Multi-Scale Protein Sequence-Structure Contrasting for Compound-Protein Interaction Prediction (PSC-CPI)

This is a PyTorch implementation of Protein Sequence-sructure Contrasting for CPI Prediction (PSC-CPI), and the code includes the following modules:

• Dataset loader (train/val/test)

• Four evaluation settings: Seen-Both, Unseen-Compound, Unseen-Protein, and Unseen-Both

• Four evaluation metrics: CPI pattern prediction (AUPRC and AUROC) and CPI Strength Prediction (RMSE and PPCs)

• Pre-training, fine-tuning, and inference paradigm

Main Requirements

• numpy==1.21.6
• scipy==1.7.3
• torch==1.6.0
• sklearn == 1.0.2

Dataset

The datasets used in this paper are available in:

https://drive.google.com/file/d/1_iZ8B1JZkCKmKlQNewOCr3kbnWfAIc-r/view?usp=sharing

Description

• train.py

  • Pre-training, fine-tuning, and inference

• models.py

  • ProteinEmbed_Model() - Learning protein sequence and structure representations
    • prot_data_aug() -- Data augmentation on proteins
    • loss_inter() -- loss for CPI pattern prediction
    • loss_affn() -- loss for CPI strength prediction
    • loss_contras() -- loss for (pre-training) multi-scale contrastive learning

• dataloader.py

  • data_loader() -- Load train, val, and test data (with four evaluation data spilts)

• utils.py

  • set_seed() -- Set radom seeds for reproducible results
  • cal_affinity_torch() -- Use Pytorch to calculate CPI affinity (RMSE and PPCs)
  • cal_interaction_torch() -- Use Pytorch to calculate CPI pattern (AUPRC and AUROC)

Running the code

1. Install the required dependency packages

2. To pre-train and fine-tune the model, please run with proper hyperparameters:

python train.py --task_mode 0 --modality seq_str_linear --pre-train 1 --seq_encoder HRNN --str_encoder GAT

where (1) task_mode is one of the two CPI tasks: 0 (Strength Prediction) and 1 (Pattern Prediction); (2) modality is one of the three inference settings: 'seq_str_linear' (both two modalities are provided), 'sequence' (only sequence is provided), and 'structure' (only structure is provided); (3) pre-train denotes whether the pre-training is conducted: 0 (w/o pre-training) and 1 (w/ pre-training); (4) seq_encoder is one of the four protein sequence encoders: HRNN, LSTM, bi-LSTM, and Transformer; and (5) str_encoder is one of the three protein structure encoders: GCN, GAT, and SAGE.

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@article{wu2024psc,
  title={Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction},
  author={Wu, Lirong and Huang, Yufei and Tan, Cheng and Gao, Zhangyang and Hu, Bozhen and Lin, Haitao and Liu, Zicheng and Li, Stan Z},
  journal={arXiv preprint arXiv:2402.08198},
  year={2024}
}