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AlphaFlow

AlphaFlow is a modified version of AlphaFold, fine-tuned with a flow matching objective, designed for generative modeling of protein conformational ensembles. In particular, AlphaFlow aims to model:

• Experimental ensembles, i.e, potential conformational states as they would be deposited in the PDB
• Molecular dynamics ensembles at physiological temperatures

We also provide a similarly fine-tuned version of ESMFold called ESMFlow. Technical details and thorough benchmarking results can be found in our paper, AlphaFold Meets Flow Matching for Generating Protein Ensembles, by Bowen Jing, Bonnie Berger, Tommi Jaakkola. This repository contains all code, instructions and model weights necessary to run the method. If you have any questions, feel free to open an issue or reach out at bjing@mit.edu.

June 2024 update: We have trained a 12-layer version of AlphaFlow-MD+Templates (base and distilled) which runs 2.5x times faster than the 48-layer version at a small loss in performance. We recommend considering this model if reference structures (PDB or AlphaFold) are available and runtime is of high priority.

Table of Contents

1. Installation
2. Model weights
3. Running inference
4. Evaluation scripts
5. Training
6. Ensembles
7. License
8. Citation

Installation

In an environment with Python 3.9 (for example, conda create -n alphaflow python=3.9), run:

pip install numpy==1.21.2 pandas==1.5.3
pip install torch==1.12.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install biopython==1.79 dm-tree==0.1.6 modelcif==0.7 ml-collections==0.1.0 scipy==1.7.1 absl-py einops
pip install pytorch_lightning==2.0.4 fair-esm mdtraj==1.9.9 wandb
pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@103d037'

The OpenFold installation requires CUDA 11. If the system has the wrong version, you can install CUDA 11 in the Conda environment:

conda install nvidia/label/cuda-11.8.0::cuda
conda install nvidia/label/cuda-11.8.0::cuda-cudart-dev
conda install nvidia/label/cuda-11.8.0::libcusparse-dev
conda install nvidia/label/cuda-11.8.0::libcusolver-dev
conda install nvidia/label/cuda-11.8.0::libcublas-dev
ln -s $CONDA_PREFIX/lib/libcudart_static.a $CONDA_PREFIX/lib/libcudart.a

Then install OpenFold:

CUDA_HOME=$CONDA_PREFIX pip install 'openfold @ git+https://github.com/aqlaboratory/openfold.git@103d037'

Model weights

We provide several versions of AlphaFlow (and similarly named versions of ESMFlow).

• AlphaFlow-PDB—trained on PDB structures to model experimental ensembles from X-ray crystallography or cryo-EM under different conditions
• AlphaFlow-MD—trained on all-atom, explicit solvent MD trajectories at 300K
• AlphaFlow-MD+Templates—trained to additionally take a PDB structure as input, and models the corresponding MD ensemble at 300K

For all models, the distilled version runs significantly faster at the cost of some loss of accuracy (benchmarked in the paper).

For AlphaFlow-MD+Templates, the 12l versions have 12 instead of 48 Evoformer layers and run 2.5x times faster at a small loss in performance.

AlphaFlow models

ESMFlow models

Training checkpoints (from which fine-tuning can be resumed) are available upon request; please reach out if you'd like to collaborate!

Running inference

Preparing input files

1. Prepare a input CSV with an name and seqres entry for each row. See splits/atlas_test.csv for examples.
2. If running an AlphaFlow model, prepare an MSA directory and place the alignments in .a3m format at the following paths: {alignment_dir}/{name}/a3m/{name}.a3m. If you don't have the MSAs, there are two ways to generate them:
   1. Query the ColabFold server with python -m scripts.mmseqs_query --split [PATH] --outdir [DIR].
   2. Download UniRef30 and ColabDB according to https://github.com/sokrypton/ColabFold/blob/main/setup_databases.sh and run python -m scripts.mmseqs_search_helper --split [PATH] --db_dir [DIR] --outdir [DIR].
3. If running an MD+Templates model, place the template PDB files into a templates directory with filenames matching the names in the input CSV. The PDB files should include only a single chain with no residue gaps.

Running the model

The basic command for running inference with AlphaFlow is:

python predict.py --mode alphafold --input_csv [PATH] --msa_dir [DIR] --weights [PATH] --samples [N] --outpdb [DIR]

If running the PDB model, we recommend appending --self_cond --resample for improved performance.

The basic command for running inference with ESMFlow is

python predict.py --mode esmfold --input_csv [PATH] --weights [PATH] --samples [N] --outpdb [DIR]

Additional command line arguments for either model:

• Use the --pdb_id argument to select (one or more) rows in the CSV. If no argument is specified, inference is run on all rows.
• If running the MD model with templates, append --templates_dir [DIR].
• If running any distilled model, append the arguments --noisy_first --no_diffusion.
• To truncate the inference process for increased precision and reduced diversity, append (for example) --tmax 0.2 --steps 2. The default inference settings correspond to --tmax 1.0 --steps 10. See Appendix B.1 in the paper for more details.

