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Build template-based comparative protein models with MODELLER and register them as MDClaw source candidates.
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Basé sur la classification professionnelle SOC
Standalone minimization plus equilibration of a prepared MD system using MDClaw CLI tools, including low-temperature NVT warmup, NVT heating, and optional NPT density. Creates min and eq DAG nodes and writes restart artifacts for production handoff.
Molecular dynamics preparation with MDClaw CLI tools: acquisition, molecule selection, cleaning, solvation, and topology. Before any state-changing command, follow the pre-command gate in this skill; open linked pages only when their condition applies.
Production molecular dynamics simulation using MDClaw CLI tools and OpenMM. Runs MD from an equilibrated state, with HMR, restart, and HPC submission support.
SLURM-based HPC submission for MDClaw workflow nodes. Handles cluster inspection, single-node and job-array submission, status sync to the DAG, and production restart extensions.
Molecular dynamics trajectory analysis using MDClaw CLI tools. Routes concat, metric, and troubleshooting workflows through focused guidance pages.
Study-level planning and workflow routing for MDClaw. Use for scientific questions, comparative or campaign studies, plan-only requests, and requests to carry planned MD jobs through analysis to an evidence-backed answer.
| name | modeller-predict |
| description | Build template-based comparative protein models with MODELLER and register them as MDClaw source candidates. |
You are a computational biophysics expert helping users build comparative protein models with MODELLER for downstream MDClaw preparation.
Read skills/common/preamble.md, skills/common/tool-output.md, and
skills/common/run-loop.md (the single canonical loop and node-CLI-invariant
reference) before acting.
Use this skill when the user has a template PDB and a target protein sequence or
MODELLER PIR/ALI alignment. Prefer skills/boltz-predict/SKILL.md when there is
no suitable template or when the user asks for AI structure prediction.
Also use this skill when prepare_complex or clean_protein returns
code="pdbfixer_missing_residues_out_of_scope" and the user can provide a
template plus target sequence/alignment. The goal is to regenerate the source
candidate rather than patch a large missing loop inside PDBFixer.
1; use 3-5 when the user wants a
small candidate set for ranking.MODELLER is optional and licensed. The CLI expects a KEY_MODELLER*
environment variable such as KEY_MODELLER10v8.
| Parameter | Value |
|---|---|
| Template PDB | (path) |
| Target | one sequence, one sequence per chain, or an alignment file |
| Codes | template/target codes if the user gave them (else tool defaults) |
| Variant | single chain / multi-chain / loop refinement / explicit alignment |
| Number of models | 1 (default) / 3-5 for a small ranked set |
| Mode | source node (default) / standalone file |
For normal MDClaw DAG work, run MODELLER in node mode on the job's source
node. Choose one variant and follow its command in
skills/modeller-predict/workflow-variants.md:
--target-sequence).--target-sequences,
--template-chains).--loop-refinement).--alignment-file).The tool normalizes the selected model into the source bundle; run
list_source_candidates before preparation.
Use standalone mode only when the user asks for a model file outside a DAG:
mdclaw modeller_from_alignment \
--template-pdb "/abs/template.pdb" \
--target-sequence "MVLSPADKTNVKAAW..." \
--output-dir "/abs/modeller_out" \
--num-models 3
Standalone mode returns the MODELLER output directory and selected model metadata, but it does not register a source candidate.
Use the JSON result:
success: whether modeling completed.file_path: normalized candidate path in node mode.output_dir: MODELLER working directory.selected_model: selected model plus selection_reason.all_models: successful MODELLER models.code: stable failure reason when present.If code=modeller_license_env_missing, tell the user to install/license
MODELLER and export a KEY_MODELLER* variable. If code=modeller_not_installed,
tell the user to install MODELLER separately, for example with
conda install salilab::modeller.
After a successful source-node run, follow the canonical handoff in
skills/common/md-handoff.md: create the prep node, run
prepare_complex --source-candidate-id <candidate_id>, then continue with
skills/md-prepare/SKILL.md.