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alphafold-database-fetch-and-analyze — Claude Code skill: AlphaFold

Pulls AlphaFold predicted structures by UniProt ID and works out what they mean: pLDDT confidence, disordered regions, rigid domain boundaries.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add google-deepmind/science-skills --skill alphafold-database-fetch-and-analyze -a claude-code
npx skills add google-deepmind/science-skills --skill alphafold_database_fetch_and_analyze -a claude-code
Open on GitHub

What alphafold-database-fetch-and-analyze does

Give the skill a UniProt Accession ID and it downloads the .cif structure file, the predicted aligned error (PAE) matrix and the API metadata from the AlphaFold Database. Three helper scripts then do the tedious part: per-residue confidence, rigid domain boundaries via a sliding-window PAE heuristic, and an assessment of intrinsic disorder. Very large proteins fall back to fragments.

Everything goes through the wrappers rather than direct database calls, because they enforce the required rate limit for you. The flow is uv run scripts/fetch_structure.py P00520 -o /path/to/output/, then analyze_plddt.py and analyze_pae.py against the saved JSON. Pass an absolute path to -o, or one relative to your project root — a path relative to the skill directory will not work.

You need uv installed and on PATH. The skill also asks you to check the AlphaFold Database terms and drop a .licenses/alphafold_database_fetch_and_analyze_LICENSE.txt file in the workspace root. A protein name, gene name or amino acid sequence is not enough input; look the ID up in UniProt first. Homolog searches belong to Foldseek, experimental structures to RCSB PDB. One thing worth heeding: if the analysis flags heavy disorder, do not run whole-protein docking on it — restrict follow-up work to the ordered domain boundaries the script reports.

What it helps with

  • download an mmCIF structure and PAE matrix for a UniProt ID
  • read pLDDT confidence and see how much of the protein scores very low
  • locate rigid domain boundaries with their residue ranges
  • tell a disordered protein from a structured one before docking
  • get flagged when no canonical entry exists and an isoform was used instead

Example prompts for the agent

  • Fetch the AlphaFold structure for P00520 and walk me through the pLDDT confidence
  • Analyze ./data/AF-P00520-F1-predicted_aligned_error_v6.json and find the domain boundaries
  • Does this protein have disordered regions? UniProt ID P04637

How to install alphafold-database-fetch-and-analyze

1Skills CLI (Vercel)

npx skills add google-deepmind/science-skills --skill alphafold-database-fetch-and-analyze -a claude-code
npx skills add google-deepmind/science-skills --skill alphafold_database_fetch_and_analyze -a claude-code

2Manually: put the files in place

~/.claude/skills/alphafold_database_fetch_and_analyze/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/alphafold_database_fetch_and_analyze/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about alphafold-database-fetch-and-analyze

How do I install alphafold-database-fetch-and-analyze in Claude?

Run npx skills add google-deepmind/science-skills --skill alphafold-database-fetch-and-analyze -a claude-code. The skill then lives at ~/.claude/skills/alphafold_database_fetch_and_analyze/SKILL.md.

What does alphafold-database-fetch-and-analyze do?

It downloads the predicted structure and PAE matrix from the AlphaFold Database for a UniProt ID, then reports pLDDT confidence, disordered regions and rigid domain boundaries.

What do I need before using it?

uv on PATH and a UniProt Accession ID. A protein name, gene name or amino acid sequence will not work — find the ID in UniProt first.

Source text

SKILL.md · Apache-2.0
---
name: alphafold-database-fetch-and-analyze
description: >
  Retrieve and analyze AlphaFold predicted structures for a protein. Use when
  the user provides a specific UniProt Accession ID and wants structural
  confidence metrics (pLDDT), domain boundary analysis, or disorder
  assessment. Do not use if the user only has a protein name, gene name,
  or amino acid sequence — ask for a UniProt ID first.
---

# AlphaFold Database: Fetch and Analyze

## Prerequisites

1.  **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure
    `uv` is installed and on PATH.
2.  **User Notification**: If
    .licenses/alphafold_database_fetch_and_analyze_LICENSE.txt does not already
    exist in the workspace root directory then (1) prominently notify the user
    to check the terms at https://alphafold.ebi.ac.uk/, then (2) create the file
    recording the notification text and timestamp.

