Structure Validation (bioSkills)

A Claude Code skill that judges whether a macromolecular model — or one specific region of it — is trustworthy enough to dock against, measure, or reason about mechanistically.


Type	Claude Skill
Supplier	GPTomics bioSkills (community OSS, MIT)
Availability	GA — part of the bioSkills collection
Pricing	Free / OSS (MIT) — Biopython, Phenix/MolProbity and DSSP are separately installed (Phenix is free for academic use under its own licence)
Capabilities	Read/Write — Claude runs the skill’s workflow locally (Bash/Python), not as an MCP tool
Verified	works · 2026-08-06
Security	caution · 2026-08-06 — GPTomics/bioSkills MIT, but bundled phenix.molprobity is academic-use-only

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 "structural-biology"
```
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/structural-biology/structure-validation ~/.claude/skills/
```
(run from inside your clone — the previous step left you in bioSkills/; otherwise replace bioSkills/ with the absolute path of your clone).

What it does

Separates global model quality from local, per-region reliability, which is the distinction that actually determines whether a measurement is safe:

Experimental metadata — pulls resolution, R-work, R-free and the experimental method from the mmCIF header (MMCIF2Dict), and reads the R-free-minus-R-work gap as an overfitting signal rather than looking at R-work alone.
B-factor screening — flags high-B residues using within-structure statistics (median / MAD z-scoring) instead of an absolute cutoff, because B-factors are only comparable inside one structure.
Geometry outliers — clashscore, Ramachandran and rotamer outliers, and cis non-proline peptides; coarse checks run in Bio.PDB, with phenix.molprobity as the authoritative validator.
Predicted models — validates AlphaFold/ESMFold output through pLDDT confidence bands and PAE before docking or molecular replacement, and can trim low-confidence regions and split PAE domains with phenix.process_predicted_model.
Method-specific reading — cryo-EM global vs local resolution (FSC 0.143 half-map vs 0.5 map-model) and NMR ensemble spread as a disorder signal.
Components — Bio.PDB (primary), phenix.molprobity and phenix.process_predicted_model (CLI), DSSP/mkdssp, NumPy.

Primary use cases: pre-docking receptor triage, deciding whether a specific loop or side chain supports a mechanistic claim, vetting a predicted model before downstream use.

Notes

Distributed as a SKILL.md (plus reference material) in the bioSkills collection — Claude executes the workflow locally rather than as an MCP server. The upstream skill front-matter name is bio-structural-biology-structure-validation; if invoked as a namespaced plugin command it resolves under the bioSkills plugin, not as a bare /structure-validation. MolProbity-grade validation requires a local Phenix installation; without it the skill falls back to the Bio.PDB coarse geometry checks and says so. Natural upstream of the catalogued Structure Preparation skill and of docking entries such as AutoDock Vina and smina; pairs with AlphaFold MCP Server for the predicted-model case. Upstream directory: structural-biology/structure-validation.

Sources

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