Bio Structure Annotation

Structure prediction and structure-based annotation.

Instructions

Tool guides and versions: docs/README.md.

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.