Awesome
Protein Workshop
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This repository provides the code for the protein structure representation learning benchmark detailed in the paper Evaluating Representation Learning on the Protein Structure Universe (ICLR 2024).
In the benchmark, we implement numerous featurisation schemes, datasets for self-supervised pre-training and downstream evaluation, pre-training tasks, and auxiliary tasks.
The benchmark can be used as a working template for a protein representation learning research project, a library of drop-in components for use in your projects, or as a CLI tool for quickly running protein representation learning evaluation and pre-training configurations.
Processed datasets and pre-trained weights are made available. Downloading datasets is not required; upon first run all datasets will be downloaded and processed from their respective source.
Configuration files to run the experiments described in the manuscript are provided in the proteinworkshop/config/sweeps/
directory.
Contents
- Protein Workshop
Installation
Below, we outline how one may set up a virtual environment for proteinworkshop
. Note that these installation instructions currently target Linux-like systems with NVIDIA CUDA support. Note that Windows and macOS are currently not officially supported.
From PyPI
proteinworkshop
is available for install from PyPI. This enables training of specific configurations via the CLI or using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install PyTorch (specifically version 2.1.2
or newer) using its official pip
installation instructions, with CUDA support as desired.
# install `proteinworkshop` from PyPI
pip install proteinworkshop
# install PyTorch Geometric using the (now-installed) CLI
workshop install pyg
# set a custom data directory for file downloads; otherwise, all data will be downloaded to `site-packages`
export DATA_PATH="where/you/want/data/" # e.g., `export DATA_PATH="proteinworkshop/data"`
However, for full exploration we recommend cloning the repository and building from source.
Building from source
With a local virtual environment activated (e.g., one created with conda create -n proteinworkshop python=3.10
):
-
Clone and install the project
git clone https://github.com/a-r-j/ProteinWorkshop cd ProteinWorkshop pip install -e .
-
Install PyTorch (specifically version
2.1.2
or newer) using its officialpip
installation instructions, with CUDA support as desired# e.g., to install PyTorch with CUDA 11.8 support on Linux: pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 torchaudio==2.1.2+cu118 --index-url https://download.pytorch.org/whl/cu118
-
Then use the newly-installed
proteinworkshop
CLI to install PyTorch Geometricworkshop install pyg
-
Configure paths in
.env
(optional, will override default paths if set). See.env.example
for an example. -
Download PDB data:
python proteinworkshop/scripts/download_pdb_mmtf.py
Tutorials
We provide a five-part tutorial series of Jupyter notebooks to provide users with examples
of how to use and extend proteinworkshop
, as outlined below.
- Training a new model
- Customizing an existing dataset
- Adding a new dataset
- Adding a new model
- Adding a new task
Quickstart
Downloading datasets
Datasets can either be built from the source structures or downloaded from Zenodo. Datasets will be built from source the first time a dataset is used in a run (or by calling the appropriate setup()
method in the corresponding datamodule). We provide a CLI tool for downloading datasets:
workshop download <DATASET_NAME>
workshop download pdb
workshop download cath
workshop download afdb_rep_v4
# etc..
If you wish to build datasets from source, we recommend first downloading the entire PDB first (in MMTF format, c. 24 Gb) to reuse shared PDB data as much as possible:
workshop download pdb
# or
python proteinworkshop/scripts/download_pdb_mmtf.py
Training a model
Launching an experiment minimally requires specification of a dataset, structural encoder, and task (devices can be specified with trainer=cpu/gpu
):
workshop train dataset=cath encoder=egnn task=inverse_folding trainer=cpu env.paths.data=where/you/want/data/
# or
python proteinworkshop/train.py dataset=cath encoder=egnn task=inverse_folding trainer=cpu # or trainer=gpu
This command uses the default configurations in configs/train.yaml
, which can be overwritten by equivalently named options. For instance, you can use a different input featurisation using the features
option, or set the display name of your experiment on wandb using the name
option:
workshop train dataset=cath encoder=egnn task=inverse_folding features=ca_bb name=MY-EXPT-NAME trainer=cpu env.paths.data=where/you/want/data/
# or
python proteinworkshop/train.py dataset=cath encoder=egnn task=inverse_folding features=ca_bb name=MY-EXPT-NAME trainer=cpu # or trainer=gpu
Finetuning a model
Finetuning a model additionally requires specification of a checkpoint.
workshop finetune dataset=cath encoder=egnn task=inverse_folding ckpt_path=PATH/TO/CHECKPOINT trainer=cpu env.paths.data=where/you/want/data/
# or
python proteinworkshop/finetune.py dataset=cath encoder=egnn task=inverse_folding ckpt_path=PATH/TO/CHECKPOINT trainer=cpu # or trainer=gpu
Running a sweep/experiment
We can make use of the hydra wandb sweeper plugin to configure experiments as sweeps, allowing searches over hyperparameters, architectures, pre-training/auxiliary tasks and datasets.
