AL
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alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences f…
FreedomIntelligence/OpenClaw-Medical-Skills查看
数据与复现
BI
bio-immunoinformatics-epitope-prediction
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic peptides.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-immunoinformatics-epitope-prediction
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Python：IEDB API ，用于 B-cell epitope prediction (BepiPred)。
- Python：mhcflurry ，用于 T-cell epitope MHC binding prediction。
- 预测 B-cell 、 T-cell epitopes in my protein" → Identify immunogenic regions in antigens ，用于 vaccine design ，使用 sequence-based 、 structure-based prediction tools. Python：IEDB API ，用于 B-cell epitope prediction (BepiPred) Python：mhcflurry ，用于 T-cell epitope MHC binding prediction。
- Goal：Predict linear B-cell epitopes ，面向 protein sequence ，使用 IEDB prediction tools。
- Approach：Submit sequence to IEDB B-cell prediction API ，支持 selectable method (BepiPred-2.0 recommended) 、 parse tab-separated results。
原始文档
SKILL.md 摘录
T-Cell Epitope Prediction
Goal: Predict T-cell epitopes by MHC-I binding across multiple HLA alleles.
Approach: Query IEDB MHC-I API for each allele-sequence combination and aggregate predictions.
Linear vs Conformational Epitopes
Goal: Classify epitopes as linear (continuous) or conformational (discontinuous) and predict structure-based epitopes.
Approach: Distinguish by residue continuity in primary sequence; for conformational epitopes, use structure-based tools (DiscoTope, ElliPro) via web servers.
Combine Multiple Predictions
Goal: Improve epitope prediction reliability by combining multiple methods into a consensus score.
Approach: Run each method independently, threshold per method, then count agreements per position and assign confidence levels.
适用场景
- 适合在designing vaccines，mapping antibody binding sites，或 predicting immunogenic peptides时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- immunoinformatics/mhc-binding-prediction - T-cell epitope prediction
- immunoinformatics/immunogenicity-scoring - Epitope ranking
- structural-biology/geometric-analysis - Structure-based epitopes
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