README.md

metadata

title: B-cell Epitope Prediction Server
emoji: 🧬
colorFrom: blue
colorTo: purple
python_version: '3.10'
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: true
license: mit

🧬 B-cell Epitope Prediction Server

A web-based interface for B-cell epitope prediction using the RoBep model.

📋 How to Use

1. Input Protein Structure

Choose one of two input methods:

Option A: PDB ID

• Enter a 4-character PDB ID (e.g., "5I9Q")
• Specify the chain ID (e.g., "A")

Option B: Upload PDB File

• Upload a PDB structure file (.pdb or .ent format)
• Optionally specify a custom PDB ID
• Specify the chain ID

2. Configure Prediction Parameters

Basic Parameters:

• Chain ID: Target protein chain (default: A)

Advanced Parameters (Optional):

• Radius: Spherical region radius in Ångstroms (default: 18.0)
• Top-k Regions: Number of top regions to analyze (default: 7)
• Encoder: Protein encoder type (ESM-C only now)
• Device Configuration: CPU or GPU processing (CPU Only now)
• Threshold: Custom prediction threshold (leave empty for auto, required)

3. View Results

The application provides:

Prediction Summary

• Protein information (PDB ID, chain, length, sequence)
• Prediction statistics (epitope count, coverage rate, etc.)
• Top-k region centers
• Predicted epitope residues
• Binding region residues

Download Options

• JSON Results: Complete prediction data with metadata
• CSV Results: Residue-level predictions for analysis
• 3D Visualization: Interactive HTML file with 3Dmol.js viewer

4. 3D Visualization

The downloadable HTML file includes:

• Display Modes:
  • Predicted Epitopes: Highlight predicted epitope residues
  • Probability Gradient: Color residues by prediction confidence
• Representation Styles: Cartoon, Surface, Stick, Sphere
• Interactive Controls: Rotate, zoom, pan, reset view, save image

Cite this work

If you want to cite this work, please cite:

@article{Xu2026RoBep,
  author  = {Xu, Yitao and Wei, Guanyun and Zhou, Jingying and Huang, Yuanhua and Yu, Weichuan and Lin, Zhixiang and Liu, Ran and Fan, Xiaodan},
  title   = {{RoBep}: A Region-Oriented Deep Learning Model for B-Cell Epitope Prediction},
  journal = {Bioinformatics},
  year    = {2026},
  doi     = {10.1093/bioinformatics/btag006}
}

or

• Yitao Xu, Guanyun Wei, Jingying Zhou, Yuanhua Huang, Weichua Yu, Zhixiang Lin, Ran Liu, Xiaodan Fan, RoBep: A Region-Oriented Deep Learning Model for B-Cell Epitope Prediction, Bioinformatics, 2026;, btag006, https://doi.org/10.1093/bioinformatics/btag006

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Note: This is a research tool for B-cell epitope prediction. Results should be validated through experimental methods for clinical or commercial applications.