alphafold3-pytorch-lightning-hydra

AlphaFold 3 - PyTorch Lightning + Hydra

Description

Implementation of AlphaFold 3 in PyTorch Lightning + Hydra

You can chat with other researchers about this work here

Review of the paper by Sergey

Illustrated guide by Elana P. Simon

Talk by Max Jaderberg

The original version of this repository with Fabric support is being maintained by Phil at this repository

A visualization of the molecules of life used in the repository can be seen and interacted with here

Appreciation

• Phil for spearheading the development of the Alphafold3 module and losses as well as the Input classes!

• Joseph for contributing the Relative Positional Encoding and the Smooth LDDT Loss!

• Felipe for contributing Weighted Rigid Align, Express Coordinates In Frame, Compute Alignment Error, and Centre Random Augmentation modules!

• Heng for pointing out inconsistencies with the paper and pull requesting the solutions (e.g., finding an issue with the molecular atom indices for the distogram loss)

• Wei Lu for catching a few erroneous hyperparameters

• Milot for generating MSA and template inputs as well as optimizing the PDB dataset clustering script!

• Andrei for working on the weighted PDB dataset sampling and for taking on the gradio frontend interface!

• Jimin for submitting a small fix to an issue with the coordinates being passed into WeightedRigidAlign

• @xluo233 for contributing the confidence measures, clash penalty ranking, sample ranking logic, as well as the logic for computing the model selection score and unresolved RASA!

• sj900 for integrating and testing the WeightedPDBSampler within the PDBDataset and for adding initial support for MSA and template parsing!

• Fandi for discovering a few inconsistencies in the elucidated atom diffusion module with the supplementary

• Paolo for proposing the PDB neutral stable molecule hypothesis!

• Dhuvi for fixing a bug related to metal ion molecule ID assignment for Alphafold3Inputs and for taking on the logic for translating Alphafold3Inputs to Biomolecules for saving inference samples to mmCIF files!

• Tom (from the Discord channel) for identifying a discrepancy between this codebase's distogram and template unit vector computations and those of OpenFold (and Andrei for helping address the distogram issue)!

• Kaihui for identifying a bug in how non-standard atoms were handled in polymer residues!

• Patrick for jaxtyping, Florian for einx, and of course, Alex for einops

• Soumith and the PyTorch organization for giving Phil the opportunity to open source this work

Contents

• Installation
• Usage
• Data preparation
• Training
• Evaluation
• For developers
• Citations

Installation

Pip

pip install alphafold3-pytorch-lightning-hydra

Conda

Install mamba for dependency management (as a fast alternative to Anaconda):

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh  # accept all terms and install to the default location
rm Mambaforge-$(uname)-$(uname -m).sh  # (optionally) remove installer after using it
source ~/.bashrc  # alternatively, one can restart their shell session to achieve the same result

Install dependencies:

# Clone project
git clone https://github.com/amorehead/alphafold3-pytorch-lightning-hydra
cd alphafold3-pytorch-lightning-hydra

# Create Conda environment
mamba env create -f environment.yaml
conda activate alphafold3-pytorch  # note: one still needs to use `conda` to (de)activate environments

# Install local project as package
pip3 install -e .

Docker

The included Dockerfile contains the required dependencies to run the package and to train/inference using PyTorch with GPUs.

The default base image is pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime and installs the latest version of this package from the main GitHub branch.

# Clone project
git clone https://github.com/amorehead/alphafold3-pytorch-lightning-hydra
cd alphafold3-pytorch-lightning-hydra

# Build Docker container
docker build -t alphafold3-pytorch-lightning-hydra .

Alternatively, use build arguments to rebuild the image with different software versions:

• PYTORCH_TAG: Changes the base image and thus builds with different PyTorch, CUDA, and/or cuDNN versions.
• GIT_TAG: Changes the tag of this repo to clone and install the package.

For example:

## Use build argument to change versions
docker build --build-arg "PYTORCH_TAG=2.2.1-cuda12.1-cudnn8-devel" --build-arg "GIT_TAG=0.1.15" -t alphafold3-pytorch-lightning-hydra .

Then, run the container with GPUs and mount a local volume (for training) using the following command:

## Run Container
docker run -v .:/data --gpus all -it alphafold3-pytorch-lightning-hydra

NOTE: An AMD ROCm version of the Docker image can alternatively be built using ROCm_Dockerfile.

