deepmind-research/alphafold_casp13

AlphaFold

This package provides an implementation of the contact prediction network, associated model weights and CASP13 dataset as published in Nature.

Any publication that discloses findings arising from using this source code must cite AlphaFold: Protein structure prediction using potentials from deep learning by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.

Setup

Dependencies

• Python 3.6+.
• Abseil 0.8.0
• Numpy 1.16
• Six 1.12
• Setuptools 41.0.0
• Sonnet 1.35
• TensorFlow 1.14. Not compatible with TensorFlow 2.0+.
• TensorFlow Probability 0.7.0

You can set up Python virtual environment with these dependencies inside the forked deepmind_research repository using:

python3 -m venv alphafold_venv
source alphafold_venv/bin/activate
pip install -r alphafold_casp13/requirements.txt

Input data

The dataset to reproduce AlphaFold's CASP13 results can be downloaded from Google Cloud Storage.

Download it e.g. using wget (the file is about 43.5 GB):

wget https://storage.googleapis.com/alphafold_casp13_data/casp13_data.zip

The zip file contains 1 directory for each CASP13 target and a LICENSE.md file. Each target directory contains the following files:

1. TARGET.tfrec file. This is a TFRecord file with serialized tf.train.Example protocol buffers that contain the features needed to run the model.
2. contacts/TARGET.pickle file(s) with the predicted distogram.
3. contacts/TARGET.rr file(s) with the contact map derived from the predicted distogram. The RR format is described on the CASP website.

Note that for T0999 the target was manually split based on hits in HHSearch into 5 sub-targets, hence there are 5 distograms (contacts/T0999s{1,2,3,4,5}.pickle) and 5 RR files (contacts/T0999s{1,2,3,4,5}.rr).

The contacts/ folder is not needed to run the model, these files are included only for convenience so that you don't need to run the inference for CASP13 targets to get the contact map.

Model checkpoints

The model checkpoints can be downloaded from Google Cloud Storage.

Download them e.g. using wget (the file is about 210 MB):

wget https://storage.googleapis.com/alphafold_casp13_data/alphafold_casp13_weights.zip

The zip file contains:

1. A directory 873731. This contains the weights for the distogram model.
2. A directory 916425. This contains the weights for the background distogram model.
3. A directory 941521. This contains the weights for the torsion model.
4. LICENSE.md. The model checkpoints have a non-commercial license which is defined in this file.

Each directory with model weights contains a number of different model configurations. Each model has a config file and associated weights. There is only one torsion model. Each model directory also contains a stats file that is used for feature normalization specific to that model.

Distogram prediction

Running the system

You can use the run_eval.sh script to run the entire Distogram prediction system. There are a few steps you need to start with:

1. Download the input data as described above. Unpack the data in the directory with the code.
2. Download the model checkpoints as described above. Unpack the data.
3. In run_eval.sh set the following:
   • DISTOGRAM_MODEL to the path to the directory with the distogram model.
   • BACKGROUND_MODEL to the path to the directory with the background model.
   • TORSION_MODEL to the path to the directory with the torsion model.
   • TARGET to the path to the directory with the target input data.

Then run alphafold_casp13/run_eval.sh from the deepmind_research parent directory (you will get errors if you try running run_eval.sh directly from the alphafold_casp13 directory).

The contact prediction works in the following way:

1. 4 replicas (by replica we mean a configuration file describing the network architecture and a snapshot with the network weights), each with slightly different model configuration, are launched to predict the distogram.
2. 4 replicas, each with slightly different model configuration are launched to predict the background distogram.
3. 1 replica is launched to predict the torsions.
4. The predictions from the different replicas are averaged together using ensemble_contact_maps.py.
5. The predictions for the 64 × 64 distogram crops are pasted together using paste_contact_maps.py.

When running run_eval.sh the output has the following directory structure:

• distogram/: Contains 4 subfolders, one for each replica. Each of these contain the predicted ASA, secondary structure and a pickle file with the distogram for each crop. It also contains an ensemble directory with the ensembled distograms.
• background_distogram/: Contains 4 subfolders, one for each replica. Each of these contain a pickle file with the background distogram for each crop. It also contains an ensemble directory with the ensembled background distograms.
• torsion/: Contains 1 subfolder as there was only a single replica. This folder contains contains the predicted ASA, secondary structure, backbone torsions and a pickle file with the distogram for each crop. It also contains an ensemble directory with the ensembled torsions.
• pasted/: Contains distograms obtained from the ensembled distograms by pasting. An RR contact map file is computed from this pasted distogram. This is the final distogram that was used in the subsequent AlphaFold folding pipeline in CASP13.

Data splits

We used a version of PDB downloaded on 2018-03-15. The train/test split can be found in the train_domains.txt and test_domains.txt files.

Disclaimer: This is not an official Google product.