bio-immunoinformatics-tcr-epitope-binding
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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-tcr-epitope-binding --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MiXCR 4.6+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # TCR-Epitope Binding **"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 ## ERGO-II Model ```python # ERGO-II uses deep learning to predict TCR-epitope binding # GitHub: https://github.com/IdoSpringer/ERGO-II def setup_ergo(): '''Setup ERGO-II for TCR-epitope prediction Requirements: - PyTorch - Pre-trained models from ERGO-II repository ERGO-II features: - Uses both CDR3 alpha and beta chains - Incorporates MHC context - Trained on VDJdb and IEDB data ''' print('ERGO-II setup:') print('1. Clone: git clone https://github.com/IdoSpringer/ERGO-II') print('2. Install: pip insta
- Version Compatibility
- ERGO-II Model
- TCR Input Format
- Predict TCR-Epitope Binding
- Match TCRs to Known Epitopes
- TCR Clustering
- Analyze Repertoire Specificity
- Related Skills
What does the bio-immunoinformatics-tcr-epitope-binding skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-tcr-epitope-binding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.