Usage

import torch
from alphafold3_pytorch import Alphafold3
from alphafold3_pytorch.utils.model_utils import exclusive_cumsum

alphafold3 = Alphafold3(
    dim_atom_inputs = 77,
    dim_template_feats = 108
)

# Mock inputs

seq_len = 16

molecule_atom_indices = torch.randint(0, 2, (2, seq_len)).long()
molecule_atom_lens = torch.full((2, seq_len), 2).long()

atom_seq_len = molecule_atom_lens.sum(dim=-1).amax()
atom_offsets = exclusive_cumsum(molecule_atom_lens)

atom_inputs = torch.randn(2, atom_seq_len, 77)
atompair_inputs = torch.randn(2, atom_seq_len, atom_seq_len, 5)

additional_molecule_feats = torch.randint(0, 2, (2, seq_len, 5))
additional_token_feats = torch.randn(2, seq_len, 33)
is_molecule_types = torch.randint(0, 2, (2, seq_len, 5)).bool()
is_molecule_mod = torch.randint(0, 2, (2, seq_len, 4)).bool()
molecule_ids = torch.randint(0, 32, (2, seq_len))

template_feats = torch.randn(2, 2, seq_len, seq_len, 108)
template_mask = torch.ones((2, 2)).bool()

msa = torch.randn(2, 7, seq_len, 32)
msa_mask = torch.ones((2, 7)).bool()

additional_msa_feats = torch.randn(2, 7, seq_len, 2)

# Required for training, but omitted on inference

atom_pos = torch.randn(2, atom_seq_len, 3)

distogram_atom_indices = molecule_atom_lens - 1

distance_labels = torch.randint(0, 37, (2, seq_len, seq_len))
resolved_labels = torch.randint(0, 2, (2, atom_seq_len))

# Offset indices correctly

distogram_atom_indices += atom_offsets
molecule_atom_indices += atom_offsets

# Train

loss = alphafold3(
    num_recycling_steps = 2,
    atom_inputs = atom_inputs,
    atompair_inputs = atompair_inputs,
    molecule_ids = molecule_ids,
    molecule_atom_lens = molecule_atom_lens,
    additional_molecule_feats = additional_molecule_feats,
    additional_msa_feats = additional_msa_feats,
    additional_token_feats = additional_token_feats,
    is_molecule_types = is_molecule_types,
    is_molecule_mod = is_molecule_mod,
    msa = msa,
    msa_mask = msa_mask,
    templates = template_feats,
    template_mask = template_mask,
    atom_pos = atom_pos,
    distogram_atom_indices = distogram_atom_indices,
    molecule_atom_indices = molecule_atom_indices,
    distance_labels = distance_labels,
    resolved_labels = resolved_labels
)

loss.backward()

# After much training ...

sampled_atom_pos = alphafold3(
    num_recycling_steps = 4,
    num_sample_steps = 16,
    atom_inputs = atom_inputs,
    atompair_inputs = atompair_inputs,
    molecule_ids = molecule_ids,
    molecule_atom_lens = molecule_atom_lens,
    additional_molecule_feats = additional_molecule_feats,
    additional_msa_feats = additional_msa_feats,
    additional_token_feats = additional_token_feats,
    is_molecule_types = is_molecule_types,
    is_molecule_mod = is_molecule_mod,
    msa = msa,
    msa_mask = msa_mask,
    templates = template_feats,
    template_mask = template_mask
)

sampled_atom_pos.shape # (2, <atom_seqlen>, 3)

An example with molecule level input handling

import torch
from alphafold3_pytorch import Alphafold3, Alphafold3Input

contrived_protein = 'AG'

mock_atompos = [
    torch.randn(5, 3),   # alanine has 5 non-hydrogen atoms
    torch.randn(4, 3)    # glycine has 4 non-hydrogen atoms
]

train_alphafold3_input = Alphafold3Input(
    proteins = [contrived_protein],
    missing_atom_indices = [[1, 2], None],
    atom_pos = mock_atompos
)

eval_alphafold3_input = Alphafold3Input(
    proteins = [contrived_protein]
)

# training

alphafold3 = Alphafold3(
    dim_atom_inputs = 3,
    dim_atompair_inputs = 5,
    atoms_per_window = 27,
    dim_template_feats = 108,
    num_molecule_mods = 0,
    confidence_head_kwargs = dict(
        pairformer_depth = 1
    ),
    template_embedder_kwargs = dict(
        pairformer_stack_depth = 1
    ),
    msa_module_kwargs = dict(
        depth = 1
    ),
    pairformer_stack = dict(
        depth = 2
    ),
    diffusion_module_kwargs = dict(
        atom_encoder_depth = 1,
        token_transformer_depth = 1,
        atom_decoder_depth = 1,
    )
)

loss = alphafold3.forward_with_alphafold3_inputs([train_alphafold3_input])
loss.backward()

