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Alphafold Database Fetch And Analyze

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Research and analysis
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

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.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to fetching and analyzing protein structure predictions from the AlphaFold Database. It enables users to retrieve structural files and perform automated heuristic analysis on structural confidence (pLDDT), domain boundaries, and intrinsic disorder. The skill requires a UniProt Accession ID to function and includes mandatory reporting for large proteins or isoform usage, ensuring users receive accurate, context-aware structural insights.

What it does

  • Downloads AlphaFold predicted structures (mmCIF) and PAE matrices for a given UniProt ID.
  • Analyzes pLDDT confidence metrics to categorize regions as structured, disordered, or mixed.
  • Detects rigid domain boundaries using a sliding-window PAE heuristic.
  • Provides mandatory warnings for large proteins or isoform-based entries.
  • Synthesizes structural analysis into a cohesive summary of folding confidence and domain layout.
  • Identifies intrinsically disordered regions and advises on downstream analysis limitations.

When to use it

  • When you have a specific UniProt Accession ID and need structural confidence data.
  • When you need to identify rigid domain boundaries within a protein structure.
  • When you need to assess a protein for intrinsically disordered regions.
  • When you require the raw mmCIF structure file for further local analysis.

Advantages

  • Automates the retrieval and interpretation of complex structural data.
  • Provides clear, standardized heuristics for pLDDT and PAE analysis.
  • Ensures consistent reporting of structural limitations and warnings.
  • Integrates directly with local file systems for subsequent structural workflows.

Usage examples

“Analyze the structural confidence and domain layout for UniProt ID P00520.”
The agent will fetch the structure files for P00520, run the pLDDT and PAE analysis scripts, and provide a summary of the protein's folding confidence and domain boundaries.
“Is the protein with UniProt ID P04637 intrinsically disordered?”
The agent will analyze the pLDDT metrics for P04637 and report whether it exhibits high levels of disorder, including any specific warnings if the protein is highly disordered.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google-deepmind/alphafold-database-fetch-and-analyze/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip alphafold-database-fetch-and-analyze.zip -d .agents/skills

Documentation by Google DeepMind

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.

  • 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.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/alphafold_database_fetch_and_analyze · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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