alphafold-database-fetch-and-analyze

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.