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https://github.com/lirongwu/mape-ppi
Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"
https://github.com/lirongwu/mape-ppi
codebook masked-modeling microenvironment ppi-networks protein-embedding protein-protein-interaction protein-representation-learning vocabulary
Last synced: 5 days ago
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Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"
- Host: GitHub
- URL: https://github.com/lirongwu/mape-ppi
- Owner: LirongWu
- License: mit
- Created: 2024-01-24T03:09:56.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-20T08:46:28.000Z (7 months ago)
- Last Synced: 2025-01-08T03:18:43.747Z (13 days ago)
- Topics: codebook, masked-modeling, microenvironment, ppi-networks, protein-embedding, protein-protein-interaction, protein-representation-learning, vocabulary
- Language: Python
- Homepage:
- Size: 6.81 MB
- Stars: 248
- Watchers: 2
- Forks: 47
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MAPE-PPI
**MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding (Spotlight)**
Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V Chawla, Stan Z. Li. In [ICLR](https://openreview.net/forum?id=itGkF993gz), 2024.
## Dependencies
```
conda env create -f environment.yml
conda activate MAPE-PPI
```
The default PyTorch version is 2.0.0 and cudatoolkit version is 11.7. They can be changed in `environment.yml`.
## Dataset
**Raw data** of the three datasets (SHS27k, SHS148k, and STRING) can be downloaded from the [Google Drive](https://drive.google.com/file/d/1hJVrQXddB9JK68z7jlIcLfd9AmTWwgJr/view?usp=sharing):
* `protein.STRING.sequences.dictionary.tsv` Protein sequences of STRING
* `protein.actions.STRING.txt` PPI network of STRING
* `STRING_AF2DB` PDB files of protein structures predicted by AlphaFold2
Pre-process raw data to generate feature and adjacency matrices (also applicable to any new dataset):
```
python ./raw_data/data_process.py --dataset data_name
```
where `data_name` is one of the three datasets (SHS27k, SHS148k, and STRING).
For ease of use, we have pre-processed these three datasets and placed the **processed data** in [Google Drive](https://drive.google.com/file/d/16JkdWJ92jy_kVGc92ngXlLKbcSLAPu0P/view?usp=sharing).
To use the processed data, please put them in `./data/processed_data/.
## Usage
### Pre-training and Inference on SHS27k/SHS148k/STRING
```
python -B train.py --dataset STRING --split_mode bfs
```
The hyperparameters customized for each dataset and data partitions are available in `./configs/param_config.json`.
### Pre-training on additional data, Inference on SHS27k/SHS148k/STRING
To pre-train with customized data (e.g., CATH or AlphaFoldDB datasets), please refer to the following steps:
(1) Download additional pre-training data (including their PDF files) from the official website.
(2) Pre-process pre-training PDB files as done in `./raw_data/data_process.py` and transform into three files:
* `protein.nodes.pretrain_data.pt`
* `protein.rball.edges.pretrain_data.npy`
* `protein.knn.edges.pretrain_data.npy`
where `pretrain_data` is the name of the additional pre-training dataset.
(3) Load pre-processed data and perform pretraining on it, running
```
python -B train.py --dataset STRING --split_mode bfs --pre_train pretrain_data
```
### Loading the pre-trained model and Inference on SHS27k/SHS148k/STRING
We provide a pre-trained model in `./trained_model/` for PPI prediction on STRING. To use it, please run
```
python -B train.py --dataset STRING --split_mode bfs --ckpt_path ../trained_model/vae_model.ckpt
```
## Citation
If you are interested in our repository and our paper, please cite the following paper:
```
@article{wu2024mape,
title={MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding},
author={Wu, Lirong and Tian, Yijun and Huang, Yufei and Li, Siyuan and Lin, Haitao and Chawla, Nitesh V and Li, Stan Z},
journal={arXiv preprint arXiv:2402.14391},
year={2024}
}
```
## Feedback
If you have any issue about this work, please feel free to contact me by email:
* Lirong Wu: [email protected]