See proteinworkshop/config/sweeps/
for examples.
- Create the sweep with weights and biases
wandb sweep proteinworkshop/config/sweeps/my_new_sweep_config.yaml
- Launch job workers
With wandb:
wandb agent mywandbgroup/proteinworkshop/2wwtt7oy --count 8
Or an example SLURM submission script:
#!/bin/bash
#SBATCH --nodes 1
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:1
#SBATCH --array=0-32
source ~/.bashrc
source $(conda info --base)/envs/proteinworkshop/bin/activate
wandb agent mywandbgroup/proteinworkshop/2wwtt7oy --count 1
Reproduce the sweeps performed in the manuscript:
# reproduce the baseline tasks sweep (i.e., those performed without pre-training each model)
wandb sweep proteinworkshop/config/sweeps/baseline_fold.yaml
wandb agent mywandbgroup/proteinworkshop/2awtt7oy --count 8
wandb sweep proteinworkshop/config/sweeps/baseline_ppi.yaml
wandb agent mywandbgroup/proteinworkshop/2bwtt7oy --count 8
wandb sweep proteinworkshop/config/sweeps/baseline_inverse_folding.yaml
wandb agent mywandbgroup/proteinworkshop/2cwtt7oy --count 8
# reproduce the model pre-training sweep
wandb sweep proteinworkshop/config/sweeps/pre_train.yaml
wandb agent mywandbgroup/proteinworkshop/2dwtt7oy --count 8
# reproduce the pre-trained tasks sweep (i.e., those performed after pre-training each model)
wandb sweep proteinworkshop/config/sweeps/pt_fold.yaml
wandb agent mywandbgroup/proteinworkshop/2ewtt7oy --count 8
wandb sweep proteinworkshop/config/sweeps/pt_ppi.yaml
wandb agent mywandbgroup/proteinworkshop/2fwtt7oy --count 8
wandb sweep proteinworkshop/config/sweeps/pt_inverse_folding.yaml
wandb agent mywandbgroup/proteinworkshop/2gwtt7oy --count 8
Embedding a dataset
We provide a utility in proteinworkshop/embed.py
for embedding a dataset using a pre-trained model.
To run it:
python proteinworkshop/embed.py ckpt_path=PATH/TO/CHECKPOINT collection_name=COLLECTION_NAME
See the embed
section of proteinworkshop/config/embed.yaml
for additional parameters.
Visualising pre-trained model embeddings for a given dataset
We provide a utility in proteinworkshop/visualise.py
for visualising the UMAP embeddings of a pre-trained model for a given dataset.
To run it:
python proteinworkshop/visualise.py ckpt_path=PATH/TO/CHECKPOINT plot_filepath=VISUALISATION/FILEPATH.png
See the visualise
section of proteinworkshop/config/visualise.yaml
for additional parameters.
Performing attribution of a pre-trained model
We provide a utility in proteinworkshop/explain.py
for performing attribution of a pre-trained model using integrated gradients.
This will write PDB files for all the structures in a dataset for a supervised task with residue-level attributions in the b_factor
column. To visualise the attributions, we recommend using the Protein Viewer VSCode extension and changing the 3D representation to colour by Uncertainty/Disorder
.
To run the attribution:
python proteinworkshop/explain.py ckpt_path=PATH/TO/CHECKPOINT output_dir=ATTRIBUTION/DIRECTORY
See the explain
section of proteinworkshop/config/explain.yaml
for additional parameters.
Verifying a config
python proteinworkshop/validate_config.py dataset=cath features=full_atom task=inverse_folding
Using proteinworkshop
modules functionally
One may use the modules (e.g., datasets, models, featurisers, and utilities) of proteinworkshop
functionally by importing them directly. When installing this package using PyPi, this makes building
on top of the assets of proteinworkshop
straightforward and convenient.
