git:20260711.5ae6f6f to git:20260712.3675171

7 added, 0 removed. Audit A to A.

---

name: bio-structure-annotation

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

---

# Bio Structure Annotation

Structure prediction and structure-based annotation.

## Instructions

1. Run a fast embedding screen with TM-Vec to triage candidate proteins by remote homology before incurring structure-prediction cost.

2. 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).

3. 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.

4. Annotate hits and route high-value unknowns back to `/bio-annotation` for sequence-side context, or to comparative analyses via `/bio-protein-clustering-pangenome`.

+ 5. 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

```text

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.