PepMCP

a peptide-specific MCP predictor using GraphSAGE

PepMCP server

Requirements

ESMC environment

• python == 3.12
• pytorch == 2.7
• esm
• transformers

PepMCP environment

• pytorch
• transformers
• dgl
• sklearn (for training evaluation)

Usage

Prediction

Test sequences and names are in ./data/test.csv

python ESMC_extract.py  # ESMC environment
python predict.py       # PepMCP environment

Training

Data processing

(This step can be skipped for using 5-fold datasets in ./data/res_split or ./data/seq_split directly)

python preprocess.py
python ESMC_extract.py   # change file name to MemAMPs.csv or pdb_sol_neg.txt, under ESMC environment

Run

python train.py

Citation

R. Dong, T. Awang, Q. Cao, K. Kang, L. Wang, Z. Zhu, and C. Song. A Graph-Based Membrane Contact Probability Predictor for Membrane-Lytic Antimicrobial Peptides, 2026.