skills/bioemu-sample/SKILL.md
Generate monomer conformational source candidates with BioEmu, then hand them to MDClaw preparation.
npx skillsauth add matsunagalab/mdclaw bioemu-sampleInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when the user wants to sample a monomer conformational ensemble with BioEmu before running atomistic MD.
Respond in the user's language. Invoke MDClaw tools via Bash with mdclaw.
Output is JSON on stdout.
BioEmu is a monomer MD surrogate source generator. It is not a replacement for production MD and should not be used for multimers, ligands, PTMs, or nucleic acids. Redirect those cases to Boltz-2 or the standard preparation workflow.
Confirm:
Reject or redirect if the input contains multiple chains, ligands, PTMs, or non-standard residue codes.
mdclaw check_surrogate_backend --model bioemu
If the backend is missing, ask the user before installing, then run one of:
mdclaw setup_surrogate_backend --model bioemu --device cpu
mdclaw setup_surrogate_backend --model bioemu --device cuda
BioEmu is installed in an isolated venv, never in the conda mdclaw
environment.
For a source node:
mdclaw generate_surrogate_candidates \
--model bioemu \
--amino-acid-sequence YYDPETGTWY \
--num-samples 100 \
--max-candidates 20 \
--job-dir <job_dir> \
--node-id source_001
This creates a source_bundle.json with source_type="surrogate" and
origin.kind="bioemu".
--num-samples is a request: BioEmu's physicality filter (CA-CA, C-N, clash
checks) drops unphysical frames, so the realized candidate count can be lower.
Compare metadata.num_samples_requested vs metadata.num_candidates in the
source bundle, or oversample with --num-samples N --max-candidates K to
guarantee K outputs.
mdclaw list_source_candidates \
--job-dir <job_dir> \
--node-id source_001
Candidates are written with side-chains already reconstructed (HPacker runs
inline after BioEmu sampling) and tagged hpacker_repacked. The raw
backbone-only frames are archived under
artifacts/candidates_backbone/ for provenance. Pass
--reconstruct-sidechains false if you only want the backbone-only
ensemble. For now choose a single candidate for prepare_complex;
multi-candidate selection and fan-out belong to later workflow phases.
Use the selected candidate with the standard preparation skill. Create the
prep node first (its parent auto-resolves to the source node), then run
prepare_complex with the node id create_node returns:
mdclaw create_node --job-dir <job_dir> --node-type prep
mdclaw --job-dir <job_dir> --node-id <prep_node_id> prepare_complex \
--source-candidate-id candidate_001
testing
Study-level planning for MDClaw. Turns scientific questions into a small MD research plan, planned jobs, analysis intent, and decision criteria before handing off to stage skills.
data-ai
Run MDPrepBench and MDStudyBench tasks with prompt-driven MD agents and deterministic scorer commands. Use for benchmark runs, agent submissions, and comparing MD agents.
data-ai
AI-driven protein structure prediction using Boltz-2 for single proteins, multimers, and protein-ligand complexes.
tools
Production molecular dynamics simulation using MDClaw CLI tools and OpenMM. Runs MD from an equilibrated state, with HMR, restart, and HPC submission support.