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(Unofficial) Implementation for Generalist Equivariant Transformer (GET)

Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning

Requirements

We have prepare the configure for creating the environment with conda in env.yml:

conda env create -f env.yml

Data Preparation

We assume the datasets are downloaded to the folder ./datasets.

1. Data for Protein-Protein Affinity (PPA)

First download and decompress the protein-protein complexes in PDBbind (registration is required):

wget http://www.pdbbind.org.cn/download/PDBbind_v2020_PP.tar.gz -P ./datasets/PPA
tar zxvf ./datasets/PPA/PDBbind_v2020_PP.tar.gz -C ./datasets/PPA
rm ./datasets/PPA/PDBbind_v2020_PP.tar.gz

Then process the dataset with the provided script:

python scripts/data_process/process_PDBbind_PP.py \
    --index_file ./datasets/PPA/PP/index/INDEX_general_PP.2020 \
    --pdb_dir ./datasets/PPA/PP \
    --out_dir ./datasets/PPA/processed

The processed data will be saved to ./datasets/PPA/processed/PDBbind.pkl.

We still need to prepare the test set, i.e. Protein-Protein Affinity Benchmark Version 2. We have provided the index file in ./datasets/PPAB_V2.csv, but the structure files need to be downloaded from the official site. In case the official site is down, we also uploaded a backup on Zenodo.

wget https://zlab.umassmed.edu/benchmark/benchmark5.5.tgz -P ./datasets/PPA
tar zxvf ./datasets/PPA/benchmark5.5.tgz -C ./datasets/PPA
rm ./datasets/PPA/benchmark5.5.tgz

Then process the test set with the provided script:

python scripts/data_process/process_PPAB.py \
    --index_file ./datasets/PPA/PPAB_V2.csv \
    --pdb_dir ./datasets/PPA/benchmark5.5 \
    --out_dir ./datasets/PPA/processed

The processed dataset as well as different splits (Rigid/Medium/Flexible/All) will be saved to ./datasets/PPA/processed.

2. Data for Ligand Binding Affinity (LBA)

You only need to download and decompress the LBA dataset:

mkdir ./datasets/LBA
wget "https://zenodo.org/record/4914718/files/LBA-split-by-sequence-identity-30.tar.gz?download=1" -O ./datasets/LBA/LBA-split-by-sequence-identity-30.tar.gz
tar zxvf ./datasets/LBA/LBA-split-by-sequence-identity-30.tar.gz -C ./datasets/LBA
rm ./datasets/LBA/LBA-split-by-sequence-identity-30.tar.gz

3. Data for Ligand Efficacy Prediction (LEP)

You only need to download and decompress the LEP dataset:

mkdir ./datasets/LEP
wget "https://zenodo.org/record/4914734/files/LEP-split-by-protein.tar.gz?download=1" -O ./datasets/LEP/LEP-split-by-protein.tar.gz
tar zxvf ./datasets/LEP/LEP-split-by-protein.tar.gz -C ./datasets/LEP
rm ./datasets/LEP/LEP-split-by-protein.tar.gz

4. Data for PDBBind Benchmark

First download and extract the raw files:

mkdir ./datasets/PDBBind
wget "https://zenodo.org/record/8102783/files/pdbbind_raw.tar.gz?download=1" -O ./datasets/PDBBind/pdbbind_raw.tar.gz
tar zxvf ./datasets/PDBBind/pdbbind_raw.tar.gz -C ./datasets/PDBBind
rm ./datasets/PDBBind/pdbbind_raw.tar.gz

Then process the dataset with the provided script:

python scripts/data_process/process_PDBbind_benchmark.py \
    --benchmark_dir ./datasets/PDBBind/pdbbind \
    --out_dir ./datasets/PDBBind/processed

What is different here is that if you want to use fragment-based representation of small molecules, you need to process the data here:

python scripts/data_process/process_PDBbind_benchmark.py \
    --benchmark_dir ./datasets/PDBBind/pdbbind \
    --fragment PS_300 \
    --out_dir ./datasets/PDBBind/processed_PS_300

5. Data for Zero-Shot Inference on Nucleic-Acid-Ligand Affinity(NLA)

We need to use protein-protein data, protein-nucleic-acid data, and protein-ligand data for training, then evaluate the zero-shot performance on nucleic-acid-ligand affinity. All the data are extracted from PDBBind database. We have got protein-protein data in PPA and protein-ligand data in LBA, now we further need to get other data. To get protein-nucleic-acid data:

wget http://www.pdbbind.org.cn/download/PDBbind_v2020_PN.tar.gz -P ./datasets/PN
tar zxvf ./datasets/PN/PDBbind_v2020_PN.tar.gz -C ./datasets
rm ./datasets/PN/PDBbind_v2020_PN.tar.gz

Then process the data:

python scripts/data_process/process_PDBbind_PN.py \
    --index_file ./datasets/PN/index/INDEX_general_PN.2020 \
    --pdb_dir ./datasets/PN \
    --out_dir ./datasets/PN/processed

To get nucleic-acid-ligand data:

wget http://www.pdbbind.org.cn/download/PDBbind_v2020_NL.tar.gz -P ./datasets/NL
tar zxvf ./datasets/NL/PDBbind_v2020_NL.tar.gz -C ./datasets
rm ./datasets/NL/PDBbind_v2020_NL.tar.gz

Then process the data:

python scripts/data_process/process_PDBbind_NL.py \
    --index_file ./datasets/NL/index/INDEX_general_NL.2020 \
    --pdb_dir ./datasets/NL \
    --out_dir ./datasets/NL/processed

Experiments

Protein-Protein Affinity (PPA)

We have provided the script for splitting, training and testing with 3 random seeds:

python scripts/exps/PPA_exps_3.py \
    --pdbbind ./datasets/PPA/processed/PDBbind.pkl \
    --ppab_dir ./datasets/PPA/processed \
    --config ./scripts/exps/configs/PPA/get.json \
    --gpus 0

Ligand Binding Affinity (LBA)

We have provided the script for training and testing with 3 random seeds:

python scripts/exps/exps_3.py \
    --config ./scripts/exps/configs/LBA/get.json \
    --gpus 0

Ligand Efficacy Prediction (LEP)

We have provided the script for training and testing with 3 random seeds:

python scripts/exps/exps_3.py \
    --config ./scripts/exps/configs/LEP/get.json \
    --gpus 0

Universal Learning of PPA and LBA

To enhance the performance on PPA with additional data on LBA:

python scripts/exps/mix_exps_3.py \
    --config ./scripts/exps/configs/MIX/get_ppa.json \
    --gpus 0

To enhance the performance on LBA with additional data on PPA:

python scripts/exps/mix_exps_3.py \
    --config ./scripts/exps/configs/MIX/get_lba.json \
    --gpus 0

PDBBind Benchmark

python scripts/exps/exps_3.py \
    --config ./scripts/exps/configs/PDBBind/identity30_get.json \
    --gpus 0

If you want to use fragment-based representation of small molecules, please replace the config with identity30_get_ps300.json.

Zero-Shot on Nucleic Acid and Ligand Affinity

This experiment needs two 12G GPU (so a total of 2000 vertexes in a batch).

python scripts/exps/NL_zeroshot.py \
    --config scripts/exps/configs/NL/get.json \
    --gpu 1 2