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NVIDIA-BioNeMo
NVIDIA-BioNeMoOFFICIAL
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openfold2-nim

Predict monomer protein structures from amino-acid sequences via OpenFold2 NIM microservices.

413|62|Updated Jun 23, 2026
Search Tags:#bioinformatics#structure#protein#nim#folding#bionemo
One-click install
npx skills add https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit --skill openfold2-nim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openfold2-nim
Source: https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit/tree/main/plugins/bionemo-agent-toolkit/skills/openfold2-nim
Command: npx skills add https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit --skill openfold2-nim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of protein structure prediction by providing a standardized interface to NVIDIA BioNeMo NIM microservices, allowing users to generate high-quality monomer protein structures from amino-acid sequences without managing complex local infrastructure.

Core Features & Use Cases

  • Monomer Structure Prediction: Generate 3D protein structures from amino-acid sequences using state-of-the-art OpenFold2 models.
  • Flexible Deployment: Seamlessly switch between hosted NVIDIA API endpoints and local Docker-based NIM deployments.
  • Workflow Integration: Supports advanced inputs like A3M multiple sequence alignments and mmCIF templates to refine prediction accuracy for scientific research.

Quick Start

Use the openfold2-nim skill to predict the structure of the protein sequence provided in the user prompt using the hosted NVIDIA API.

Frequently Asked Questions about openfold2-nim

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict monomer protein structures from amino-acid sequences?▼

Predict monomer protein structures by submitting amino-acid sequences to the OpenFold2 NIM microservice. This skill generates 3D models using state-of-the-art folding algorithms without requiring local infrastructure management.

What inputs do I need to refine protein structure prediction accuracy?▼

To refine protein structure prediction accuracy, you can supply optional A3M multiple sequence alignments and mmCIF templates alongside the core amino-acid sequences to ensure high-confidence structural modeling.

Can I use local Docker-based NIM deployments instead of the hosted NVIDIA API?▼

Yes, you can seamlessly switch between hosted NVIDIA API endpoints and local Docker-based NIM deployments for inference, allowing flexible deployment depending on your infrastructure preferences.

Do I need NGC API credentials for protein folding with OpenFold2?▼

Yes, valid NGC API credentials are required to authenticate with the OpenFold2 NIM microservices when using the hosted NVIDIA API endpoint for monomer protein structure prediction.

Does this skill support complex protein assemblies or only monomer folding?▼

This skill specifically supports monomer structure prediction. It focuses on generating 3D models of single-chain proteins from amino-acid sequences using OpenFold2 models.

What Python dependencies are required to interface with OpenFold2 NIM?▼

The skill requires the Python requests library to interface with OpenFold2 NIM microservices, handling API calls to the hosted NVIDIA endpoints or local Docker deployments.

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