-35; aa=2; R=2; pKa=10.67\n",
    "for df,ax in zip([titr,dimer,trimer],[ax2,ax3,ax4]): \n",
    "    for profile,ls in zip(['0','1','0/1'],[':','--','-']):\n",
    "        x = df.loc[ah,at,aa,R,pKa]['x'+profile]\n",
    "        y = df.loc[ah,at,aa,R,pKa]['y'+profile]\n",
    "        Ey = df.loc[ah,at,aa,R,pKa]['Ey'+profile]\n",
    "        ax.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "        ax.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "    #if ax==ax2: calc_yt(ax,df,ah,at,aa,R,pKa,7)\n",
    "    \n",
    "for ax in f.axes:\n",
    "    ax.set_xticks(np.arange(0,42,10)); ax.set_xlim(0,40); \n",
    "    ax.set_ylabel('PMF  /  $k_B T$'); ax.set_ylim(-20,45)\n",
    "    ax.set_xticks(np.arange(0,41,10),['{:1g}'.format(i) for i in np.arange(0,41,10)])\n",
    "    \n",
    "ax1.set_xticks(np.arange(-10,32,10)); ax1.set_xlim(-10,32)\n",
    "ax1.set_xticks(np.arange(-10,32,10),['{:1g}'.format(i) for i in np.arange(-10,32,10)])\n",
    "\n",
    "ax3.set_xlabel('$z$  /  Å')\n",
    "ax4.set_xlabel('$z$  /  Å')\n",
    "ax3.set_xticks(np.arange(0,34,10),['{:1g}'.format(i) for i in np.arange(0,34,10)])\n",
    "ax4.set_xticks(np.arange(0,34,10),['{:1g}'.format(i) for i in np.arange(0,34,10)])\n",
    "    \n",
    "ax1.annotate(r'A',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax2.annotate(r'B',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax3.annotate(r'C',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax4.annotate(r'D',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax1.annotate(r'AA',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax2.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax3.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax4.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax1.annotate('Lysine\\nMonomer',xy=(0.18,.82), fontsize=11, xycoords='axes fraction')\n",
    "ax2.annotate('Monomer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax3.annotate('Dimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax4.annotate('Trimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax2.yaxis.set_label_position('right'); ax2.yaxis.set_ticks_position('right')\n",
    "ax4.yaxis.set_label_position('right'); ax4.yaxis.set_ticks_position('right')\n",
    "ax1.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax2.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax3.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax4.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "f.tight_layout(w_pad=.1,h_pad=.1)    \n",
    "f.savefig('figs/lysine.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arginine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "187 187\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x480 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'figure.figsize': [6.5, 6]})\n",
    "f, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2,ncols=2)\n",
    "\n",
    "for profile,ls in zip(['0','1'],[':','--']):\n",
    "    x = allatom.loc['R']['x'+profile]*10\n",
    "    y = allatom.loc['R']['y'+profile]\n",
    "    Ey = allatom.loc['R']['Ey'+profile]\n",
    "    ax1.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "    ax1.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "calc_ytAA(ax1,'R',12.1,7)\n",
    "\n",
    "ah=44; at=-44; aa=2; R=2.5; pKa=12.1\n",
    "for df,ax in zip([titr,dimer,trimer],[ax2,ax3,ax4]): \n",
    "    for profile,ls in zip(['0','1','0/1'],[':','--','-']):\n",
    "        x = df.loc[ah,at,aa,R,pKa]['x'+profile]\n",
