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syedkumailhussain/
ProPepX

Name: ProPepX
Availability: InStock

bioinformatics,

computational-biology,

protein-binding-site,

peptide-binding-site,

residue-level-prediction,

protein-language-model,

token-classification

other

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ProPepX

ProPepX is a unified deep learning framework for residue-level protein–peptide binding-site prediction. The framework introduces interaction-aware transfer learning with bidirectional cross-attention to jointly model protein and peptide representations, enabling accurate prediction of binding residues on both interaction partners.

ProPepX provides pretrained checkpoints for protein-side, peptide-side, joint, and zero-shot prediction using ProtTransT5 and ESM-3 (600M) protein language models.

Model Variants

Task	ProtTransT5	ESM-3 (600M)
Protein-side prediction	✓	✓
Peptide-side prediction	✓	✓
Joint prediction	✓	✓
Zero-shot prediction	✓	✓

Quick Inference

After downloading the corresponding checkpoint and embeddings, run:

bash

python propepx_predict.py \
    --protein "<PROTEIN_SEQUENCE>" \
    --peptide "<PEPTIDE_SEQUENCE>" \
    --embedding {prottrans|esm} \
    --mode {prot|pep|mode-GLOBAL|zero-shot} \
    --dataset <DATASET_NAME> \
    --save_html results/

Example output:

results/
├── prediction_report.html
├── protein_binding_scores.csv
├── peptide_binding_scores.csv
├── interaction_heatmap.png
└── summary.json

Resources

Resource	Link
🌐 GitHub Repository	https://github.com/SyedKumailHussainNaqvi/ProPepX
🖥️ Interactive Web Server	https://syedkumailhussainnaqvi.github.io/ProPepX/
📚 Documentation	https://github.com/SyedKumailHussainNaqvi/ProPepX/tree/main/docs

Intended Use

ProPepX is designed for research applications in:

Protein–peptide interaction prediction
Residue-level binding-site prediction
Structural bioinformatics
Computational biology
AI-assisted protein engineering

The models are intended for research use only and should be experimentally validated before downstream biological interpretation.

Citation

If you use ProPepX in your research, please cite:

bibtex

@article{ProPepX2026,
  title={ProPepX: A unified, interpretable, bidirectional interaction-aware transfer-learning framework for residue-level protein–peptide binding-site prediction},
  author={Syed Kumail Hussain Naqvi and Sourav Chandra et al.},
  journal={Nature Machine Intelligence},
  year={2026},
  note={Manuscript under review}
}

Contact

Syed Kumail Hussain Naqvi
Department of Physical-AI Convergence Engineering
Jeonbuk National University, Republic of Korea

📧 syedkumailhussainnaqvi@jbnu.ac.kr

syedkumailhussain/ProPepX