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