A newer version of the Gradio SDK is available:
6.1.0
metadata
title: Antibody Non-Specificity Predictor
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.0.0
app_file: app.py
pinned: false
license: mit
tags:
- antibody
- protein
- ESM
- gradio
- polyreactivity
- machine-learning
🧬 Antibody Non-Specificity Predictor
Predict antibody polyreactivity (non-specificity) from Variable Heavy (VH) or Variable Light (VL) sequences using ESM-1v protein language models.
Model
- Architecture: ESM-1v (650M parameters) + Logistic Regression
- Training Data: Boughter dataset (914 antibodies, ELISA polyreactivity)
- Methodology: Sakhnini et al. (2025) - Prediction of Antibody Non-Specificity using PLMs
Usage
- Paste your antibody VH or VL amino acid sequence
- Click "🔬 Predict Non-Specificity"
- Get prediction (specific vs non-specific) + probability
Supported Input
- Valid characters: Standard amino acids (ACDEFGHIKLMNPQRSTVWY)
- Max length: 2000 amino acids
- Auto-cleaning: Lowercase automatically converted to uppercase
Examples
The app includes example sequences:
- Standard VH (128aa)
- Standard VL (107aa)
- Short VH (Herceptin-like)
Citation
If you use this tool in your research, please cite:
@article{sakhnini2025antibody,
title={Prediction of Antibody Non-Specificity using Protein Language Models},
author={Sakhnini, et al.},
year={2025}
}
Repository
Full source code: antibody_training_pipeline_ESM
License
MIT License - See repository for details