## Overview

Downloads AlphaFold predicted structures (mmCIF) and Predicted Aligned Error
(PAE) matrices from the AlphaFold Database for a given UniProt ID, then performs
automated heuristic analysis on structural confidence (pLDDT), intrinsically
disordered regions, rigid domain boundaries, and inter-domain flexibility.

**Do NOT use when:**

-   The user only has a protein name, gene name, or amino acid sequence (no
    UniProt ID) — ask them to look up the ID on
    [UniProt](https://www.uniprot.org).
-   The user wants to search for structural homologs (use **Foldseek**).
-   The user wants to run AlphaFold predictions on a custom sequence.
-   The user needs experimental PDB structures (use **RCSB PDB**).

## Core Rules

-   **Use the Wrapper**: ALWAYS execute the provided helper scripts to query the
    database rather than accessing the database directly. The scripts
    automatically enforce the required rate limit gracefully.
-   Do not attempt to calculate domain boundaries or assess structural disorder
    yourself; always rely on the output provided by the script.
-   If this skill is used, ensure this is mentioned in the output.

## Utility Scripts

**1. Fetch Structure Files**

Downloads the `.cif` structure file, `_predicted_aligned_error.json`, and API
metadata JSON (`-metadata.json`) for a UniProt ID. Handles fragment fallback for
very large proteins.

Examples:

```bash
uv run scripts/fetch_structure.py P00520 -o /path/to/output/
uv run scripts/fetch_structure.py P04637 -o /path/to/custom_results/
```

Always specify `-o` with an absolute path or a path relative to the user's
project root, never a path relative to the skill directory.

**2. Analyze pLDDT Confidence**

Reads pLDDT confidence metrics from a saved AFDB metadata JSON file (produced by
`fetch_structure.py`) and prints a heuristic confidence assessment (structured,
disordered, mixed).

Example:

```bash
uv run scripts/analyze_plddt.py ./data/AF-P00520-F1-metadata.json
```

**3. Analyze PAE / Domain Boundaries**

Reads a downloaded PAE JSON file and detects rigid domain boundaries using a
sliding-window PAE heuristic.

Example:

```bash
uv run scripts/analyze_pae.py ./data/AF-P00520-F1-predicted_aligned_error_v6.json
```

## Interpreting the Output

The script prints analysis to stdout. Read it carefully and synthesize the
results for the user:

1.  **Isoform / Large Protein Warning (MANDATORY):** Check the script output for
    any `[!] WARNING` lines. If the script reports that no canonical entry was
    found and an isoform was used, or if the protein is very large (>2700 AAs),
    you **MUST** prominently relay this warning to the user. Do not omit this
    warning.
2.  **Synthesize the Structural Analysis**: Combine the "pLDDT Conclusion" and
    the "PAE Structural Conclusion" into a single, cohesive overall summary.
    Describe the protein's overall folding confidence, the presence of
    disordered regions, and its rigid domain layout.
3.  Highlight the supporting metrics:
    -   Overall Global pLDDT and the breakdown of fraction confidence
        (especially Very Low vs. Very High).
    -   Domain Boundary Analysis (number of distinct global domains and their
        specific residue ranges).
4.  **Explicit Disorder Warning:** If the analysis concludes that the protein is
    highly intrinsically disordered (e.g., high fraction of <50 pLDDT or lack of
    rigid domains), issue a separate, prominent warning. Advise the user against
    proceeding with whole-protein downstream structural analysis (like Foldseek
    or docking). If small ordered domains exist amidst the disorder, advise the
    user to restrict any future analysis strictly to those specific residue
    boundaries.
5.  Remind the user that per-residue pLDDT is embedded in the B-factor column of
    the downloaded mmCIF file.

© google-deepmind · Apache-2.0 · google-deepmind/science-skills

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