MHC Class II Prediction (bioSkills)

A Claude Code skill that predicts CD4 T-cell epitopes by scoring peptide binding to MHC class II (HLA-DR/DQ/DP) alleles, with explicit handling of the accuracy limits class II carries.


Type	Claude Skill
Supplier	GPTomics bioSkills (community OSS, MIT)
Availability	GA — part of the bioSkills collection
Pricing	Free / OSS (MIT)
Capabilities	Read/Write — Claude runs the skill’s workflow locally (Bash/Python), not as an MCP tool
Verified	works · 2026-07-20
Security	cleared · 2026-07-20 — provenance matches GPTomics/bioSkills, MIT, no advisories, read-only local class II prediction

How to install

bioSkills is not an npm package — skills are plain markdown/code read directly by the agent. Clone the repo, then either run the installer for the whole category or copy the single skill directory.

Claude Code — clone and install via the bundled script:
```
git clone https://github.com/GPTomics/bioSkills
cd bioSkills
./install-claude.sh --categories "immunoinformatics"
```
The installer copies matching skills into ~/.claude/skills/ (default target). Use ./install-claude.sh --list to preview the skills first.
Claude Code / other agents — copy just this one skill:
```
cp -r bioSkills/immunoinformatics/mhc-class-ii-prediction ~/.claude/skills/
```
(run from inside your clone — the previous step left you in bioSkills/; otherwise replace bioSkills/ with the absolute path of your clone). The skill declares its external dependencies (NetMHCIIpan-4.3, MixMHC2pred-2.0) in SKILL.md; install them when prompted on first use.

What it does

Predicts CD4 T-cell epitopes through class II binding with two complementary tools:

NetMHCIIpan-4.3 — pan-allele predictor covering DR, DQ, and DP isotypes; outputs EL scores and optional binding affinity.
MixMHC2pred-2.0 — mass-spec immunopeptidome-grounded motif tool that models the reverse-binding DP mode.

The skill is explicit about why class II is far less reliable than class I: the open binding groove, 9-mer core register ambiguity, sparse/noisy training data, and a DR > DP > DQ accuracy asymmetry. It flags the DQ/DP heterodimer alpha/beta pairing trap (scoring non-existent heterodimers) and recommends looser %Rank thresholds (≤1% strong, ≤5% weak) than class I, treating calls as ranked hypotheses rather than facts.

Primary use cases: CD4 epitope discovery for vaccine T-helper design, class II neoantigen mapping, scoring long peptides against DR/DQ/DP.

Notes

Distributed as a SKILL.md (plus reference material) in the bioSkills collection — Claude executes the workflow locally via Bash/Python rather than as an MCP server. The upstream skill front-matter name is bio-immunoinformatics-mhc-class-ii-prediction; if you invoke it as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /mhc-class-ii-prediction. For CD8/class I binding, use the mhc-binding-prediction skill instead. NetMHCIIpan and MixMHC2pred require a separate (free, academic) download/registration from their vendors. Upstream directory: immunoinformatics/mhc-class-ii-prediction.

Sources

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