bio-structure-annotationlisted

# 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`. ## Quick Reference | Task | Action | |------|--------| | Run workflow | Follow the steps in this skill and cap