--- name: bio-structure-annotation-836f519e50a6 description: Predict protein or complex structures and find structural homologs. Use when sequence search gives weak or no hits, or when a fold, complex model, or prediction confidence is needed. --- # Bio Structure Annotation Structure prediction and structure-based annotation. ## Instructions Tool guides and versions: [docs/README.md](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 | Task | Action | |------|--------| | Validate and plan | `uv run --script skills/bio-structure-annotation/scripts/run_structure_annotation.py --boltz-yaml complex.yaml --out-dir results/bio-structure-annotation` | | Structure search | add `--foldseek-query query.pdb --foldseek-db targetDB` (with `--gpu`, pass `--foldseek-padded-db`) | | Embedding screen | add `--tmvec-query proteins.faa --tmvec-db db.npz` | ## 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. --- Source attribution: fmschulz Original project: https://github.com/fmschulz/omics-skills/blob/493585ac4b5cc032c974273a8c4e8f0458df6c2a/skills/bio-structure-annotation/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Frederik Schulz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: fmschulz Original project: https://github.com/fmschulz/omics-skills/blob/493585ac4b5cc032c974273a8c4e8f0458df6c2a/skills/bio-structure-annotation/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Researcher Specialties: Backend, Database, Frontend Tools: Pixi, Conda Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 35c4b71e5da053e39a12fa535d9ca0ac294dcccedb8a059988edf0c24290bad3