Bio structure annotation

Skill fmschulz/omics-skills/skills/bio-structure-annotation

Install

npx -y skills add fmschulz/omics-skills --skill bio-structure-annotation

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Predict protein structures and perform structure-based annotation. Use when sequence evidence is insufficient or structural similarity, confidence, domains, or complexes matter.

SKILL.md

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Bio Structure Annotation

Structure prediction and structure-based annotation.

Instructions

Run a fast embedding screen with TM-Vec to triage candidate proteins by remote homology before incurring structure-prediction cost.
Predict structures on a GPU node. AlphaFold3 is intentionally not part of this stack (non-commercial license, large VRAM footprint, no clear quality gap for the workflows in this repo). Use:
- Boltz-2 (MIT license; CUDA; NVIDIA cuEquivariance kernels) as the default predictor — joint structure-and-affinity, ~1000× faster than FEP for binding-affinity estimation, comparable accuracy to AF3 on benchmarked complexes.
- ColabFold v1.5.5+ with an MMseqs2-GPU MSA backend when a wider MSA than Boltz-2 builds is required (≈31.8× faster MSA generation versus the standard AF2 pipeline; Nature Protocols 2025, DOI: 10.1038/s41596-024-01060-5).
- ESMFold for fast monomer pre-screening only (15–20 GB VRAM; lower accuracy than Boltz-2).
Search predicted or experimental structures with Foldseek v9+. Use --gpu 1 on CUDA Turing or newer for the ProstT5-backed search (4–27× speedup). Consider Foldseek-Multimer when complex-vs-complex search is needed.
Annotate hits and route high-value unknowns back to /bio-annotation for sequence-side context, or to comparative analyses via /bio-protein-clustering-pangenome.
Build and validate commands with scripts/run_structure_annotation.py. Public MSA services receive biological sequences; --use-msa-server is rejected unless the user explicitly approved upload with --approve-public-msa-upload.

Quick Reference

Task	Action
Run workflow	Follow the steps in this skill and capture outputs.
Validate inputs	Confirm required inputs and reference data exist.
Review outputs	Inspect reports and QC gates before proceeding.
Tool docs	See `docs/README.md`.
Validate and plan	`uv run --script scripts/run_structure_annotation.py ...`

Input Requirements

Prerequisites:

Tools declared in the project's pinned Pixi environment. See docs/README.md for expected tools.
Reference DB root: set BIO_DB_ROOT to the project or site-local database directory.
Protein FASTA inputs are available. Inputs:
proteins.faa (FASTA protein sequences)

Output

results/bio-structure-annotation/structures/
results/bio-structure-annotation/structure_hits.tsv
results/bio-structure-annotation/structure_report.md
results/bio-structure-annotation/logs/

Quality Gates

Prediction success rate meets project thresholds.
Search hit thresholds meet project thresholds.
On failure: retry with alternative parameters; if still failing, record in report and exit non-zero.
Verify proteins.faa is non-empty and amino acid encoded.
Verify Foldseek databases exist under the reference root.
GPU Foldseek searches use a database produced by makepaddedseqdb.
Public MSA upload has explicit user approval recorded before --use_msa_server is used.

Examples

Example 1: Expected input layout

proteins.faa (FASTA protein sequences)

Troubleshooting

Issue: Missing inputs or reference databases Solution: Verify paths and permissions before running the workflow.

Issue: Low-quality results or failed QC gates Solution: Review reports, adjust parameters, and re-run the affected step.