# sampling

alphafold3.eval()
sampled_atom_pos = alphafold3.forward_with_alphafold3_inputs(eval_alphafold3_input)

assert sampled_atom_pos.shape == (1, (5 + 4), 3)

Data preparation

PDB dataset curation

To acquire the AlphaFold 3 PDB dataset, first download all first-assembly (and asymmetric unit) complexes in the Protein Data Bank (PDB), and then preprocess them with the script referenced below. The PDB can be downloaded from the RCSB: https://www.wwpdb.org/ftp/pdb-ftp-sites#rcsbpdb. The two Python scripts below (i.e., filter_pdb_{train,val,test}_mmcifs.py and cluster_pdb_{train,val,test}_mmcifs.py) assume you have downloaded the PDB in the mmCIF file format, placing its first-assembly and asymmetric unit mmCIF files at data/pdb_data/unfiltered_assembly_mmcifs/ and data/pdb_data/unfiltered_asym_mmcifs/, respectively.

For reproducibility, we recommend downloading the PDB using AWS snapshots (e.g., 20240101). To do so, refer to AWS's documentation to set up the AWS CLI locally. Alternatively, on the RCSB website, navigate down to "Download Protocols", and follow the download instructions depending on your location.

For example, one can use the following commands to download the PDB as two collections of mmCIF files:

# For `assembly1` complexes, use the PDB's `20240101` AWS snapshot:
aws s3 sync s3://pdbsnapshots/20240101/pub/pdb/data/assemblies/mmCIF/divided/ ./data/pdb_data/unfiltered_assembly_mmcifs
# Or as a fallback, use rsync:
rsync -rlpt -v -z --delete --port=33444 \
rsync.rcsb.org::ftp_data/assemblies/mmCIF/divided/ ./data/pdb_data/unfiltered_assembly_mmcifs/

# For asymmetric unit complexes, also use the PDB's `20240101` AWS snapshot:
aws s3 sync s3://pdbsnapshots/20240101/pub/pdb/data/structures/divided/mmCIF/ ./data/pdb_data/unfiltered_asym_mmcifs
# Or as a fallback, use rsync:
rsync -rlpt -v -z --delete --port=33444 \
rsync.rcsb.org::ftp_data/structures/divided/mmCIF/ ./data/pdb_data/unfiltered_asym_mmcifs/

WARNING: Downloading the PDB can take up to 700GB of space.

NOTE: The PDB hosts all available AWS snapshots here: https://pdbsnapshots.s3.us-west-2.amazonaws.com/index.html.

After downloading, you should have two directories formatted like this: https://files.rcsb.org/pub/pdb/data/assemblies/mmCIF/divided/ & https://files.rcsb.org/pub/pdb/data/structures/divided/mmCIF/

00/
01/
02/
..
zz/

For these directories, unzip all the files:

find ./data/pdb_data/unfiltered_assembly_mmcifs/ -type f -name "*.gz" -exec gzip -d {} \;
find ./data/pdb_data/unfiltered_asym_mmcifs/ -type f -name "*.gz" -exec gzip -d {} \;

Next run the commands

wget -P ./data/ccd_data/ https://files.wwpdb.org/pub/pdb/data/monomers/components.cif.gz
wget -P ./data/ccd_data/ https://files.wwpdb.org/pub/pdb/data/component-models/complete/chem_comp_model.cif.gz

from the project's root directory to download the latest version of the PDB's Chemical Component Dictionary (CCD) and its structural models. Extract each of these files using the following command:

find data/ccd_data/ -type f -name "*.gz" -exec gzip -d {} \;

PDB dataset filtering

Then run the following with pdb_assembly_dir, pdb_asym_dir, ccd_dir, and mmcif_output_dir replaced with the locations of your local copies of the first-assembly PDB, asymmetric unit PDB, CCD, and your desired dataset output directory (i.e., ./data/pdb_data/unfiltered_assembly_mmcifs/, ./data/pdb_data/unfiltered_asym_mmcifs/, ./data/ccd_data/, and ./data/pdb_data/{train,val,test}_mmcifs/).

python scripts/filter_pdb_train_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --ccd_dir <ccd_dir> --output_dir <mmcif_output_dir>
python scripts/filter_pdb_val_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --output_dir <mmcif_output_dir>
python scripts/filter_pdb_test_mmcifs.py --mmcif_assembly_dir <pdb_assembly_dir> --mmcif_asym_dir <pdb_asym_dir> --output_dir <mmcif_output_dir>

See the scripts for more options. Each first-assembly mmCIF that successfully passes all processing steps will be written to mmcif_output_dir within a subdirectory named according to the mmCIF's second and third PDB ID characters (e.g. 5c).

