Proteinmpnn Nim logo

Proteinmpnn Nim

Organization
NVIDIA-BioNeMo
proteinmpnn-nim

Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omit_AAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.

Overview

PublisherNVIDIA-BioNeMo
Repositorybionemo-agent-toolkit
Skill nameproteinmpnn-nim
Stars
502
Forks
78
Bundled files
18
Links
  • 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.

  • 18 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-BioNeMo on GitHub. Read the source before you install it.

Installation

Install the Proteinmpnn Nim 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/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/proteinmpnn-nim
  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/NVIDIA-BioNeMo/bionemo-agent-toolkit.git /tmp/bionemo-agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/bionemo-agent-toolkit/nim-skills/proteinmpnn-nim .claude/skills/proteinmpnn-nim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proteinmpnn Nim 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 Nim 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 Nim 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 NIM

Design protein sequences for a supplied backbone PDB. Use this guide for first-pass hosted/local usage; load supplemental files only when needed:

  • references/api.md: exact endpoints, schemas, Docker flags, response fields.
  • references/science.md: inverse-folding uses, limits, and validation.
  • references/parameters.md: design controls, fixed positions, sampling.
  • references/validation.md: FASTA, score, and structure checks.
  • references/examples.md: compact hosted/local request patterns.

Choose Mode

Honor the user's explicit mode; otherwise use the configured runtime. NIM_API_MODE=local selects the local service at PROTEINMPNN_NIM_URL; the URL defaults to http://localhost:8000 for a NIM running in the same host or container. Ask only when neither the environment nor the user's request makes the mode clear:

Hosted NVIDIA API or local Docker NIM?

  • Hosted: https://health.api.nvidia.com/v1/biology/ipd/proteinmpnn/predict
  • Local: append /biology/ipd/proteinmpnn/predict to PROTEINMPNN_NIM_URL (default base URL: http://localhost:8000).

Local inference paths do not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed.

Data Transfer and Authorization

Before a hosted request, tell the user that the entire PDB file and design parameters will be uploaded to NVIDIA's hosted API at the endpoint above. Proceed if the user has explicitly requested hosted processing of that PDB or already approved the transfer; otherwise ask for confirmation before submitting. For confidential structures, recommend a local NIM in the user's approved environment. A configured local URL may point to another machine; use only the configured or user-selected destination. Do not switch from local to hosted processing without the user's authorization.

The client reads NIM_API_MODE, PROTEINMPNN_NIM_URL, and, for hosted mode, NGC_API_KEY from the environment. It sends the key only in the HTTPS Authorization header to the hosted endpoint; local inference sends no key. Keep credentials out of logs and saved artifacts. The output directory contains the full input PDB in request.json and the returned sequences and scores, so use a location appropriate for the input's sensitivity. See references/api.md for endpoint and data-handling details.

Local Docker

For local setup, run the full sequence — env preflight, docker login, docker run, readiness loop, then the no-auth localhost request; do not answer with only a localhost Python request. For the exact preflight (.env sourcing, NGC_API_KEY/NVIDIA_API_KEY handling, and the docker run for nvcr.io/nim/ipd/proteinmpnn:latest), copy the command block in references/api.md under Docker Reference verbatim. This NIM's cache mount is /home/nvs/.cache/nim, not /opt/nim/.cache. When PROTEINMPNN_NIM_URL is supplied, the service is already managed elsewhere; use that URL and do not start another Docker container.

Readiness:

bash
proteinmpnn_nim_url="${PROTEINMPNN_NIM_URL:-http://localhost:8000}"
until curl -sf "${proteinmpnn_nim_url%/}/v1/health/ready"; do sleep 5; done

Instructions

For a request to execute a design, run scripts/design.py and inspect its results. Writing a request script alone does not complete an execution request. If the user asks only for code or setup instructions, provide those without submitting an inference request.

  1. Use the user's PDB path and requested sequence count. The client reads the entire PDB into input_pdb; do not replace or truncate the supplied backbone.
  2. Select --mode hosted or --mode local and follow Data Transfer and Authorization above before submitting. Hosted mode uploads the PDB to the documented NVIDIA endpoint and requires NGC_API_KEY in the environment. Check only whether the key is set; do not print it, dump the environment, or save authentication headers. Local inference sends no authorization header.
  3. Choose a new --output-dir for each request. The client reserves it before submitting, preserves the raw response for diagnostics, and validates the designed sequence count and score alignment before reporting completion.
  4. Read summary.json and report the actual results described below. If the request or validation fails, report the failure and diagnostic path; do not substitute example sequences or repeatedly resubmit the same request.

Examples

Run from this skill's directory, or use an absolute path to scripts/design.py. Substitute the user's input path and a new output directory:

bash
python scripts/design.py --mode hosted \
  --pdb /path/to/backbone.pdb --num-sequences 10 \
  --temperature 0.1 --output-dir /path/to/new-design-run

For a running local NIM, use --mode local; the client honors PROTEINMPNN_NIM_URL. To design only chain A, exclude cysteine, or request the soluble model, add --chains A, --omit-aas C, or --soluble respectively. --seed sets random_seed; --ca-only selects the CA-only model. The helper uses one temperature per request; run separate output directories for a temperature sweep. For advanced JSONL controls or a custom batch request, use references/api.md and the post-response example in references/examples.md.

Save And Report Output

The client writes request.json, response.raw, response.json, designed_sequences.fa, and summary.json into the requested output directory. The FASTA preserves the complete returned mfasta, including a native/WT entry when present. The summary contains only designed sequences, each paired with its actual score, and records whether scores came from the JSON array or FASTA headers. It is also printed after the artifacts are saved and checked.

In the final response, report:

  • The number of designed sequences, excluding the native/WT reference.
  • Each design's identifier and actual returned score, plus its sequence (for long sequences, give a clearly labelled preview and link to the full FASTA).
  • The saved FASTA and summary paths, and the raw response path for provenance.
  • That these are inverse-folding candidates, with no fold-back validation performed unless it was actually requested and run.

Do not treat a score as proof that a sequence folds or binds. Further validation with Boltz2 or OpenFold3 is an optional next step. For FASTA/score sanity checks, read references/validation.md.

Limits And Troubleshooting

  • Minimum GPU VRAM: about 3 GB.
  • sampling_temp must be a list, even for one value.
  • Empty mfasta: check non-empty input_pdb and num_seq_per_target >= 1.
  • PDB parse errors: use valid PDB ATOM records.
  • Local URL 404 usually means an accidental /v1/ prefix.
  • Cache mount error: use /home/nvs/.cache/nim inside the container.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Proteinmpnn Nim AI skill do?

Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omit_AAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.

Why use Proteinmpnn Nim on TypingMind?

Because you install it once and use it with any model. Proteinmpnn Nim 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 Nim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/nim-skills/proteinmpnn-nim. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Proteinmpnn Nim?

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 Nim?

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

Is the Proteinmpnn Nim AI skill free?

It is published on GitHub by NVIDIA-BioNeMo. Check the repository for licensing terms. 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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