EquiScore is a generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling

Paper on BioRXiv

Implementation of EquiScore, by Duanhua Cao 😊.

🔔 News

Some bugs(🐛 🐛) have been fixed, and bash commands are further provided to help users unfamiliar with python quickly use EquiScore for virtual screening 😃

This repository contains all code, instructions and model weights necessary to screen compounds by EquiScore, eval EquiScore or to retrain a new model.

If you have any question, feel free to open an issue or reach out to us: [email protected] ✉️.

Framework

Dataset

If you want to train one of our models with the PDBscreen data you should do:

1. download Preprocessed PDBscreen data from zenodo
2. uncompress the directory by tar command and place it into data such that you have the path /EquiScore/data/training_data/PDBscreen
3. see retraining EquiScore part for details.

Setup Environment

We recommend setting up the environment using Anaconda.

Clone the current repo

git clone [email protected]:CAODH/EquiScore.git

This is an example for how to set up a working conda environment to run the code (but make sure to use the correct pytorch, DGL, cuda versions or cpu only versions):

conda create --name EquiScore python=3.8 conda activate EquiScore

Through our testing, the relevant environment can be successfully installed by executing the following commands in sequence:

conda install pytorch==1.11.0 cudatoolkit=11.3 -c pytorch

conda install -c dglteam dgl-cuda11.1

conda install -c conda-forge rdkit

conda install -c conda-forge biopython

conda install -c conda-forge scikit-learn

conda install -c conda-forge prolif

pip install prefetch-generator

pip install lmdb

pip install numpy==1.22.3

or you can download the conda-packed file zenodo, and then unzip it in ${anaconda install dir}/anaconda3/envs/EquiScore. ${anaconda install dir} represents the dir where the anaconda is installed. For me, ${anaconda install dir}=/root.

mkdir ${anaconda install dir}/anaconda3/envs/EquiScore

tar -xzvf EquiScore.tar.gz -C ${anaconda install dir}/anaconda3/envs/EquiScore

conda activate EquiScore

after enter the EquiScore env: run

conda-unpack

Using the provided model weights to screen a compound for your target protein

We implemented a Screening.py python script, to help anyone want to screen Hits from a compound library.

We provide a toy example under the ./data/sample_data folder for illustration.

🚀just some steps needed

1. Docking compounds with target protein to get docking pose, EquiScore is robust to pose sources and you can choose any method you are familiar with to generate poses(Glide,Vina,Surflex,Gold,LeDock), or you can try a deep learning method.

2. Assume that you have obtained the results of the docking in the previous step. Then, get pocket region and compound pose. run script:

   python ./get_pocket/get_pocket.py --docking_result ./data/sample_data/sample_compounds.sdf --recptor_pdb ./data/sample_data/sample_protein.pdb --single_sdf_save_path ./data/sample_data/tmp_sdfs --pocket_save_dir ./data/sample_data/tmp_pockets

   or use bash command script in bash_scripts dir: You just need to replace the corresponding parameters

   cd ~/EquiScore/bash_scripts bash Getpocket.sh

3. Then, you have all data to predict protein-ligand interaction by EquiScore! Be patient. This is the last step!

   python Screening.py --ngpu 1 --test --test_path ./data/sample_data/ --test_name tmp_pockets --pred_save_path ./data/test_results/EquiScore_pred_for_tmp_pockets.csv

   or use bash command script in bash_scripts dir: You just need to replace the corresponding parameter

   cd ~/EquiScore/bash_scripts bash Screening.sh

4. Until now, you get all prediction result in pred_save_path!

Using the provided model weights for evaluation and Reproduces the benchmark result

Just like screen compounds for a target, benchmark dataset have many targets for screening, so we implemented a script to calculate the results

just some steps needed

1. We provided Preprocessed pockets on zenodo (download pockets from zenodo). IF YOU WANT GET RAW DATASET PLEASE DOWNLOAD RAW DATA FROM REFERENCE PAPERS.

2. you need to download the Preprocessed dataset and extract data to ./data/external_test_data.(for example, all pockets in DEKOIS2.0 docking by Glide SP should be extract into one dir like ./data/external_test_data/dekois2_pocket)

3. if you want to preprocessed data to get pocket , all pocket file names should contain '_active' for active ligand,'_decoy' for decoys and all pockets in a dir for one benchmark dataset

4. run script (You can use the nohup command and output redirects as you normally like):

   python Independent_test.py --test --test_path ./data/external_test_data --test_name dekois2_pocket --test_mode multi_pose

   the result will be saved in ~/EquiScore/workdir/official_weight/

   or use bash command script in bash_scripts dir: You just need to replace the corresponding parameter

   cd ~/EquiScore/bash_scripts bash Benchmark_test.sh

   use multi_pose arg if one ligand have multi pose and set pose_num and idx_style in args ，see args --help for more details

Retraining EquiScore 🤖 Model

Retraining EquiScore or fine tune your model is also very simple!

1. you need to download the traing dataset , and extract pocket data to ./data/training_data/PDBscreen (You can also use your own private data, As long as it can fit to EquiScore after processing)

2. use uniprot id to deduplicated data and split data in ./data/data_splits/screen_model/data_split_for_training.py in this script, will help deduplicated dataset by uniprot id and split train/val data and save data path into a pkl file (like "train_keys.pkl, val_keys.pkl, test_keys.pkl").

3. run train.py script:

   python Train.py --ngpu 1 --train_keys your_keys_path --val_keys your_keys_path --test_keys your_keys_path --test

   or use bash command script in bash_scripts dir: You just need to replace the corresponding parameter

   cd ~/EquiScore/bash_scripts bash Training.sh

   (In the first round of training, data is processed and saved, so it may be slower, depending on hardware conditions OR If you wish to expedite the training process, please refer to the preprocessing workflow in dataset.py, save the data to the LMDB database, and then specify the LMDB path in the training script by adding --lmdb_cache lmdb_cache_path to replace --test like we did in bash command )

Citation

Cao D, Chen G, Jiang J, et al. EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling[J]. bioRxiv, 2023: 2023.06. 18.545464. doi: https://doi.org/10.1101/2023.06.18.545464

License

MIT