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bio-immunoinformatics-tcr-epitope-binding
Maintainer FreedomIntelligence · Last updated April 1, 2026
Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-immunoinformatics-tcr-epitope-binding
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Python: ERGO-II model for TCR-epitope binding prediction.
- Predict which epitopes my TCRs recognize" → Match T-cell receptors to their cognate epitopes using deep learning models for TCR antigen specificity prediction. Python: ERGO-II model for TCR-epitope binding prediction.
- PyTorch.
- Pre-trained models from ERGO-II repository.
- Uses both CDR3 alpha and beta chains.
Source Doc
Excerpt From SKILL.md
TCR Clustering
Goal: Group TCRs that likely recognize the same epitope based on CDR3 sequence similarity, enabling specificity group discovery from large repertoire datasets.
Approach: Compute pairwise Levenshtein distances between CDR3 sequences, apply hierarchical clustering with average linkage, and cut the dendrogram at a maximum edit distance threshold to define specificity groups.
Use cases
- Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- tcr-bcr-analysis/mixcr-analysis - TCR repertoire sequencing analysis
- immunoinformatics/mhc-binding-prediction - Epitope context
- single-cell/clustering - Single-cell TCR analysis
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