foldingdiff - Diffusion model for protein backbone generation

We present a diffusion model for generating novel protein backbone structures. For more details, see our preprint on arXiv. We also host a trained version of our model on HuggingFace spaces so you can get started with generating protein structures with just your browser!

Installation

To install, clone this using git clone. This software is written in Python, notably using PyTorch, PyTorch Lightning, and the HuggingFace transformers library. The required conda environment is defined within the environment.yml file. To set this up, make sure you have conda (or mamba) installed, clone this repository, and run:

conda env create -f environment.yml
conda activate foldingdiff
pip install -e ./  # make sure ./ is the dir including setup.py

Downloading data

We require some data files not packaged on Git due to their large size. These are not required for sampling (as long as you are not using the --testcomparison option, see below); this is required for training your own model. We provide a script in the data dir to download requisite CATH data.

# Download the CATH dataset
cd data  # Ensure that you are in the data subdirectory within the codebase
chmod +x download_cath.sh
./download_cath.sh

Training models

To train your own model on the CATH dataset, use the script at bin/train.py in combination with one of the json config files under config_jsons (or write your own). An example usage of this is as follows:

python bin/train.py config_jsons/cath_full_angles_cosine.json --dryrun

By default, the training script will calculate the KL divergence at each timestep before starting training, which can be quite computationally expensive with more timesteps. To skip this, append the --dryrun flag. The output of the model will be in the results folder with the following major files present:

results/
    - config.json           # Contains the config file for the huggingface BERT model itself
    - logs/                 # Contains the logs from training
    - models/               # Contains model checkpoints. By default we store the best 5 models by validation loss and the best 5 by training loss
    - training_args.json    # Full set of arguments, can be used to reproduce run

Pre-trained models

We provide weights for a model trained on the CATH dataset. These weights are stored on HuggingFace model hub at wukevin/foldingdiff_cath. The following code snippet shows how to load this model, load data (assuming it's been downloaded), and perform a forward pass:

from huggingface_hub import snapshot_download
from torch.utils.data.dataloader import DataLoader
from foldingdiff import modelling
from foldingdiff import datasets as dsets

# Load the model (files will be cached for future calls)
m = modelling.BertForDiffusion.from_dir(snapshot_download("wukevin/foldingdiff_cath"))

# Load dataset
# As part of loading, we try to compute internal angles in parallel. This may
# throw warnings like the following; this is normal.
# WARNING:root:Illegal values for omega in /home/*/projects/foldingdiff-main/data/cath/dompdb/2ebqA00 -- skipping
# After computing these once, the results are saved in a .pkl file under the
# foldingdiff source directory for faster loading in future calls.
clean_dset = dsets.CathCanonicalAnglesOnlyDataset(pad=128, trim_strategy='randomcrop')
noised_dset = dsets.NoisedAnglesDataset(clean_dset, timesteps=1000, beta_schedule='cosine')
dl = DataLoader(noised_dset, batch_size=32, shuffle=False)
x = iter(dl).next()

# Forward pass
predicted_noise = m(x['corrupted'], x['t'], x['attn_mask'])

Sampling protein backbones

To sample protein backbones, use the script bin/sample.py. Example commands to do this using the pretrained weights described above are as follows.

# To sample 10 backbones per length ranging from [50, 128) with a batch size of 512 - reproduces results in our manuscript
python ~/projects/foldingdiff/bin/sample.py -l 50 128 -n 10 -b 512 --device cuda:0

This will run the trained model hosted at wukevin/foldingdiff_cath and generate sequences of varying lengths. If you wish to load the test dataset and include test chains in the generated plots, use the option --testcomparison; note that this requires downloading the CATH dataset, see above. Running sample.py will create the following directory structure in the diretory where it is run:

some_dir/
    - plots/            # Contains plots comparing the distribution of training/generated angles
    - sampled_angles/   # Contains .csv.gz files with the sampled angles
    - sampled_pdb/      # Contains .pdb files from converting the sampled angles to cartesian coordinates
    - model_snapshot/   # Contains a copy of the model used to produce results

Not specifying a --device will default to the first device cuda:0; use --device cpu to run on CPU (though this will be very slow). See the following table for runtimes from our machines.

Device	Runtime estimates sampling 512 structures
Nvidia RTX 2080Ti	7 minutes
i9-9960X (16 physical cores)	2 hours

Maximum training similarity TM scores

After generating sequences, we can calculate TM-scores to evaluate the simliarity of the generated sequences and the original sequences. This is done using the script under bin/tmscore_training.py and requires data to have been downloaded prior (see above).

Visualizing diffusion "folding" process

The above sampling code can also be run with the --fullhistory flag to write an additional subdirectory sample_history under each of the sampled_angles and sampled_pdb folders that contain pdb/csv files coresponding to each timestep in the sampling process. The pdb files, for example, can then be passed into the script under foldingdiff/pymol_vis.py to generate a gif of the folding process (as shown above). An example command to do this is:

python ~/projects/foldingdiff/foldingdiff/pymol_vis.py pdb2gif -i sampled_pdb/sample_history/generated_0/*.pdb -o generated_0.gif

Note this script lives separately from other plotting code because it depends on PyMOL; feel free to install/activate your own installation of PyMOL for this.

Evaluating designability of generated backbones

One way to evaluate the quality of generated backbones is via their "designability". This refers to whether or not we can design an amino acid chain that will fold into the designed backbone. To evaluate this, we use the ESM inverse folding model to generate residues that are predicted to fold into our generated backbone, and use OmegaFold to check whether that generated sequence actually does fold into a structure comparable to our backbone. (While prior backbone design works have used AlphaFold for their designability evaluations, this was previously done without providing AlphaFold with MSA information; OmegaFold is designed from the ground up to use sequence only, and is therefore better suited for this use case.)

Inverse folding with ESM

We use a different conda environment for this step; see https://colab.research.google.com/github/facebookresearch/esm/blob/main/examples/inverse_folding/notebook.ipynb for setup details. We found that the following command works on our machines:

mamba create -n inverse python=3.9 pytorch cudatoolkit pyg -c pytorch -c conda-forge -c pyg
conda activate inverse
mamba install -c conda-forge biotite
pip install git+https://github.com/facebookresearch/esm.git

After this, we cd into the folder that contains the sampled_pdb directory created by the prior step, and run:

python ~/projects/foldingdiff/bin/pdb_to_residues_esm.py sampled_pdb -o esm_residues

This creates a new folder, esm_residues that contains 10 potential residues for each of the pdb files contained in sampled_pdb.

Structural prediction with OmegaFold

We use OmegaFold to fold the amino acid sequences produced above. After creating and activating a separate conda environment and following the authors' instructions for installing OmegaFold, we use the following script to split our input amino acid fasta files across GPUs for inference, and subsequently calculate the self-consistency TM (scTM) scores.

# Fold each fasta, spreading the work over GPUs 0 and 1, outputs to omegafold_predictions folder
python ~/projects/foldingdiff/bin/omegafold_across_gpus.py esm_residues/*.fasta -g 0 1
# Calculate the scTM scores; parallelizes across all CPUs
python ~/projects/foldingdiff/bin/omegafold_self_tm.py  # Requires no arguments

After executing these commands, the final command produces a json file of all scTM scores, as well as various pdf files containing plots and correlations of the scTM score distribution.

Tests

Tests are implemented through a mixture of doctests and unittests. To run unittests, run:

python -m unittest -v

You may see warnings like the following; these are expected.

WARNING:root:Illegal values for omega in protdiff-main/data/cath/dompdb/5a2qw00 -- skipping

timudk / foldingdiff