AL
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alphafold
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数据与复现
BI
bio-immunoinformatics-mhc-binding-prediction
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-immunoinformatics-mhc-binding-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：mhcflurry.Class1PresentationPredictor().predict() ，用于 MHC-I。
- CLI：netMHCpan ，用于 alternative MHC-I/II predictions。
- 预测 which peptides bind to MHC" → Predict peptide-MHC class I 、 II binding affinity ，使用 neural network models to identify potential T-cell epitopes ，面向 protein sequences. Python：mhcflurry.Class1PresentationPredictor().predict() ，用于 MHC-I CLI：netMHCpan ，用于 alternative MHC-I/II predictions。
- mhcflurry-downloads fetch。
原始文档
SKILL.md 摘录
MHCflurry Setup
Goal: Install MHCflurry and download pre-trained prediction models.
Approach: Install via pip and fetch model weights for class I pan-allele or specific allele predictions.
## Download models for specific alleles
mhcflurry-downloads fetch models_class1_pan
MHCflurry Python API
Goal: Predict peptide-MHC binding affinity and presentation scores for a set of peptides.
Approach: Load the Class1PresentationPredictor and call predict() with peptide-allele pairs to obtain IC50, percentile rank, and presentation scores.
from mhcflurry import Class1PresentationPredictor
适用场景
- 适合在predicting MHC binding ，用于 vaccine design 或 neoantigen identification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/neoantigen-prediction - Tumor neoantigen discovery
- immunoinformatics/epitope-prediction - B-cell epitope prediction
- clinical-databases/hla-typing - Determine patient HLA type
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