alphafold-structure-analysis

name	alphafold-structure-analysis
description	Retrieve an AlphaFold predicted structure from the AlphaFold Database for a given UniProt accession and analyze its confidence (pLDDT), intrinsically disordered regions, and rigid domain boundaries (PAE). Use when the user has a UniProt ID and wants structural confidence, disorder assessment, or domain layout. Public data, no credential needed.
license	Apache-2.0

AlphaFold Database Fetch and Analyze (Codex-native)

Codex-native adaptation of Google DeepMind's alphafold-database-fetch-and-analyze skill.

Attribution: adapted from google-deepmind/science-skills (Apache-2.0 + CC-BY-4.0). AFDB terms: https://alphafold.ebi.ac.uk/

When to use / not use

• Use when the user provides a UniProt accession (e.g. P04637) and wants confidence metrics, disorder, or domain boundaries.
• Do not use when the user has only a protein/gene name or a raw sequence (ask them to find the UniProt ID first), wants to search structural homologs (use $cx-foldseek-structural-search), needs an experimental structure (use a PDB skill), or wants to run a new folding prediction.

Gates (ask before proceeding)

• Terms notice: on first use in a workspace, tell the user to review the AFDB terms, and record the notice in provenance.
• Network / install: fetching from AFDB is read-only public HTTP. Ask before installing any analysis package; run Python via uv run, not bare python3.

Workflow

Fetch current model metadata first with science_search_alphafold using a canonical UniProt accession. Prefer its returned versioned URLs over guessing a model version.

1. Fetch the structure for the UniProt ID from the AlphaFold Database: the mmCIF model, the Predicted Aligned Error (PAE) JSON, and the entry metadata JSON. Endpoint pattern (v6 example): https://alphafold.ebi.ac.uk/files/AF-<ACC>-F1-model_v6.cif and the matching -predicted_aligned_error_v6.json; entry metadata via the AFDB API https://alphafold.ebi.ac.uk/api/prediction/<ACC>. Save all files to an output folder under the user's project (absolute path), rate-limited and polite. For very large proteins handle fragment fallback (-F2, ...).
2. Analyze pLDDT from the per-residue confidence (embedded in the mmCIF B-factor column, and in metadata). Report the global mean and the fraction in each band: very low (<50), low (50-70), confident (70-90), very high (>90).
3. Analyze domains / PAE. Detect rigid domain boundaries with a sliding-window heuristic over the PAE matrix; report the number of distinct domains and their residue ranges. Do not eyeball domains from coordinates — derive them from PAE.
4. Synthesize one cohesive summary: overall fold confidence, disordered regions, and rigid-domain layout, plus supporting metrics.
5. Mandatory warnings:
   • If no canonical entry was found and an isoform/fragment was used, or the protein is very large (>2700 aa), relay that prominently.
   • If the protein is largely disordered (high <50 fraction / no rigid domains), warn against whole-protein downstream analysis (Foldseek, docking); if small ordered domains exist, advise restricting analysis to those residue ranges.
6. Remind the user that per-residue pLDDT lives in the mmCIF B-factor column.
7. Provenance & review: record the accession, file versions, and outputs with $science-provenance; check claims with $science-review.

Boundaries

• pLDDT and PAE are confidence heuristics, not correctness guarantees; a confident fold can still be biologically wrong.
• Report the model version explicitly; AFDB re-releases change coordinates and metrics.