For example, to use any datamodule available in proteinworkshop
:
from proteinworkshop.datasets.cath import CATHDataModule
datamodule = CATHDataModule(path="data/cath/", pdb_dir="data/pdb/", format="mmtf", batch_size=32)
datamodule.download()
train_dl = datamodule.train_dataloader()
To use any model or featuriser available in proteinworkshop
:
from proteinworkshop.models.graph_encoders.dimenetpp import DimeNetPPModel
from proteinworkshop.features.factory import ProteinFeaturiser
from proteinworkshop.datasets.utils import create_example_batch
model = DimeNetPPModel(hidden_channels=64, num_layers=3)
ca_featuriser = ProteinFeaturiser(
representation="CA",
scalar_node_features=["amino_acid_one_hot"],
vector_node_features=[],
edge_types=["knn_16"],
scalar_edge_features=["edge_distance"],
vector_edge_features=[],
)
example_batch = create_example_batch()
batch = ca_featuriser(example_batch)
model_outputs = model(example_batch)
Read the docs for a full list of modules available in proteinworkshop
.
Models
Invariant Graph Encoders
Name | Source | Protein Specific |
---|---|---|
GearNet | Zhang et al. | ✓ |
DimeNet++ | Gasteiger et al. | ✗ |
SchNet | Schütt et al. | ✗ |
CDConv | Fan et al. | ✓ |
Equivariant Graph Encoders
(Vector-type)
Name | Source | Protein Specific |
---|---|---|
GCPNet | Morehead et al. | ✓ |
GVP-GNN | Jing et al. | ✓ |
EGNN | Satorras et al. | ✗ |
(Tensor-type)
Name | Source | Protein Specific |
---|---|---|
Tensor Field Network | Corso et al. | ✓ |
Multi-ACE | Batatia et al. | ✗ |
Sequence-based Encoders
Name | Source | Protein Specific |
---|---|---|
ESM2 | Lin et al. | ✓ |
Datasets
To download a (processed) dataset from Zenodo, you can run
workshop download <DATASET_NAME>
where <DATASET_NAME>
is given the first column in the tables below.
Otherwise, simply starting a training run will download and process the data from source.
Structure-based Pre-training Corpuses
Pre-training corpuses (with the exception of pdb
, cath
, and astral
) are provided in FoldComp database format. This format is highly compressed, resulting in very small disk space requirements despite the large size. pdb
is provided as a collection of
MMTF
files, which are significantly smaller in size than conventional .pdb
or .cif
file.
Name | Description | Source | Size | Disk Size | License |
---|---|---|---|---|---|
astral | SCOPe domain structures | SCOPe/ASTRAL | 1 - 2.2 Gb | Publicly available | |
afdb_rep_v4 | Representative structures identified from the AlphaFold database by FoldSeek structural clustering | Barrio-Hernandez et al. | 2.27M Chains | 9.6 Gb | GPL-3.0 |
afdb_rep_dark_v4 | Dark proteome structures identied by structural clustering of the AlphaFold database. | Barrio-Hernandez et al. | ~800k | 2.2 Gb | GPL-3.0 |
afdb_swissprot_v4 | AlphaFold2 predictions for SwissProt/UniProtKB | Kim et al. | 542k Chains | 2.9 Gb | GPL-3.0 |
afdb_uniprot_v4 | AlphaFold2 predictions for UniProt | Kim et al. | 214M Chains | 1 Tb | GPL-3.0 / CC-BY 4.0 |
cath | CATH 4.2 40% split by CATH topologies. | Ingraham et al. | ~18k chains | 4.3 Gb | CC-BY 4.0 |
esmatlas | ESMAtlas predictions (full) | Kim et al. | 1 Tb | GPL-3.0 / CC-BY 4.0 | |
esmatlas_v2023_02 | ESMAtlas predictions (v2023_02 release) | Kim et al. | 137 Gb | GPL-3.0 / CC-BY 4.0 | |
highquality_clust30 | ESMAtlas High Quality predictions | Kim et al. | 37M Chains | 114 Gb | GPL-3.0 / CC-BY 4.0 |
igfold_paired_oas | IGFold Predictions for Paired OAS | Ruffolo et al. | 104,994 paired Ab chains | CC-BY 4.0 | |
igfold_jaffe | IGFold predictions for Jaffe2022 data | Ruffolo et al. | 1,340,180 paired Ab chains | CC-BY 4.0 | |
pdb | Experimental structures deposited in the RCSB Protein Data Bank | wwPDB consortium | ~800k Chains | 23 Gb | CC0 1.0 |
