How do I install the Alterlab Proteinmpnn skill?

Run npx skillmds@latest add alterlab-ieu/alterlab-proteinmpnn in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Alterlab Proteinmpnn safe to use?

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Which AI agents work with Alterlab Proteinmpnn?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Alterlab Proteinmpnn free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Alterlab Proteinmpnn?

AlterLab-IEU (@alterlab-ieu) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alterlab Proteinmpnn by alterlab-ieu

Name: Alterlab Proteinmpnn
Rating: 4.2 (5 reviews)
Author: AlterLab-IEU

Alterlab Proteinmpnn

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.

AlterLab-IEU Updated Sep 8, 2026 60 repo stars

File contents

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.

---name: alterlab-proteinmpnndescription: 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.license: MIT---# 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 aplaceholder sequence), it designs amino-acid **sequences predicted to fold to thatbackbone**. It is fast, robust, runs on CPU, and is the standard "sequence" step betweenbackbone generation (`alterlab-rfdiffusion`) and structure validation(`alterlab-alphafold`).## When to Use This SkillUse 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```bash# 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.jsonlpython 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; highertemperatures increase diversity. Output FASTA headers carry the model **score** (lower =better) and sequence recovery.### 2. Fixed positions and chainsSupply a fixed-positions spec (JSONL from the helper scripts) to keep catalytic/knownresidues while redesigning the rest, and a chain spec to design only some chains. Verify theexact helper-script names against your checkout (`TODO(verify)`).### 3. Symmetry / tied positionsTie positions across chains so a homo-oligomer receives one sequence applied symmetrically —essential for symmetric `alterlab-rfdiffusion` outputs.### 4. Design → fold → score loopThe canonical de-novo pipeline:1. **Generate** a backbone with `alterlab-rfdiffusion`.2. **Design** sequences for it here (ProteinMPNN), sampling several per backbone.3. **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.

Frequently asked questions