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UDSMProt, universal deep sequence models for protein classification

UDSMProt is an algorithm for the classification of proteins based on the sequence of amino acids alone. Its key component is a self-supervised pretraining step based on a language modeling task. The model is then subsequently finetuned to specific classification tasks. In our paper we considered enzyme class classification, gene ontology prediction and remote homology detection showcasing the excellent performance of UDSMProt.

For a detailed description of technical details and experimental results, please refer to our paper:

Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek, UDSMProt: universal deep sequence models for protein classification, Bioinformatics 36, no. 8, 2401-2409, 2020.

@article{Strodthoff:2019universal,
author = {Strodthoff, Nils and Wagner, Patrick and Wenzel, Markus and Samek, Wojciech},
title = "{UDSMProt: universal deep sequence models for protein classification}",
journal = {Bioinformatics},
volume = {36},
number = {8},
pages = {2401-2409},
year = {2020},
month = {01},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa003},
}

An earlier preprint of this work is also available at bioRxiv. This is the accompanying code repository, where we also provide links to pretrained language models.

Also have a look at USMPep:Universal Sequence Models for Major Histocompatibility Complex Binding Affinity Prediction that builds on the same framework.

Dependencies

for training/evaluation: pytorch fastai fire

for dataset creation: numpy pandas scikit-learn biopython sentencepiece lxml

Installation

We recommend using conda as Python package and environment manager. Either install the environment using the provided proteomics.yml by running conda env create -f proteomics.yml or follow the steps below:

  1. Create conda environment: conda create -n proteomics and conda activate proteomics
  2. Install pytorch: conda install pytorch -c pytorch
  3. Install fastai: conda install -c fastai fastai=1.0.52
  4. Install fire: conda install fire -c conda-forge
  5. Install scikit-learn: conda install scikit-learn
  6. Install Biopython: conda install biopython -c conda-forge
  7. Install sentencepiece: pip install sentencepiece
  8. Install lxml: conda install lxml

Optionally (for support of threshold 0.4 clusters) install cd-hit and add cd-hit to the default searchpath.

Data

Swiss-Prot and UniRef

  • Download and extract the desired Swiss-Prot release (by default we use 2017_03) from the UniProt ftp server. Save the contained uniprot_sprot.xml as uniprot_sprot_YEAR_MONTH.xml in the ./data directory
  • Download and extract the desired UniRef release (by default we use 2017_03) from the UniProt ftp server. Save the contained uniref50.xml as uniref50_YEAR_MONTH.xml in the ./data directory. As an alternative and for full reproducibility, we also provide pickled cluster files cdhit04_uniprot_sprot_2016_07.pkl and uniref50_2017_03_uniprot_sprot_2017_03.pkl to be placed under ./tmp_data that avoid downloading the full UniRef file or running cd-hit.
  • Or just call our provided script ./download_swissprot_uniref.sh 2017 03 which manages everything for you.

EC prediction

  • Preprocessed versions of the DEEPre and ECPred datasets are already contained in the ./git_data folder of the repository.
  • The custom EC40 and EC50 datasets will be created from Swiss-Prot data directly.

GO prediction

  • Download the raw GO prediction data data-2016.tar.gz from DeepGoPlus and extract it into the ./data/deepgoplus_data_2016 folder

Remote Homology Detection

  • Download the superfamily and fold datasets and extract them into the ./data folder

Data Preprocessing

  • Run the data preparation script
cd code 
./create_datasets.sh
  • The output is structured as follows:
    • tok.npy sequences as list of numerical indices (mapping is provided by tok_itos.npy)
    • label.npy (if applicable) label as list of numerical indices (mapping is provided by label_itos.npy)
    • train_IDs.npy/val_IDs.npy/test_IDs.npy numerical indices identifying training/validation/test set by specifying rows in tok.npy
    • train_IDs_prev.npy/val_IDs_prev.npy/test_IDs_prev.npy original non-numerical IDs for all entries that were ever assigned to the respective sets (used to obtain consistent splits for downstream tasks)
    • ID.npy original non-numerical IDs for all entries in tok.npy
  • The approach is easily extendable to further downstream classification or regression tasks. It only requires to implement a corresponding preprocessing method similar to the ones provided for the existing tasks in preprocessing_proteomics.py.

Basic Usage

We provide some basic usage information for the most common tasks:

  • Language Model Pretraining (or skip this step and use the provided pretrained LMs (forward and backward models trained on SwissProt 2017_03))
cd code
python modelv1.py language_model --epochs=60 --lr=0.01 --working_folder=datasets/lm/lm_sprot_dirty/ --export_preds=False --eval_on_val_test=True
  • Finetuning for enzyme class classification (here for level 1 and EC50 dataset; assuming the pretrained folder is located at datasets/lm/lm_sprot_uniref_fwd)
cd code
python modelv1.py classification --from_scratch=False --pretrained_folder=datasets/lm/lm_sprot_uniref_fwd --epochs=30 --metrics=["accuracy","macro_f1"] --lr=0.001 --lr_fixed=True --bs=32 --lr_slice_exponent=2.0 --working_folder=datasets/clas_ec/clas_ec_ec50_level1 --export_preds=True --eval_on_val_test=True
  • Finetuning for gene ontology prediction
cd code
python modelv1.py classification --from_scratch=False --pretrained_folder=datasets/lm/lm_sprot_uniref_fwd --epochs=30 --lr=0.001 --lr_fixed=True --bs=32 --lin_ftrs=[1024] --lr_slice_exponent=2.0 --metrics=[] --working_folder=datasets/clas_go/clas_go_deepgoplus_2016 --export_preds=True --eval_on_val_test=True
  • Finetuning for remote homology detection (here for superfamily level and a single dataset)
cd code
python modelv1.py classification --from_scratch=False --pretrained_folder=datasets/lm/lm_sprot_uniref_fwd --epochs=10 --bs=128 --metrics=["binary_auc","binary_auc50","accuracy"] --early_stopping=binary_auc --bs=64 --lr=0.05 --fit_one_cycle=False --working_folder=datasets/clas_scop/clas_scop0 --export_preds=True --eval_on_val_test=True

The output is logged in logfile.log in the working directory, the final results are exported for convenience as result.npy and individual predictions that can be used for example for ensembling forward and backward models are exported as preds_valid.npz and preds_valid.npz (in case export_preds is set to true).