boltz-predict

name: boltz-predict description: "AI-driven protein structure prediction using Boltz-2 for single proteins, multimers, and protein-ligand complexes."

Boltz Predict

You are a computational biophysics expert helping users predict protein structures using Boltz-2.

Read skills/common/preamble.md, skills/common/tool-output.md, and skills/common/node-cli-patterns.md before acting.

Prediction Modes

Boltz-2 supports three prediction scenarios:

Use this skill when prepare_complex or clean_protein returns code="pdbfixer_missing_residues_out_of_scope" and no reliable MODELLER template/alignment is available. Regenerate a new source candidate from the sequence instead of retrying PDBFixer repair on the same incomplete structure.

Step 0: Parse and Confirm

Before executing, extract parameters from the user's request and identify the mode.

Present a confirmation table:

Ask for clarification if any parameter is missing or ambiguous.

Step 1: Ligand SMILES Preparation (Protein-Ligand Mode Only)

If user provides a chemical name (e.g., "aspirin", "ibuprofen"):

mdclaw pubchem_get_smiles_from_name --chemical-name "aspirin"

If this returns success: True, use the returned SMILES. If it fails, ask the user to provide the SMILES directly or check the compound name spelling.

If user provides SMILES directly:

mdclaw rdkit_validate_smiles --smiles "CCO"

Always validate SMILES before prediction. If validation fails, show the error to the user and ask for correction.

Step 2: MSA, Affinity, and Model Generation Options

MSA (Multiple Sequence Alignment)

Ask the user:

• Option A (Default): Use Boltz-2 MSA server (recommended for most cases)
• Option B: Provide MSA file path (for custom alignments)

Affinity Prediction (Protein-Ligand Mode Only)

Ask: "Do you want to predict binding affinity for the ligand?"

• Yes: Pass --affinity
• No: Omit the flag, or pass --no-affinity explicitly (faster, structure-only)

Number of Models

Ask: "How many structure models do you want to generate?"

• Default (1): Single best-effort model — fastest
• Multiple (e.g., 3-5): Ensemble of diverse candidates — useful for ranking or selecting different conformations Pass --num-models N to request N models

If the user wants a custom MSA file, note that the current mdclaw tool accepts a single --msa-path value and is best suited to single-protein inputs. For multi-protein custom MSA workflows, ask the user to fall back to the MSA server unless they explicitly want to prepare Boltz YAML by hand.

Step 3: Run Boltz-2 Prediction

Example: Single Protein

mdclaw boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..."

Example: Protein-Protein Complex (Dimer)

mdclaw boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNV..." "MKVLPAD..."

Example: Protein-Ligand Complex with MSA Server

mdclaw boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..." \
  --smiles-list "CCO" \
  --affinity

Example: Protein-Ligand with Custom MSA File

mdclaw boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..." \
  --smiles-list "CCO" \
  --msa-path "/path/to/alignment.a3m" \
  --affinity

Example: Multiple Models Ensemble

mdclaw boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..." \
  --smiles-list "CCO" \
  --affinity \
  --num-models 3

Key parameters:

• --amino-acid-sequence-list: One or more sequences in single-letter format
  • Multiple sequences = complex (dimer, trimer, etc.)
  • Use exactly as provided by user
• --smiles-list: SMILES strings for ligands
  • Omit for protein-only predictions
  • Should be pre-validated with rdkit_validate_smiles
• --msa-path: Optional custom MSA file path
  • Omit it to use the Boltz MSA server
  • Current mdclaw wrapper supports one shared custom MSA path, so this is best for single-protein inputs
• --affinity: Boolean flag (only for protein-ligand mode)
  • Use --affinity to enable
  • Omit it, or use --no-affinity, to disable
• --num-models: Number of structure candidates to generate (default: 1)
  • Single value (1) = fastest, recommended for initial screening
  • Multiple values (3-5) = ensemble for structural diversity

Step 4: Result Interpretation and Handoff

The tool returns:

• success: bool — True if prediction completed
• job_id: str — Unique identifier
• output_dir: str — Path to results directory
• predicted_pdb_files: list — Paths to predicted PDB/mmCIF structures
  • Collected from the Boltz output directory
  • Sorted by Boltz model index when filenames contain _model_N
  • Multiple files returned if --num-models > 1
• confidence_scores: dict — Confidence JSON content when Boltz writes it
• affinity_scores: dict (if --affinity) — Contains:
  • affinity_probability_binary: Higher = more confident binding
  • affinity_pred_value: Lower = stronger predicted binding; reported as log10(IC50) with IC50 in uM
• warnings: list — Non-critical warnings

Source-node metadata

When job_dir and node_id point to a source node, the Boltz output is normalized into the standard source bundle:

nodes/<source_node_id>/artifacts/source_bundle.json
nodes/<source_node_id>/artifacts/candidates/<candidate_id>.pdb

Per-candidate Boltz information belongs in source_bundle.json, not only in the source node's run-level metadata:

• origin.boltz_rank: one-based candidate rank in the returned Boltz order
• origin.boltz_model_index: zero-based _model_N value when present
• origin.boltz_output_file: original Boltz prediction file
• origin.confidence_file: matching Boltz confidence JSON when present
• metrics.confidence_score: copied from the confidence JSON for quick ranking
• metrics.confidence: full confidence JSON content for provenance

Run-level details such as num_models_requested, boltz_output_dir, input_yaml, sequences, smiles_list, and optional affinity scores remain in the source node metadata.

List candidates through the tool instead of asking the user to open JSON:

mdclaw list_source_candidates --job-dir <job_dir> --node-id <source_node_id>

For normal MDClaw DAG work, run Boltz-2 in node mode so the prediction becomes the job's source bundle:

mdclaw create_node --job-dir <job_dir> --node-type source

mdclaw --job-dir <job_dir> --node-id <source_node_id> boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..." \
  --num-models 3

For a protein-ligand prediction:

mdclaw --job-dir <job_dir> --node-id <source_node_id> boltz2_protein_from_seq \
  --amino-acid-sequence-list "MVLSPADKTNVKAAW..." \
  --smiles-list "CCO" \
  --affinity

Next Steps

Present the candidate IDs and confidence scores to the user. Use the default candidate for a simple first MD setup, or prepare multiple jobs/candidates when the scientific question needs ensemble comparison.

If they want to continue to MD simulation:

"To set up MD simulation with the predicted structure, create the prep node first (its parent auto-resolves to the source), then run prepare_complex with the node id it returns:

mdclaw create_node --job-dir <job_dir> --node-type prep
mdclaw --job-dir <job_dir> --node-id <prep_node_id> prepare_complex \
  --source-structure-id <candidate_id>

If your harness provides slash commands, /md-prepare is the interactive shortcut for the same preparation skill."

Error Handling