    "        y = df.loc[ah,at,aa,R,pKa]['y'+profile]\n",
    "        Ey = df.loc[ah,at,aa,R,pKa]['Ey'+profile]\n",
    "        ax.plot(x,y,lw=2,label=profile,color='k',ls=ls)\n",
    "        ax.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "    #if ax==ax2: calc_yt(ax,df,ah,at,aa,R,pKa,7)\n",
    "    \n",
    "for ax in f.axes:\n",
    "    ax.set_xticks(np.arange(0,42,10)); ax.set_xlim(0,40); \n",
    "    ax.set_ylabel('PMF  /  $k_B T$'); ax.set_ylim(-20,45)\n",
    "    ax.set_xticks(np.arange(0,41,10),['{:1g}'.format(i) for i in np.arange(0,41,10)])\n",
    "    \n",
    "ax1.set_xticks(np.arange(-10,32,10)); ax1.set_xlim(-10,32)\n",
    "ax1.set_xticks(np.arange(-10,32,10),['{:1g}'.format(i) for i in np.arange(-10,32,10)])\n",
    "ax3.set_ylim(-30,45); ax4.set_ylim(-30,45)\n",
    "\n",
    "ax2.set_xlim(0,45); ax3.set_xlim(0,45); ax4.set_xlim(0,45)\n",
    "\n",
    "ax3.set_xlabel('$z$  /  Å')\n",
    "ax4.set_xlabel('$z$  /  Å')\n",
    "    \n",
    "ax1.annotate(r'A',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax2.annotate(r'B',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax3.annotate(r'C',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax4.annotate(r'D',xy=(0.06,.85), fontsize=20, xycoords='axes fraction')\n",
    "ax1.annotate(r'AA',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax2.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax3.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax4.annotate(r'CG',xy=(0.06,.06), fontsize=14, xycoords='axes fraction')\n",
    "ax1.annotate('Arginine\\nMonomer',xy=(0.18,.82), fontsize=11, xycoords='axes fraction')\n",
    "ax2.annotate('Monomer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax3.annotate('Dimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax4.annotate('Trimer',xy=(0.18,.87), fontsize=11, xycoords='axes fraction')\n",
    "ax2.yaxis.set_label_position('right'); ax2.yaxis.set_ticks_position('right')\n",
    "ax4.yaxis.set_label_position('right'); ax4.yaxis.set_ticks_position('right')\n",
    "ax1.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax2.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax3.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax4.legend(handlelength=1.5,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "f.tight_layout(w_pad=.1,h_pad=.1)    \n",
    "f.savefig('figs/arginine.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stoichiometric p$K_a$ of Basic Amino Acids vs Depth of Penetration into the Lipid Bilayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x280 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'figure.figsize': [6.5, 3.5]})\n",
    "f, (ax1,ax2) = plt.subplots(nrows=1,ncols=2)\n",
    "\n",
    "def calc_pKaz(sub,aminoacid,ah,at,aa,R,pKa,ls):\n",
    "    x = titr.loc[ah,at,aa,R,pKa]['x0']\n",
    "    y0 = titr.loc[ah,at,aa,R,pKa]['y0']\n",
    "    Ey0 = titr.loc[ah,at,aa,R,pKa]['Ey0']\n",
    "    y1 = titr.loc[ah,at,aa,R,pKa]['y1']\n",
    "    Ey1 = titr.loc[ah,at,aa,R,pKa]['Ey1']\n",
    "    pKaz = (y0-y1)/np.log(10)+pKa\n",
    "    EpKaz = np.sqrt(Ey0**2+Ey1**2)/np.log(10)\n",
    "    sub.plot(x,pKaz,lw=2,label=aminoacid,color='k',ls=ls)\n",
    "    sub.fill_between(x,pKaz-EpKaz,pKaz+EpKaz,alpha=.3,color='k')\n",
    "    \n",
    "def calc_pKazAA(sub,aminoacid,pKa,ls):\n",
    "    x0 = allatom.loc[aminoacid]['x0']*10\n",
    "    y0 = allatom.loc[aminoacid]['y0']\n",
    "    Ey0 = allatom.loc[aminoacid]['Ey0']\n",
    "    x1 = allatom.loc[aminoacid]['x1']*10\n",
    "    y1 = allatom.loc[aminoacid]['y1']\n",
