AlphaFold with saved intermediate representations

This repository contains modifications to [AlphaFold v2.0] (https://github.com/deepmind/alphafold), which saves 12 intermediate representations of the proteins predicted.

Representations

The 12 representations are saved within output/processed_features_model_1.pkl and output/result_model_1.pkl by running the feature extraction scripts.

Four output representations from featurization

• Saved to processed_features_model_1.pkl['msa_feat']
• Dimensions: (4, s, r, 49). The first dimension indicates the 4 output representations. To obtain the representation corresponding to the predicted protein for each of the 4 representations, please extract the first row of dimension s (number of clusters). The resulting dimension per representation should be (r, 49).

Four output representations from the Evoformer

• Saved as result_model_1.pkl['representations']['msa_first_row_iter_0-3']
• Dimensions: (r, 256)

Four output representations from the Structure Module

• Saved as result_model_1.pkl['representations']['structure_msa_first_row_iter_0-3']
• Dimensions: (r, 384)

The above extracted features could be unpacked by running the feature unpacking scripts.

Installation

Run install_alphafold.sh to install this package. Conda is required.

Running the script

The representation extraction process consists of two parts. First, to save the intermediate features from AF2. Then, to unpack those representations. A script to extract the MSA alone is also available.

Feature extraction

scripts/run_extract_features.py: This script runs AlphaFold v2.0 and saves intermediate representations.

bsub/bsub_extract_features.sh: This script submits a jobarray for the above script on an HPC with an LSF scheduler.

• #BSUB -J run-alphafold[1-355]: This indicates a job array for the 355 RBPs in the dataset.
• #BSUB -R rusage[mem=10]: Please increase the memory requirement as needed.
• #BSUB -n 8: Exactly 8 cores are required to run an AlphaFold job.
• Please set DATA_DIR to the directory containing the AlphaFold database.
• Stdout and stderr files will be saved in logs/.

Feature unpacking

scripts/run_unpack_features.py: This script unpacks the output from run_extract_features.py and saves the 12 intermediate representations.

bsub/bsub_unpack_features.sh: This script submits a job for the above script on an HPC with an LSF scheduler.

MSA extraction

scripts/run_extract_msa.py: This script runs AlphaFold v2.0 and saves the MSA.

bsub/bsub/extract_msa.py: This script submits a jobarray for the above script on an HPC with an LSF scheduler.