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Proteinmpnn Design Workflow

Organization
ai4protein
proteinmpnn_design_workflow

ProteinMPNN inverse folding: design or score sequences on a fixed backbone. Use when the user wants sequence design from PDB, interface/binder design, homomer symmetry, or fixed catalytic residues. Do NOT use for zero-shot mutation ranking on a wild-type sequence (zero_shot_mutation_workflow) or de novo fold hallucination without a backbone.

Overview

Publisherai4protein
RepositoryVenusFactory2
Skill nameproteinmpnn_design_workflow
Stars
255
Forks
37
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ai4protein on GitHub. Read the source before you install it.

Installation

Install the Proteinmpnn Design Workflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/proteinmpnn_design_workflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ai4protein/VenusFactory2.git /tmp/VenusFactory2
mkdir -p .claude/skills
cp -r /tmp/VenusFactory2/src/agent/skills/proteinmpnn_design_workflow .claude/skills/proteinmpnn_design_workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proteinmpnn Design Workflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Proteinmpnn Design Workflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Proteinmpnn Design Workflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ProteinMPNN Design Workflow

Overview

Orchestrates VenusFactory denovo tools for backbone-conditioned sequence design and scoring. Residue indices in fixed_residues_json are 1-indexed.

Project Tools (VenusFactory2)

ToolArgsWhen
proteinmpnn_sequence_design_from_structurepdb_path; optional designed_chains, fixed_chains, fixed_residues_json, homomer, num_sequences, temperatures, out_dirDesign sequences
proteinmpnn_sequence_scoring_from_structurepdb_path; optional fasta_path, designed_chains, out_dirScore designs / native
pdb_chain_sequences / check_pdb_apoPDB pathPrep / inspect
zero_shot_mutation_structure_predictiondesigned PDB contextOptional post-design ranking

Workflow patterns

GoalKey args
Single-chain redesignpdb_path only
Design chain B against fixed Adesigned_chains=["B"], fixed_chains=["A"]
Keep catalytic residuesfixed_residues_json='{"A":[57,102,195]}'
Homomerdesigned_chains=["A","B","C"], homomer=true
  1. Validate PDB (check_pdb_apo if ligand-free backbone expected).
  2. Design with explicit out_dir.
  3. Score designs with proteinmpnn_sequence_scoring_from_structure.
  4. Optional: structure prediction / property checks on top sequences.

Common mistakes

  • Passing nearly all residues as fixed when intending to redesign the whole chain (or the inverse) — check tool log warnings.
  • 0-based residue indices in fixed_residues_json.
  • Expecting affinity/ΔG; ProteinMPNN gives sequence likelihoods, not binding free energy.

Frequently asked questions

What does the Proteinmpnn Design Workflow AI skill do?

ProteinMPNN inverse folding: design or score sequences on a fixed backbone. Use when the user wants sequence design from PDB, interface/binder design, homomer symmetry, or fixed catalytic residues. Do NOT use for zero-shot mutation ranking on a wild-type sequence (zero_shot_mutation_workflow) or de novo fold hallucination without a backbone.

Why use Proteinmpnn Design Workflow on TypingMind?

Because you install it once and use it with any model. Proteinmpnn Design Workflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Proteinmpnn Design Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/proteinmpnn_design_workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Proteinmpnn Design Workflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Proteinmpnn Design Workflow?

As many as you like. As long as a model supports skills, you can use Proteinmpnn Design Workflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Proteinmpnn Design Workflow AI skill free?

Yes. It is published on GitHub by ai4protein under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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