Implementation of enzyme catalytic acitve site prediction with EasIFA
The high-definition images from the manuscript can be found at Manuscript Figures.
Multi-Modal Deep Learning Enables Ultrafast and Accurate Annotation of Enzymatic Active Sites
We have developed a WebServer for EasIFA, which allows you to conveniently annotate the active sites of enzymes you are interested in. The workflow is divided into two logical steps: 1) You can directly upload the PDB structure of the enzyme and the catalyzed reaction equation, 2) Provide the UniProt ID of the enzyme of interest directly.
About 15 minutes for automated deployment.
This repository has been tested on Linux operating systems.
- Python (version >= 3.8)
- PyTorch (version >= 1.12.1)
- RDKit (version >= 2019)
- TorchDrug (version == 0.2.1)
- fair-esm (version == 2.0.1)
- Py3Dmol (version ==2.0.3)
Create a virtual environment to run the code of EasIFA.
It is recommended to use conda to manage the virtual environment.The installation method for conda can be found here.
Make sure to install pytorch with the cuda version that fits your device.
This process usually takes few munites to complete.
git clone https://github.com/wangxr0526/EasIFA.git
cd EasIFA
chmod +x ./setup_EasIFA.sh
./setup_EasIFA.sh
conda activate easifa_env
Open the Jupyter notebook file script/scoring_results.ipynb, follow the links within to download the result files and place them in the corresponding paths. Run the cells sequentially to obtain the results reported in the paper.
Running the following command can download the model's checkpoints and datasets (including the PDB structures in the dataset).
python download_data.py
The links correspond to the paths of the zip files as follows:
https://drive.google.com/uc?id=1ra11M4PpIalKx9ZZP-mrgj13IuFakjz3 ---> checkpoints.zip (14Gb)
https://drive.google.com/uc?id=15c-KoZ47TpF9_qyQfJiY67gcgVZ8N5WR ---> dataset.zip
Test in the SwissProt E-RXN ASA dataset:
Active site position prediction task:
EasIFA-ESM-bin:
python main_test.py --gpu CUDA_ID \
--task_type active-site-position-prediction \
--dataset_path dataset/ec_site_dataset/uniprot_ecreact_cluster_split_merge_dataset_limit_100 \
--checkpoint checkpoints/enzyme_site_predition_model/train_in_uniprot_ecreact_cluster_split_merge_dataset_limit_100_at_2024-05-24-02-53-35/global_step_92000
EasIFA-SaProt-bin:
python main_test_saprot.py --gpu CUDA_ID \
--task_type active-site-position-prediction \
--dataset_path dataset/ec_site_dataset/uniprot_ecreact_cluster_split_merge_dataset_limit_100 \
--checkpoint checkpoints/enzyme_site_prediction_saprod_embding_model/train_in_uniprot_ecreact_cluster_split_merge_dataset_limit_100_at_2024-05-16-10-25-16/global_step_14000
EasIFA-NG-bin:
python main_test.py --gpu CUDA_ID \
--task_type ablation-experiment-3 \
--dataset_path dataset/ec_site_dataset/uniprot_ecreact_cluster_split_merge_dataset_limit_100 \
--checkpoint checkpoints/enzyme_site_no_gearnet_prediction_model/train_in_uniprot_ecreact_cluster_split_merge_dataset_limit_100_at_2024-05-20-05-13-33/global_step_24000
Active site categorie prediction task
EasIFA-ESM-multi:
python main_test.py --gpu CUDA_ID \
--task_type active-site-categorie-prediction \
--dataset_path dataset/ec_site_dataset/uniprot_ecreact_cluster_split_merge_dataset_limit_100 \
--checkpoint checkpoints/enzyme_site_type_predition_model/train_in_uniprot_ecreact_cluster_split_merge_dataset_limit_100_at_2024-05-26-02-48-38/global_step_86000
EasIFA-SaProt-multi:
python main_test_saprot.py --gpu CUDA_ID \
--task_type active-site-categorie-prediction \
--dataset_path dataset/ec_site_dataset/uniprot_ecreact_cluster_split_merge_dataset_limit_100 \
--checkpoint checkpoints/enzyme_site_type_prediction_saprod_embding_model/train_in_uniprot_ecreact_cluster_split_merge_dataset_limit_100_at_2024-05-19-20-00-00/global_step_72000
Test in the MCSA E-RXN CSA dataset:
EasIFA-ESM-bin
python test_knowledge_transfer_learning.py --gpu CUDA_ID \
--dataset_path dataset/mcsa_fine_tune/normal_mcsa \
--structure_path dataset/mcsa_fine_tune/structures \
--checkpoint checkpoints/enzyme_site_predition_model_finetune_with_mcsa/train_in_normal_mcsa_at_2023-10-06-09-48-04/global_step_37200
EasIFA-SaProt-bin
python test_knowledge_transfer_learning.py --gpu CUDA_ID \
--dataset_path dataset/mcsa_fine_tune/normal_mcsa \
--structure_path dataset/mcsa_fine_tune/structures \
--checkpoint checkpoints/enzyme_site_predition_saprot_embding_model_finetune_with_mcsa/train_in_normal_mcsa_at_2024-05-23-08-05-31/global_step_19400 \
--use_saprot
@article{wang_easifa_2024,
title = {Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites},
volume = {15},
issn = {2041-1723},
url = {https://www.nature.com/articles/s41467-024-51511-6},
doi = {10.1038/s41467-024-51511-6},
urldate = {2024-08-27},
journal = {Nature Communications},
author = {Wang, Xiaorui and Yin, Xiaodan and Jiang, Dejun and Zhao, Huifeng and Wu, Zhenxing and Zhang, Odin and Wang, Jike and Li, Yuquan and Deng, Yafeng and Liu, Huanxiang and Luo, Pei and Han, Yuqiang and Hou, Tingjun and Yao, Xiaojun and Hsieh, Chang-Yu},
month = aug,
year = {2024},
pages = {7348},
}