Structural Biology

Structural Biology

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 [technicalreference.md](technicalreference.md) for confidence interpretation and source-selection rules.
• Read [commandsandthresholds.md](commandsandthresholds.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-.cif
• results/structures/AF-.pdb
• results/confidence/AF--confidence.json
• results/confidence/AF--pae.json
• figures/AF--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 | | 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: cheatthegod
• Source: cheatthegod/BioHermes
• License: MIT

Install and usage instructions live in the source repository linked above.