    "    Ey1 = allatom.loc[aminoacid]['Ey1']\n",
    "    n = 25 if aminoacid == 'R' else 0\n",
    "    x1 = x1 - x1[y1==y1[n:].max()]\n",
    "    y1 = Akima1DInterpolator(x1, y1)(x0)\n",
    "    Ey1 = Akima1DInterpolator(x1, Ey1)(x0)\n",
    "    pKaz = (y0-y1)/np.log(10)+pKa\n",
    "    EpKaz = np.sqrt(Ey0**2+Ey1**2)/np.log(10)\n",
    "    #sub.plot(x0,y0,lw=2,label=aminoacid,color=colors[c])\n",
    "    #sub.plot(x0,y1,lw=2,label=aminoacid,color=colors[c])\n",
    "    sub.plot(x0,pKaz,lw=2,label=aminoacid,color='k',ls=ls)\n",
    "    sub.fill_between(x0,pKaz-EpKaz,pKaz+EpKaz,alpha=.3,color='k',lw=0)\n",
    "    \n",
    "calc_pKazAA(ax1,'H',6.04,'-')\n",
    "calc_pKazAA(ax1,'K',10.67,'--')\n",
    "calc_pKazAA(ax1,'R',12.1,':')\n",
    "    \n",
    "calc_pKaz(ax2,'H',25,-35,2,2,6.04,'-')\n",
    "calc_pKaz(ax2,'K',25,-35,2,2,10.67,'--')\n",
    "calc_pKaz(ax2,'R',44,-44,2,2.5,12.1,':')\n",
    "\n",
    "for ax in f.axes:\n",
    "    ax.set_xticks(np.arange(0,41,10))\n",
    "    ax.set_xticklabels(['{:1g}'.format(i) for i in np.arange(0,41,10)])\n",
    "    ax.set_yticks(np.arange(0,15,3.5))\n",
    "    ax.set_yticklabels(['{:1g}'.format(i) for i in np.arange(0,15,3.5)])\n",
    "    ax.set_xlabel('$z$  /  Å'); ax.set_ylabel('p$K_a(z)$')\n",
    "    ax.set_ylim(-6,20)\n",
    "\n",
    "ax1.set_xlim(0,32)  \n",
    "ax2.set_xlim(0,45)\n",
    "    \n",
    "ax2.yaxis.set_label_position('right'); ax2.yaxis.set_ticks_position('right')\n",
    "ax1.annotate(r'A',xy=(0.06,.87), fontsize=20, xycoords='axes fraction')\n",
    "ax2.annotate(r'B',xy=(0.06,.87), fontsize=20, xycoords='axes fraction')\n",
    "ax1.legend(handlelength=1.5,handletextpad=0.5,loc='lower right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax2.legend(handlelength=1.5,handletextpad=0.5,loc='lower right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "f.tight_layout(w_pad=.1)    \n",
    "f.savefig('figs/pKa.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demonstration of the Accuracy of the Energetics of Permeation of CG Peptides Constructed from the Monopeptide Building Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 520x280 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'figure.figsize': [6.5, 3.5]})\n",
    "f, (ax1,ax2) = plt.subplots(nrows=1,ncols=2)\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "class AnyObject1(object):\n",
    "    pass\n",
    "\n",
    "class AnyObject2(object):\n",
    "    pass\n",
    "\n",
    "class data_handler1(object):\n",
    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
    "        scale = 1\n",
    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
    "        width, height = handlebox.width, handlebox.height\n",
    "        patch_sq = mpatches.Rectangle([x0, y0 + height/2 * (1 - scale) ], height * scale, height * scale, \n",
    "                facecolor='k',edgecolor='k', transform=handlebox.get_transform())\n",
    "        patch_circ = mpatches.Circle([x0 + width +5 - height/2, y0 + height/2], height/2 * scale, facecolor='k',\n",
    "                edgecolor='k', transform=handlebox.get_transform())\n",
    "\n",
    "        handlebox.add_artist(patch_sq)\n",
    "        handlebox.add_artist(patch_circ)\n",
    "        return patch_sq\n",
    "    \n",
    "class data_handler2(object):\n",
    "    def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
    "        scale = 1*13/9\n",
    "        x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
    "        width, height = handlebox.width, handlebox.height\n",