PDB dataset clustering

Next, run the following with mmcif_dir and {train,val,test}_clustering_output_dir replaced, respectively, with your local output directory created using the dataset filtering script above and with your desired clustering output directories (i.e., ./data/pdb_data/{train,val,test}_mmcifs/ and ./data/pdb_data/data_caches/{train,val,test}_clusterings/):

python scripts/cluster_pdb_train_mmcifs.py --mmcif_dir <mmcif_dir> --output_dir <train_clustering_output_dir> --clustering_filtered_pdb_dataset
python scripts/cluster_pdb_val_mmcifs.py --mmcif_dir <mmcif_dir> --reference_clustering_dir <train_clustering_output_dir> --output_dir <val_clustering_output_dir> --clustering_filtered_pdb_dataset
python scripts/cluster_pdb_test_mmcifs.py --mmcif_dir <mmcif_dir> --reference_1_clustering_dir <train_clustering_output_dir> --reference_2_clustering_dir <val_clustering_output_dir> --output_dir <test_clustering_output_dir> --clustering_filtered_pdb_dataset

Note: The --clustering_filtered_pdb_dataset flag is recommended when clustering the filtered PDB dataset as curated using the scripts above, as this flag will enable faster runtimes in this context (since filtering leaves each chain's residue IDs 1-based). However, this flag must not be provided when clustering other (i.e., non-PDB) datasets of mmCIF files. Otherwise, interface clustering may be performed incorrectly, as these datasets' mmCIF files may not use strict 1-based residue indexing for each chain.

Note: One can instead download preprocessed (i.e., filtered) mmCIF (train/val/test) files (~25GB, comprising 148k complexes) and chain/interface clustering (train/val/test) files (~3GB) for the PDB's 20240101 AWS snapshot via a shared OneDrive folder. Each of these tar.gz archives should be decompressed within the data/pdb_data/ directory e.g., via tar -xzf data_caches.tar.gz -C data/pdb_data/. One can also download and prepare PDB distillation data using as a reference the script scripts/distillation_data_download.sh. Once downloaded, one can run scripts/reduce_uniprot_predictions_to_pdb.py to filter this dataset to only examples associated with at least one PDB entry. Moreover, for convenience, a mapping of UniProt accession IDs to PDB IDs for training on PDB distillation data has already been downloaded and extracted as data/afdb_data/data_caches/uniprot_to_pdb_id_mapping.dat.

Training

Train model with default configuration

# Train on CPU
python alphafold3_pytorch/train.py trainer=cpu

# Train on GPU
python alphafold3_pytorch/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

# e.g., Train an initial set of weights
python alphafold3_pytorch/train.py experiment=alphafold3_initial_training.yaml

You can override any parameter from command line like this

python alphafold3_pytorch/train.py trainer.max_steps=1e6 data.batch_size=128

Evaluation

Evaluate a trained set of weights on held-out test data

# e.g., Evaluate on GPU
python alphafold3_pytorch/eval.py trainer=gpu

For developers

Contributing

At the project root, run

bash contributing.sh

Then, add your module to alphafold3_pytorch/models/components/alphafold3.py, add your tests to tests/test_alphafold3.py, and submit a pull request. You can run the tests locally with

pytest tests/

Dependency management

We use pip and docker to manage the project's underlying dependencies. Notably, to update the dependencies built by the project's Dockerfile, first edit the contents of the dependencies list in pyproject.toml, and then rebuild the project's docker image:

docker stop <container_id> # First stop any running `af3` container(s)
docker rm <container_id> # Then remove the container(s) - Caution: Make sure to push your local changes to GitHub before running this!
docker build -t af3 . # Rebuild the Docker image
docker run -v .:/data --gpus all -it af3 # Lastly, (re)start the Docker container from the updated image

If you want to update the project's pip dependencies only, you can simply push to GitHub your changes to the pyproject.toml file.

Code formatting

We use pre-commit to automatically format the project's code. To set up pre-commit (one time only) for automatic code linting and formatting upon each execution of git commit:

pre-commit install

To manually reformat all files in the project as desired:

pre-commit run -a

Refer to pre-commit's documentation for more details.

Citations

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