Skip to content
Research
Skill

/alphafold_database_fetch_and_analyze

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

BOOST
From plugin
science-skills
3.2k40 skills
Install
$ npx -y skills add google-deepmind/science-skills --skill alphafold_database_fetch_and_analyze --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/alphafold_database_fetch_and_analyze

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

alphafold_database_fetch_and_analyze.SKILL.md
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:

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:

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:

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.

Read more
Ships withscience-skills

A collection of agent skills for scientific research tasks, spanning genomics, structural biology, cheminformatics, literature search, and more.

Get the whole plugin
Stats
3,220
Stars
362
Forks
Active
Maintenance
Python
Language
Apache-2.0
License
22d ago
Last commit
4mo ago
Created
15h ago
Added

Repo: google-deepmind/science-skills

Other skills on science-skills.