Name | Description | Source | Size |
---|---|---|---|
a_thaliana | Arabidopsis thaliana (thale cress) proteome | AlphaFold2 | |
c_albicans | Candida albicans (a fungus) proteome | AlphaFold2 | |
c_elegans | Caenorhabditis elegans (roundworm) proteome | AlphaFold2 | |
d_discoideum | Dictyostelium discoideum (slime mold) proteome | AlphaFold2 | |
d_melanogaster | Drosophila melanogaster (fruit fly) proteome | AlphaFold2 | |
d_rerio | Danio rerio (zebrafish) proteome | AlphaFold2 | |
e_coli | Escherichia coli (a bacteria) proteome | AlphaFold2 | |
g_max | Glycine max (soy bean) proteome | AlphaFold2 | |
h_sapiens | Homo sapiens (human) proteome | AlphaFold2 | |
m_jannaschii | Methanocaldococcus jannaschii (an archaea) proteome | AlphaFold2 | |
m_musculus | Mus musculus (mouse) proteome | AlphaFold2 | |
o_sativa | Oryza sative (rice) proteome | AlphaFold2 | |
r_norvegicus | Rattus norvegicus (brown rat) proteome | AlphaFold2 | |
s_cerevisiae | Saccharomyces cerevisiae (brewer's yeast) proteome | AlphaFold2 | |
s_pombe | Schizosaccharomyces pombe (a fungus) proteome | AlphaFold2 | |
z_mays | Zea mays (corn) proteome | AlphaFold2 |
Supervised Datasets
Name | Description | Source | License |
---|---|---|---|
antibody_developability | Antibody developability prediction | Chen et al. | CC-BY 3.0 |
atom3d_msp | Mutation stability prediction | Townshend et al. | MIT |
atom3d_ppi | Protein-protein interaction prediction | Townshend et al. | MIT |
atom3d_psr | Protein structure ranking | Townshend et al. | MIT |
atom3d_res | Residue identity prediction | Townshend et al. | MIT |
ccpdb_ligands | Ligand binding residue prediction | Agrawal et al. | Publicly Available |
ccpdb_metal | Metal ion binding residue prediction | Agrawal et al. | Publicly Available |
ccpdb_nucleic | Nucleic acid binding residue prediction | Agrawal et al. | Publicly Available |
ccpdb_nucleotides | Nucleotide binding residue prediction | Agrawal et al. | Publicly Available |
deep_sea_proteins | Gene Ontology prediction (Biological Process) | Sieg et al. | Public domain |
go-bp | Gene Ontology prediction (Biological Process) | Gligorijevic et al | CC-BY 4.0 |
go-cc | Gene Ontology (Cellular Component) | Gligorijevic et al | CC-BY 4.0 |
go-mf | Gene Ontology (Molecular Function) | Gligorijevic et al | CC-BY 4.0 |
ec_reaction | Enzyme Commission (EC) Number Prediction | Hermosilla et al. | MIT |
fold_fold | Fold prediction, split at the fold level | Hou et al. | CC-BY 4.0 |
fold_family | Fold prediction, split at the family level | Hou et al. | CC-BY 4.0 |
fold_superfamily | Fold prediction, split at the superfamily level | Hou et al. | CC-BY 4.0 |
masif_site | Protein-protein interaction site prediction | Gainza et al. | Apache 2.0 |
metal_3d | Zinc Binding Site Prediction | Duerr et al. | MIT |
ptm | Post Translational Modification Side Prediction | Yan et al. | CC-BY 4.0 |
Tasks
Self-Supervised Tasks
Name | Description | Source |
---|---|---|
inverse_folding | Predict amino acid sequence given structure | |
residue_prediction | Masked residue type prediction | |
distance_prediction | Masked edge distance prediction | Zhang et al. |
angle_prediction | Masked triplet angle prediction | Zhang et al. |
dihedral_angle_prediction | Masked quadruplet dihedral prediction | Zhang et al. |
multiview_contrast | Contrastive learning with multiple crops and InfoNCE loss | Zhang et al. |
structural_denoising | Denoising of atomic coordinates with SE(3) decoders |
Generic Supervised Tasks
Generic supervised tasks can be applied broadly across datasets. The labels are directly extracted from the PDB structures.
These are likely to be most frequently used with the pdb
dataset class which wraps the PDB Dataset curator from Graphein.