Evaluation scripts

Our ensemble evaluations may be reproduced via the following steps:

1. Download the ATLAS dataset by runnig from bash scripts/download_atlas.sh from the desired root directory
2. Prepare the ensemble directory with a PDB file for each ATLAS target, each with 250 structures (see zipped AlphaFlow ensembles below for examples). Some results are not directly comparable for evaluations with a different number of structures.
3. Run python -m scripts.analyze_ensembles --atlas_dir [DIR] --pdb_dir [DIR] --num_workers [N]. This will produce an analysis file named out.pkl in the pdb_dir.
4. Run python -m scripts.print_analysis [PATH] [PATH] ... with an arbitrary number of paths to out.pkl files. A formatted comparison table will be printed.

Training

Downloading datasets

To download and preprocess the PDB,

1. Run aws s3 sync --no-sign-request s3://pdbsnapshots/20230102/pub/pdb/data/structures/divided/mmCIF pdb_mmcif from the desired directory.
2. Run find pdb_mmcif -name '*.gz' | xargs gunzip to extract the MMCIF files.
3. From the repository root, run python -m scripts.unpack_mmcif --mmcif_dir [DIR] --outdir [DIR] --num_workers [N]. This will preprocess all chains into NPZ files and create a pdb_mmcif.csv index.
4. Download OpenProteinSet with aws s3 sync --no-sign-request s3://openfold/ openfold from the desired directory.
5. Run python -m scripts.add_msa_info --openfold_dir [DIR] to produce a pdb_mmcif_msa.csv index with OpenProteinSet MSA lookup.
6. Run python -m scripts.cluster_chains to produce a pdb_clusters file at 40% sequence similarity (Mmseqs installation required).
7. Create MSAs for the PDB validation split (splits/cameo2022.csv) according to the instructions in the previous section.

To download and preprocess the ATLAS MD trajectory dataset,

1. Run bash scripts/download_atlas.sh from the desired directory.
2. From the repository root, run python -m scripts.prep_atlas --atlas_dir [DIR] --outdir [DIR] --num_workers [N]. This will preprocess the ATLAS trajectories into NPZ files.
3. Create MSAs for all entries in splits/atlas.csv according to the instructions in the previous section.

Running training

Before running training, download the pretrained AlphaFold and ESMFold weights into the repository root via

wget https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar
tar -xvf alphafold_params_2022-12-06.tar params_model_1.npz
wget https://dl.fbaipublicfiles.com/fair-esm/models/esmfold_3B_v1.pt

The basic command for training AlphaFlow is

python train.py --lr 5e-4 --noise_prob 0.8 --accumulate_grad 8 --train_epoch_len 80000 --train_cutoff 2018-05-01 --filter_chains \
    --train_data_dir [DIR] \
    --train_msa_dir [DIR] \
    --mmcif_dir [DIR] \
    --val_msa_dir [DIR] \
    --run_name [NAME] [--wandb]

where the PDB NPZ directory, the OpenProteinSet directory, the PDB mmCIF directory, and the validation MSA directory are specified. This training run produces the AlphaFlow-PDB base version. All other models are built off this checkpoint.

To continue training on ATLAS, run

python train.py --normal_validate --sample_train_confs --sample_val_confs --num_val_confs 100 --pdb_chains splits/atlas_train.csv --val_csv splits/atlas_val.csv --self_cond_prob 0.0 --noise_prob 0.9 --val_freq 10 --ckpt_freq 10 \
    --train_data_dir [DIR] \
    --train_msa_dir [DIR] \
    --ckpt [PATH] \
    --run_name [NAME] [--wandb]

where the ATLAS MSA and NPZ directories and AlphaFlow-PDB checkpoints are specified.

To instead train on ATLAS with templates, run with the additional arguments --first_as_template --extra_input --lr 1e-4 --restore_weights_only --extra_input_prob 1.0.

Distillation: to distill a model, append --distillation and supply the --ckpt [PATH] of the model to be distilled. For PDB training, we remove --accumulate_grad 8 and recommend distilling with a shorter --train_epoch_len 16000. Note that --self_cond_prob and --noise_prob will be ignored and can be omitted.

ESMFlow: run the same commands with --mode esmfold and --train_cutoff 2020-05-01.

Ensembles

We provide the ensembles sampled from the model which were used for the analyses and results reported in the paper.

AlphaFlow ensembles

ESMFlow ensembles

License

MIT. Other licenses may apply to third-party source code noted in file headers.

Citation

@inproceedings{jing2024alphafold,
  title={AlphaFold Meets Flow Matching for Generating Protein Ensembles},
  author={Jing, Bowen and Berger, Bonnie and Jaakkola, Tommi},
  year={2024},
  booktitle={Forty-first International Conference on Machine Learning}
}