structural-biology

name	structural-biology
description	Structure retrieval, confidence-aware AlphaFold DB usage, coordinate download, PAE and pLDDT interpretation, and structure-guided biological annotation.
tool_type	python
primary_tool	AlphaFold DB

Structural Biology

[!note] Vault audit 2026-07-24 — USE-11 Use this to retrieve and interpret existing structures (PDB download, AlphaFold DB, PAE/pLDDT reading, structure-guided annotation); to predict new structures use colabfold / struct-predictor (local) or the boltz2-nim / openfold2-nim / openfold3-nim NIMs. Retrieve vs predict is the routing axis.

Version Compatibility

Reference examples assume:

• biopython 1.84+
• AlphaFold DB public API current format
• optional visualization stack such as py3Dmol or PyMOL

Verify before use:

• Python: python -c "import Bio; print(Bio.__version__)"

Overview

Use this skill when the task is:

• retrieving AlphaFold-predicted structures by UniProt accession
• downloading coordinate and confidence files
• reading pLDDT or PAE to judge confidence
• mapping sequence findings onto structure

When To Use This Skill

• a UniProt accession or known protein target exists
• experimental structure is absent or incomplete
• the user needs confidence-aware structural interpretation

Quick Route

• known UniProt accession: query AlphaFold DB first
• novel designed sequence without AlphaFold DB entry: use a separate prediction workflow such as ColabFold
• structure interpretation request: always inspect pLDDT and PAE before making mechanistic claims

Progressive Disclosure

• Read technical_reference.md for confidence interpretation and source-selection rules.
• Read commands_and_thresholds.md for AlphaFold DB retrieval patterns, URL layouts, and file conventions.

Expected Inputs

• UniProt accession or sequence context
• optional residue list, mutation list, or ligand site hypothesis

Expected Outputs

• results/structures/AF-<accession>.cif
• results/structures/AF-<accession>.pdb
• results/confidence/AF-<accession>-confidence.json
• results/confidence/AF-<accession>-pae.json
• figures/AF-<accession>-pae.png

Starter Pattern

from Bio.PDB import alphafold_db

prediction = next(alphafold_db.get_predictions("P00520"))
cif_path = alphafold_db.download_cif_for(prediction, directory="results/structures")
print(cif_path)

Confidence Thresholds

pLDDT

pLDDT	Interpretation
> 90	very high confidence
70-90	good backbone confidence
50-70	low confidence
< 50	likely disorder or unreliable local structure

PAE

PAE	Interpretation
< 5 Å	confident relative positioning
5-15 Å	moderate uncertainty
> 15 Å	domain orientation may be unreliable

Workflow

1. Choose the structure source

• experimental structure if available and suitable
• AlphaFold DB for known proteins with UniProt accessions
• separate prediction workflow for novel sequences

2. Retrieve coordinates and confidence files

Download:

• mmCIF or PDB
• confidence JSON
• PAE JSON

3. Inspect confidence before interpretation

Do not map mutations or infer interfaces from low-confidence regions without saying so.

4. Annotate the biological question

Map domains, active sites, mutations, motifs, or interfaces onto the structure.

5. Export reusable artifacts

Save coordinates, confidence files, and a PAE heatmap or equivalent summary.

Output Artifacts

results/
├── structures/
│   ├── AF-P00520-F1-model_v4.cif
│   └── AF-P00520-F1-model_v4.pdb
└── confidence/
    ├── AF-P00520-F1-confidence_v4.json
    └── AF-P00520-F1-predicted_aligned_error_v4.json
figures/
└── AF-P00520-F1-pae.png

Quality Review

• pLDDT must be reviewed before claiming local residue geometry is trustworthy
• PAE must be reviewed before claiming domain-domain arrangement is trustworthy
• residue numbering and chain mapping must be checked before mutation interpretation
• low-confidence or disordered regions should be labeled explicitly

Anti-Patterns

• treating every AlphaFold region as equally reliable
• ignoring PAE when discussing domain orientation
• mapping variants onto mismatched residue numbering
• using AlphaFold DB retrieval as if it were de novo prediction for novel sequences

Related Skills

• Proteomics
• Pathway Analysis

Optional Supplements

• alphafold-database