Alterlab proteinmpnn

Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/bioinformatics/alterlab-proteinmpnn

239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministic citation verifier, research→write→review→publish pipeline, and a skill-finder front door. Claude Code, Cursor, Codex, Gemini CLI & Copilot.

Install

npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-proteinmpnn

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inverse-folding a backbone PDB into sequences, redesigning selected positions, imposing symmetry across chains, or generating the sequence step of a design→fold→score loop. For pocket/interface design WITH a bound ligand, metal, or nucleic acid prefer alterlab-ligandmpnn; to GENERATE a new backbone prefer alterlab-rfdiffusion; to refold and validate a design prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.3 KB, as published. Nobody here has run it

ProteinMPNN (fixed-backbone sequence design)

Overview

ProteinMPNN (Dauparas et al., Science 2022; dauparas/ProteinMPNN) solves the inverse-folding problem: given a protein backbone (a 3D structure with no or a placeholder sequence), it designs amino-acid sequences predicted to fold to that backbone. It is fast, robust, runs on CPU, and is the standard "sequence" step between backbone generation (alterlab-rfdiffusion) and structure validation (alterlab-alphafold).

When to Use This Skill

Use this skill when the user wants to:

Inverse-fold a backbone PDB into one or more candidate sequences.
Redesign only selected positions while fixing the rest (partial design).
Enforce symmetry by tying residues/chains so homo-oligomers get identical sequences.
Bias the amino-acid composition (e.g. avoid cysteines) or use the soluble model.
Produce the sequence step of a design → fold → score loop.

Does NOT Trigger

Scenario	Use instead
Design a pocket/interface with a ligand, metal, or nucleic acid present	`alterlab-ligandmpnn`
Generate a new backbone (no starting structure)	`alterlab-rfdiffusion`
Refold a designed sequence to check it (validation)	`alterlab-alphafold`
Generative multimodal (sequence+structure+function) design	`alterlab-esm`

Core Capabilities

1. Basic inverse folding

# Parse the PDB, then design sequences (dauparas/ProteinMPNN CLI — TODO(verify) flags/version).
# The parser lives in the repo's helper_scripts directory; run it by name:
python parse_multiple_chains.py --input_path=pdbs/ --output_path=parsed.jsonl
python protein_mpnn_run.py \
  --jsonl_path parsed.jsonl --out_folder out/ \
  --num_seq_per_target 8 --sampling_temp "0.1"

Lower --sampling_temp (e.g. 0.1) gives conservative, high-confidence designs; higher temperatures increase diversity. Output FASTA headers carry the model score (lower = better) and sequence recovery.

2. Fixed positions and chains

Supply a fixed-positions spec (JSONL from the helper scripts) to keep catalytic/known residues while redesigning the rest, and a chain spec to design only some chains. Verify the exact helper-script names against your checkout (TODO(verify)).

3. Symmetry / tied positions

Tie positions across chains so a homo-oligomer receives one sequence applied symmetrically — essential for symmetric alterlab-rfdiffusion outputs.

4. Design → fold → score loop

The canonical de-novo pipeline:

Generate a backbone with alterlab-rfdiffusion.
Design sequences for it here (ProteinMPNN), sampling several per backbone.
Score by refolding each with alterlab-alphafold and accepting only self-consistent designs (returns to the target backbone with high pLDDT, low PAE).

Resources

references/proteinmpnn_usage.md — install/pinning, helper-script inputs (fixed positions, tied chains, bias), the soluble model, temperature guidance, and loop integration. Loaded on demand.

Part of the AlterLab Academic Skills suite.