Name | Description | Requires |
---|---|---|
binding_site_prediction | Predict ligand binding residues | HETATM ligands (for training) |
ppi_site_prediction | Predict protein binding residues | graph_y attribute in data objects specifying the desired chain to select interactions for (for training) |
Featurisation Schemes
Part of the goal of the proteinworkshop
benchmark is to investigate the impact of the degree to which increasing granularity of structural detail affects performance. To achieve this, we provide several featurisation schemes for protein structures.
Invariant Node Features
N.B. All angular features are provided in [sin, cos] transformed form. E.g.: $\textrm{dihedrals} = [sin(\phi), cos(\phi), sin(\psi), cos(\psi), sin(\omega), \cos(\omega)]$, hence their dimensionality will be double the number of angles.
Name | Description | Dimensionality |
---|---|---|
residue_type | One-hot encoding of amino acid type | 21 |
positional_encoding | Transformer-like positional encoding of sequence position | 16 |
alpha | Virtual torsion angle defined by four $C_\alpha$ atoms of residues $I_{-1}, I, I_{+1}, I_{+2}$ | 2 |
kappa | Virtual bond angle (bend angle) defined by the three $C_\alpha$ atoms of residues $I_{-2}, I, I_{+2}$ | 2 |
dihedrals | Backbone dihedral angles $(\phi, \psi, \omega)$ | 6 |
sidechain_torsions | Sidechain torsion angles $(\chi_{1-4})$ | 8 |
Equivariant Node Features
Name | Description | Dimensionality |
---|---|---|
orientation | Forward and backward node orientation vectors (unit-normalized) | 2 |
Edge Construction
We predominanty support two types of edges: $k$-NN and $\epsilon$ edges.
Edge types can be specified as follows:
python proteinworkshop/train.py ... features.edge_types=[knn_16, knn_32, eps_16]
Where the suffix after knn
or eps
specifies $k$ (number of neighbours) or $\epsilon$ (distance threshold in angstroms).
Invariant Edge Features
Name | Description | Dimensionality |
---|---|---|
edge_distance | Euclidean distance between source and target nodes | 1 |
node_features | Concatenated scalar node features of the source and target nodes | Number of scalar node features $\times 2$ |
edge_type | Type annotation for each edge | 1 |
sequence_distance | Sequence-based distance between source and target nodes | 1 |
pos_emb | Structured Transformer-inspired positional embedding of $i - j$ for source node $i$ and target node $j$ | 16 |
Equivariant Edge Features
Name | Description | Dimensionality |
---|---|---|
edge_vectors | Edge directional vectors (unit-normalized) | 1 |
For Developers
Dependency Management
We use poetry
to manage the project's underlying dependencies and to push updates to the project's PyPI package. To make changes to the project's dependencies, follow the instructions below to (1) install poetry
on your local machine; (2) customize the dependencies; or (3) (de)activate the project's virtual environment using poetry
:
-
Install
poetry
for platform-agnostic dependency management using its installation instructionsAfter installing
poetry
, to avoid potential keyring errors, disable its keyring usage by addingPYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
to your shell's startup configuration and restarting your shell environment (e.g.,echo 'export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring' >> ~/.bashrc && source ~/.bashrc
for a Bash shell environment and likewise for other shell environments). -
Install, add, or upgrade project dependencies
poetry install # install the latest project dependencies # or poetry add XYZ # add dependency `XYZ` to the project # or poetry show # list all dependencies currently installed # or poetry lock # standardize the (now-)installed dependencies
-
Activate the newly-created virtual environment following
poetry
's usage documentation# activate the environment on a `posix`-like (e.g., macOS or Linux) system source $(poetry env info --path)/bin/activate
# activate the environment on a `Windows`-like system & ((poetry env info --path) + "\Scripts\activate.ps1")
# if desired, deactivate the environment deactivate
Code Formatting
To keep with the code style for the proteinworkshop
repository, using the following lines, please format your commits before opening a pull request:
# assuming you are located in the `ProteinWorkshop` top-level directory
isort .
autoflake -r --in-place --remove-unused-variables --remove-all-unused-imports --ignore-init-module-imports .
black --config=pyproject.toml .
Documentation
To build a local version of the project's Sphinx documentation web pages:
# assuming you are located in the `ProteinWorkshop` top-level directory
pip install -r docs/.docs.requirements # one-time only
rm -rf docs/build/ && sphinx-build docs/source/ docs/build/ # NOTE: errors can safely be ignored
Citing ProteinWorkshop
Please consider citing proteinworkshop
if it proves useful in your work.
@inproceedings{
jamasb2024evaluating,
title={Evaluating Representation Learning on the Protein Structure Universe},
author={Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
}