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name: inference-design description: "Run and adapt ProteinMPNN inference/design workflows, including single-PDB design, parsed JSONL batches, monomer and multichain design, CA-only and soluble models, score-only mode, probability outputs, output interpretation, and CPU/GPU-safe command construction." disable-model-invocation: true metadata: disco-role: operating license: MIT

ProteinMPNN Inference Design

Use this sub-skill when a user wants to run protein_mpnn_run.py, change inference flags, choose model weights, interpret generated FASTA/NPZ outputs, or debug ProteinMPNN design runs.

Quick Routing

• Use direct PDB mode when the user has one structure: --pdb_path structure.pdb --pdb_path_chains "A B".
• Use parsed JSONL mode when the user has many PDBs or precomputed helper JSONL inputs: --jsonl_path parsed_pdbs.jsonl plus optional constraint JSONL files.
• Route chain assignment, fixed positions, tied positions, amino-acid bias, omit masks, and PSSM builder details to ../constraint-inputs/.
• Route retraining, custom checkpoint production, or modifying model architecture/training scripts to ../training-custom-models/.
• Use scripts/build_inference_command.py to print safe protein_mpnn_run.py commands without copying ProteinMPNN model code.

Minimum Runtime Assumptions

• The user runs from a ProteinMPNN checkout containing protein_mpnn_run.py, protein_mpnn_utils.py, and model weight folders.
• Python can import torch and numpy in the selected environment.
• Model weights are present locally; this skill does not bundle or copy .pt weight files.
• --out_folder is required because ProteinMPNN writes seqs/, scores/, probs/, score_only/, conditional_probs_only/, or unconditional_probs_only/ under it.

Start Here

1. Identify input style: single PDB (--pdb_path) or parsed batch JSONL (--jsonl_path). Do not pass both unless intentionally testing argument precedence; direct PDB mode wins when --pdb_path is non-empty.
2. Identify task mode: design sequences, score existing sequence/backbone pairs, save sampled probabilities, or emit conditional/unconditional probabilities.
3. Choose weights: default full-backbone vanilla, --use_soluble_model for soluble full-backbone weights, or --ca_only for CA-only weights.
4. Set conservative controls first: --batch_size 1, explicit --seed, one or more --sampling_temp values, and a small --num_seq_per_target for dry runs.
5. Read the references in this sub-skill before changing less common flags.

References

• references/cli-reference.md lists the important protein_mpnn_run.py flags and model-family rules.
• references/workflows.md gives distilled recipes for examples 1, 2, 3, score-only, score-only from FASTA, CA-only, soluble, and probability-only runs.
• references/output-formats.md explains seqs/*.fa headers and NPZ payloads.
• references/troubleshooting.md covers common runtime failures and safe CPU/GPU adjustments.

Bundled Helper

Print a direct-PDB dry-run command:

python skills/protein-mpnn/sub-skills/inference-design/scripts/build_inference_command.py \
  --pdb-path inputs/my_target.pdb \
  --chains "A B" \
  --out-folder outputs/my_design \
  --num-seq-per-target 2 \
  --batch-size 1 \
  --seed 37

Print a parsed-JSONL score-only command using a FASTA:

python skills/protein-mpnn/sub-skills/inference-design/scripts/build_inference_command.py \
  --jsonl-path outputs/parsed_pdbs.jsonl \
  --out-folder outputs/score_existing \
  --score-only \
  --path-to-fasta outputs/seqs/target.fa \
  --num-seq-per-target 5 \
  --batch-size 1

The helper prints a command only; it does not run ProteinMPNN and does not create helper JSONL files.