OpenFold

A faithful PyTorch reproduction of DeepMind's AlphaFold 2.

Features

OpenFold carefully reproduces (almost) all of the features of the original open source inference code. The sole exception is model ensembling, which fared poorly in DeepMind's own ablation testing and is being phased out in future DeepMind experiments. It is omitted here for the sake of reducing clutter. In cases where the Nature paper differs from the source, we always defer to the latter.

OpenFold is built to support inference with AlphaFold's original JAX weights. Try it out with our Colab notebook.

Unlike DeepMind's public code, OpenFold is also trainable. It can be trained with DeepSpeed and with mixed precision. bfloat16 training is not currently supported, but will be in the future.

Installation (Linux)

Python dependencies available through pip are provided in requirements.txt. OpenFold depends on openmm==7.5.1 and pdbfixer, which are only available via conda. For producing sequence alignments, you'll also need jackhmmer, kalign, and the HH-suite installed on your system. Finally, some download scripts require aria2c.

For convenience, we provide a script that installs Miniconda locally, creates a conda virtual environment, installs all Python dependencies, and downloads useful resources (including DeepMind's pretrained parameters). Run:

scripts/install_third_party_dependencies.sh

To activate the environment, run:

source scripts/activate_conda_env.sh

To deactivate it, run:

source scripts/deactivate_conda_env.sh

To install the HH-suite to /usr/bin, run

# scripts/install_hh_suite.sh

Usage

To download DeepMind's pretrained parameters and common ground truth data, run:

scripts/download_data.sh data/

You have two choices for downloading protein databases, depending on whether you want to use DeepMind's MSA generation pipeline (w/ HMMR & HHblits) or ColabFold's, which uses the faster MMseqs2 instead. For the former, run:

scripts/download_alphafold_databases.sh data/

For the latter, run:

scripts/download_mmseqs_databases.sh data/    # downloads .tar files
scripts/prep_mmseqs_databases.sh data/        # unpacks and preps the databases

Make sure to run the latter command on the machine that will be used for MSA generation (the script estimates how the precomputed database index used by MMseqs2 should be split according to the memory available on the system).

Alternatively, you can use raw MSAs from ProteinNet. After downloading the database, use scripts/prepare_proteinnet_msas.py to convert the data into a format recognized by the OpenFold parser. The resulting directory becomes the alignment_dir used in subsequent steps. Use scripts/unpack_proteinnet.py to extract .core files from ProteinNet text files.

Inference

To run inference on a sequence or multiple sequences using a set of DeepMind's pretrained parameters, run e.g.:

python3 run_pretrained_openfold.py \
    target.fasta \
    data/uniref90/uniref90.fasta \
    data/mgnify/mgy_clusters_2018_12.fa \
    data/pdb70/pdb70 \
    data/pdb_mmcif/mmcif_files/ \
    data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
    --output_dir ./ \
    --bfd_database_path data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
    --model_device cuda:1 \
    --jackhmmer_binary_path lib/conda/envs/openfold_venv/bin/jackhmmer \
    --hhblits_binary_path lib/conda/envs/openfold_venv/bin/hhblits \
    --hhsearch_binary_path lib/conda/envs/openfold_venv/bin/hhsearch \
    --kalign_binary_path lib/conda/envs/openfold_venv/bin/kalign

where data is the same directory as in the previous step. If jackhmmer, hhblits, hhsearch and kalign are available at the default path of /usr/bin, their binary_path command-line arguments can be dropped. If you've already computed alignments for the query, you have the option to circumvent the expensive alignment computation here.

Training

After activating the OpenFold environment with source scripts/activate_conda_env.sh, install OpenFold by running

python setup.py install

To train the model, you will first need to precompute protein alignments.

You have two options. You can use the same procedure DeepMind used by running the following:

python3 scripts/precompute_alignments.py mmcif_dir/ alignment_dir/ \
    data/uniref90/uniref90.fasta \
    data/mgnify/mgy_clusters_2018_12.fa \
    data/pdb70/pdb70 \
    data/pdb_mmcif/mmcif_files/ \
    data/uniclust30/uniclust30_2018_08/uniclust30_2018_08 \
    --bfd_database_path data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt \
    --cpus 16 \
    --jackhmmer_binary_path lib/conda/envs/openfold_venv/bin/jackhmmer \
    --hhblits_binary_path lib/conda/envs/openfold_venv/bin/hhblits \
    --hhsearch_binary_path lib/conda/envs/openfold_venv/bin/hhsearch \
    --kalign_binary_path lib/conda/envs/openfold_venv/bin/kalign

As noted before, you can skip the binary_path arguments if these binaries are at /usr/bin. Expect this step to take a very long time, even for small numbers of proteins.

Alternatively, you can generate MSAs with the ColabFold pipeline (and templates with HHsearch) with:

python3 scripts/precompute_alignments_mmseqs.py input.fasta \
    data/mmseqs_dbs \
    uniref30_2103_db \
    alignment_dir \
    ~/MMseqs2/build/bin/mmseqs \
    /usr/bin/hhsearch \
    --env_db colabfold_envdb_202108_db
    --pdb70 data/pdb70/pdb70

where input.fasta is a FASTA file containing one or more query sequences. To generate an input FASTA from a directory of mmCIF and/or ProteinNet .core files, we provide scripts/data_dir_to_fasta.py.

Next, generate a cache of certain datapoints in the mmCIF files:

python3 scripts/generate_mmcif_cache.py \
    mmcif_dir/ \
    mmcif_cache.json \
    --no_workers 16

This cache is used to minimize the number of mmCIF parses performed during training-time data preprocessing. Finally, call the training script:

python3 train_openfold.py mmcif_dir/ alignment_dir/ template_mmcif_dir/ \
    2021-10-10 \ 
    --template_release_dates_cache_path mmcif_cache.json \ 
    --precision 16 \
    --gpus 8 --replace_sampler_ddp=True \
    --seed 42 \ # in multi-gpu settings, the seed must be specified
    --deepspeed_config_path deepspeed_config.json \
    --resume_from_ckpt ckpt_dir/

where --template_release_dates_cache_path is a path to the .json file generated in the previous step. A suitable DeepSpeed configuration file can be generated with scripts/build_deepspeed_config.py. The training script is written with PyTorch Lightning and supports the full range of training options that entails, including multi-node distributed training. For more information, consult PyTorch Lightning documentation and the --help flag of the training script.

Testing

To run unit tests, use

scripts/run_unit_tests.sh

The script is a thin wrapper around Python's unittest suite, and recognizes unittest commands. E.g., to run a specific test verbosely:

scripts/run_unit_tests.sh -v tests.test_model

Certain tests require that AlphaFold (v. 2.0.1) be installed in the same Python environment. These run components of AlphaFold and OpenFold side by side and ensure that output activations are adequately similar. For most modules, we target a maximum difference of 1e-4.

Copyright notice

While AlphaFold's and, by extension, OpenFold's source code is licensed under the permissive Apache Licence, Version 2.0, DeepMind's pretrained parameters remain under the more restrictive CC BY-NC 4.0 license, a copy of which is downloaded to openfold/resources/params by the installation script. They are thereby made unavailable for commercial use.

Contributing

If you encounter problems using OpenFold, feel free to create an issue!

Citing this work

Stay tuned for an OpenFold DOI. Any work that cites OpenFold should also cite AlphaFold.