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- 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
How to use alphafold-database-fetch-and-analyze
- 1.Obtain the UniProt Accession ID for your protein (e.g., P00520)
- 2.Run the fetch script: uv run scripts/fetch_structure.py <UNIPROT_ID> -o /path/to/output/
- 3.Analyze pLDDT confidence: uv run scripts/analyze_plddt.py ./data/AF-<ID>-F1-metadata.json
- 4.Analyze domain boundaries: uv run scripts/analyze_pae.py ./data/AF-<ID>-F1-predicted_aligned_error_v6.json
- 5.Review output for warnings (isoforms, large proteins, disorder) and synthesize results into structural assessment
Use cases
- 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
- 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
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.
No. Use the Foldseek skill for homolog searches. This skill analyzes a single known structure for a given UniProt ID.
No. This skill only retrieves pre-computed predictions from the AlphaFold Database. For custom sequences, use a local AlphaFold installation or server.
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.
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
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- 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:
- Isoform / Large Protein Warning (MANDATORY): Check the script output for
any
[!] WARNINGlines. 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. - 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.
- 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).
- 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.
- Remind the user that per-residue pLDDT is embedded in the B-factor column of the downloaded mmCIF file.
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