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 through sbatch. AlphaFold3 is not part of this stack (non-commercial license, large VRAM footprint). Use:
   • Boltz-2 (MIT license, CUDA) as the default predictor for monomers, complexes, and protein-ligand binding affinity.
   • ColabFold v1.5.5+ with an MMseqs2-GPU MSA backend when a deeper MSA than Boltz-2 builds is required. Kallenborn et al. (2025) Nature Methods https://doi.org/10.1038/s41592-025-02819-8 report that MMseqs2-GPU makes ColabFold prediction 31.8× faster than the standard AlphaFold2 pipeline.
   • ESMFold for fast monomer pre-screening only; route candidates to Boltz-2 for final models.
3. Search predicted or experimental structures with Foldseek v9+. On CUDA Turing or newer, --gpu 1 enables the GPU ungapped prefilter and needs a target database built with makepaddedseqdb; the same paper reports a 4-27× Foldseek speedup. Use easy-multimersearch for complex-vs-complex search.
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. It prints the command plan as JSON and, with --execute, runs it. Public MSA services receive biological sequences; --use-msa-server (passed to Boltz as --use_msa_server) is rejected unless the user explicitly approved upload with --approve-public-msa-upload.

Quick Reference

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)
• Boltz input YAML (version: 1 with a sequences list; see fixtures/boltz-complex.yaml)
• One Foldseek query structure file (PDB/mmCIF) and a target database prefix; a padded database for GPU search
• TM-Vec database (.npz from tmvec build-db)

Output

Written by the driver's --execute run:

• results/bio-structure-annotation/boltz/ (Boltz predictions and confidence files)
• results/bio-structure-annotation/foldseek_hits.tsv
• results/bio-structure-annotation/tmvec/results.tsv (TM-Vec hits; tmvec search writes into the tmvec/ folder)
• The command plan on stdout, or in --plan-out when given

Written by the agent:

• results/bio-structure-annotation/structure_hits.tsv (merged, filtered hits with thresholds)
• 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 execution failure, preserve logs and report the failed command; retry only after diagnosing the cause and recording the changed parameters. Report unmet biological thresholds as results; never tune parameters solely to pass a gate.
• 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 passed.
• GPU prediction and search run through sbatch on a GPU node, never on a login node.