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BERTology Meets Biology: Interpreting Attention in Protein Language Models

This repository is the official implementation of BERTology Meets Biology: Interpreting Attention in Protein Language Models.

ProVis Attention Visualizer

Image Image

Installation

General requirements:

  • Python >= 3.6
  • PyTorch (See installation instructions here.)

Specific libraries:

pip install biopython==1.77
pip install tape-proteins==0.4

NGLViewer (based on instructions found here):

  • Available on conda-forge channel

    conda install nglview -c conda-forge
    jupyter-nbextension enable nglview --py --sys-prefix
    
    # if you already installed nglview, you can `upgrade`
    conda upgrade nglview --force
    # might need: jupyter-nbextension enable nglview --py --sys-prefix
  • Available on PyPI

   pip install nglview
   jupyter-nbextension enable nglview --py --sys-prefix

To use in Jupyter Lab you need to install appropriate extension:

jupyter labextension install  nglview-js-widgets

Execution

cd <project_root>/notebooks
jupyter notebook provis.ipynb

You may edit the notebook to choose other proteins, attention heads, etc. The visualization tool is based on the excellent nglview library.


Experiments

Installation

cd <project_root>
python setup.py develop

Datasets

To download additional required datasets from TAPE:

cd <project_root>/data
wget http://s3.amazonaws.com/proteindata/data_pytorch/secondary_structure.tar.gz
tar -xvf secondary_structure.tar.gz && rm secondary_structure.tar.gz
wget http://s3.amazonaws.com/proteindata/data_pytorch/proteinnet.tar.gz
tar -xvf proteinnet.tar.gz && rm proteinnet.tar.gz

Attention Analysis

The following steps will recreate the reports currently found in <project_root>/reports/attention_analysis

Before performing steps, navigate to appropriate directory:

cd <project_root>/protein_attention/attention_analysis

Amino Acids

Run analysis (may wish to run in background):

sh scripts/compute_aa_features.sh

The above steps create a pickle extract file in <project_root>/data/cache

Run report from extract file:

python report_edge_features.py edge_features_aa
python report_aa_correlations.py edge_features_aa

Secondary Structure

Run analysis:

sh scripts/compute_sec_features.sh

Run reports:

python report_edge_features.py edge_features_sec

Contact Maps

Run analysis:

sh scripts/compute_contact_features.sh

Run report:

python report_edge_features.py edge_features_contact

Binding Sites

Run analysis:

sh scripts/compute_site_features.sh

Run report:

python report_edge_features.py edge_features_sites

Combined features

Create report of all features combined

python report_edge_features_combined.py edge_features_aa edge_features_sec edge_features_contact edge_features_sites

Probing Analysis

The following steps will recreate the reports currently found in <project_root>/reports/probing

Navigate to directory:

cd <project_root>/protein_attention/probing

Training

Train diagnostic classifiers. Each script will write out an extract file with evaluation results. Note: each of these scripts may run for several hours.

sh scripts/probe_ss4_0_all
sh scripts/probe_ss4_1_all
sh scripts/probe_ss4_2_all
sh scripts/probe_sites.sh
sh scripts/probe_contacts.sh

Reports

python report.py

License

This project is licensed under BSD3 License - see the LICENSE file for details

Acknowledgments

This project incorporates code from the following repo:

Citation

When referencing this repository, please cite this paper.

@misc{vig2020bertology,
    title={BERTology Meets Biology: Interpreting Attention in Protein Language Models},
    author={Jesse Vig and Ali Madani and Lav R. Varshney and Caiming Xiong and Richard Socher and Nazneen Fatema Rajani},
    year={2020},
    eprint={2006.15222},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2006.15222}
}

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