    "        patch_sq = mpatches.Rectangle([x0, y0 + height/2 * (1 - scale) ], height * scale, height * scale, \n",
    "                facecolor='w',edgecolor='k', transform=handlebox.get_transform())\n",
    "        patch_circ = mpatches.Circle([x0 + width +5 - height/2, y0 + height/2], height/2 * scale, facecolor='w',\n",
    "                edgecolor='k', transform=handlebox.get_transform())\n",
    "\n",
    "        handlebox.add_artist(patch_sq)\n",
    "        handlebox.add_artist(patch_circ)\n",
    "        return patch_sq\n",
    "\n",
    "ah=11; at=-3; R=2.5\n",
    "for profile,ls,l in zip(['monomer','dimer','trimer'],['-','--',':'],[1,2,3]):\n",
    "    x = hu.loc[ah,at,R,profile]['x1']\n",
    "    y = hu.loc[ah,at,R,profile]['y1']\n",
    "    Ey = hu.loc[ah,at,R,profile]['Ey1']\n",
    "    ax1.plot(x,y,lw=3,label='Arg$_{:1g}$'.format(l),color='k',ls=ls)\n",
    "    ax1.fill_between(x,y-Ey,y+Ey,alpha=.3,color='k',lw=0)\n",
    "\n",
    "ymax = hu.loc[11,-3,2.5][['max1']].values\n",
    "ymin = hu.loc[11,-3,2.5][['min1']].values\n",
    "Eymax = hu.loc[11,-3,2.5][['Emax1']].values\n",
    "Eymin = hu.loc[11,-3,2.5][['Emin1']].values\n",
    "\n",
    "# data from Hu et al. DOI: dx.doi.org/10.1021/jp504853t\n",
    "ymax_hu = np.array([6.94,8.64,12.80])*4.184/(.303*8.3145)\n",
    "ymin_hu = np.array([-0.83,-3.33,-3.29])*4.184/(.303*8.3145)\n",
    "\n",
    "p1 = ax2.errorbar([0,1,2],ymax,Eymax,lw=0,marker='s',color='k',\n",
    "    markeredgecolor=None,ms=9,elinewidth=1.,capsize=2,capthick=1.,label='Max')\n",
    "p2 = ax2.errorbar([0,1,2],ymin,Eymin,lw=0,marker='o',color='k',\n",
    "      markeredgecolor=None,ms=9,elinewidth=1.,capsize=2,capthick=1.,label='Min')\n",
    "p3 = ax2.plot([0,1,2],ymax_hu,lw=0,marker='s',color='w',markeredgecolor='k',ms=13,label='Max Hu et al.')\n",
    "p4 = ax2.plot([0,1,2],ymin_hu,lw=0,marker='o',color='w',markeredgecolor='k',ms=13,label='Min Hu et al.')\n",
    "\n",
    "ax1.set_xticks(np.arange(0,41,10))\n",
    "ax1.set_xticklabels(['{:1g}'.format(i) for i in np.arange(0,41,10)])\n",
    "ax1.set_xlabel('$z$  /  Å'); ax1.set_ylabel('PMF  /  $k_B T$')\n",
    "ax1.set_xlim(0,40)\n",
    "\n",
    "for ax in f.axes:\n",
    "    ax.set_yticks(np.arange(-5,26,5))\n",
    "    ax.set_yticklabels(['{:1g}'.format(i) for i in np.arange(-5,26,5)])\n",
    "    ax.set_ylim(-7.5,27.5)\n",
    "\n",
    "ax2.set_xticks(np.arange(0,3,1))\n",
    "ax2.set_xticklabels(['Arg$_1$','Arg$_2$','Arg$_3$'])\n",
    "ax2.set_ylabel('$\\Delta$F  /  $k_B T$')\n",
    "ax2.set_xlim(-.5,2.5)\n",
    "    \n",
    "ax2.yaxis.set_label_position('right'); ax2.yaxis.set_ticks_position('right')\n",
    "ax1.annotate(r'A',xy=(0.06,.87), fontsize=20, xycoords='axes fraction')\n",
    "ax2.annotate(r'B',xy=(0.06,.87), fontsize=20, xycoords='axes fraction')\n",
    "ax1.legend(handlelength=2.3,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "               frameon=False,fontsize=12,markerfirst=False)\n",
    "#ax2.legend(handlelength=2.3,handletextpad=0.5,loc='upper right',fancybox=False,borderpad=.5,\n",
    "#               frameon=False,fontsize=12,markerfirst=False)\n",
    "ax.legend([AnyObject1(), AnyObject2()], ['CG', 'AA'], markerfirst=False,\n",
    "          handler_map={AnyObject1: data_handler1(),AnyObject2: data_handler2()}, loc=(.5,.22),\n",
    "          frameon=True,borderpad=1)\n",
    "f.tight_layout(w_pad=.1)    \n",
    "f.savefig('figs/hu.pdf')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}