PluginBench
Skill
Pass
Audit score 90

alphafold-database-fetch-and-analyze

google-deepmind/science-skills

Retrieve and analyze AlphaFold predicted protein structures from UniProt IDs with confidence metrics and domain analysis.

What is alphafold-database-fetch-and-analyze?

Fetches AlphaFold Database structures and Predicted Aligned Error matrices for a given UniProt Accession ID, then performs automated analysis on structural confidence (pLDDT), disordered regions, and domain boundaries. Use this when you have a specific UniProt ID and need to assess protein folding quality and structural organization.

  • Downloads mmCIF structure files and PAE matrices from AlphaFold Database for UniProt IDs
  • Analyzes pLDDT confidence metrics to identify structured vs. disordered regions
  • Detects rigid domain boundaries using sliding-window PAE heuristics
  • Assesses intrinsic disorder and inter-domain flexibility
  • Handles large proteins and isoform fallback automatically
  • Provides per-residue confidence data embedded in B-factor column

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

npx skills add https://github.com/google-deepmind/science-skills --skill alphafold-database-fetch-and-analyze
Prerequisites
  • uv package manager installed and on PATH (see uv skill)
  • UniProt Accession ID for the target protein
  • License acknowledgment: check https://alphafold.ebi.ac.uk/ terms on first use
Claude Code
Cursor
Windsurf
Cline

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

  1. 1.Obtain the UniProt Accession ID for your protein (e.g., P00520)
  2. 2.Run the fetch script: uv run scripts/fetch_structure.py <UNIPROT_ID> -o /path/to/output/
  3. 3.Analyze pLDDT confidence: uv run scripts/analyze_plddt.py ./data/AF-<ID>-F1-metadata.json
  4. 4.Analyze domain boundaries: uv run scripts/analyze_pae.py ./data/AF-<ID>-F1-predicted_aligned_error_v6.json
  5. 5.Review output for warnings (isoforms, large proteins, disorder) and synthesize results into structural assessment

Use cases

Good for
  • Evaluate structural confidence of a predicted protein before downstream analysis like docking
  • Identify disordered regions in a protein to guide experimental design or restrict analysis to ordered domains
  • Map rigid domain boundaries to understand protein architecture and modularity
  • Assess whether a protein is suitable for structure-based homology searches (Foldseek)
  • Validate AlphaFold predictions for a specific UniProt entry before publication or further study
Who it's for
  • Structural biologists and computational biologists
  • Researchers validating AlphaFold predictions
  • Scientists planning protein engineering or docking studies
  • Anyone analyzing predicted protein structures with confidence requirements

alphafold-database-fetch-and-analyze FAQ

What if I only have a protein name or gene name, not a UniProt ID?

Ask the user to look up the UniProt ID on https://www.uniprot.org first. This skill requires a specific UniProt Accession ID to fetch the correct structure.

Can I use this to search for structural homologs?

No. Use the Foldseek skill for homolog searches. This skill analyzes a single known structure for a given UniProt ID.

Can I run AlphaFold predictions on my own protein sequence?

No. This skill only retrieves pre-computed predictions from the AlphaFold Database. For custom sequences, use a local AlphaFold installation or server.

What do pLDDT and PAE metrics mean?

pLDDT (predicted Local Distance Difference Test) measures per-residue confidence (0–100); higher values indicate more reliable structure. PAE (Predicted Aligned Error) measures confidence in relative domain positioning; low PAE between regions indicates rigid domains.

What should I do if the protein is highly disordered?

The skill will issue a disorder warning. Avoid whole-protein structural analysis (docking, Foldseek). Instead, restrict analysis to specific ordered domain regions identified in the output.

Full instructions (SKILL.md)

Source of truth, from google-deepmind/science-skills.


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.
  • 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.