NVIDIA BioNeMo Blueprint: Protein Binder Design

The NVIDIA BioNeMo Blueprint for protein binder design shows how generative AI and accelerated NIM microservices can be used to design binders to a target protein sequence smarter and faster. This workflow simplifies the process of in silico protein binder design by automatically generating binder sequences and predicted structures for the binder and target.

This Blueprint takes as input a valid amino acid protein sequence. It utilizes the following models:

• AlphaFold2: A deep learning model for predicting protein structure from amino acid sequence, originally developed by DeepMind.
• ProteinMPNN: a deep learning model for predicting amino acid sequences for protein backbones.
• RFDiffusion: a generative model of protein backbones for protein binder design.
• AlphaFold2-Multimer: A deep learning model for predicting protein structure of multimers from a list of amino acid sequences, originally developed by DeepMind.

Once completed, this Blueprint outputs predicted multimer structures (in PDB format) for the target protein sequence and any generated peptide binders. These binder-target multimeric structures can then be assessed to find binders that effectively bind the target protein.

System Requirements

The docker compose setup for this NIM Agent Blueprint requires the following specifications:

• At least 1300 GB (1.3 TB) of fast NVMe SSD space
• A modern CPU with at least 24 CPU cores
• At least 64 GB of RAM
• Two or more NVIDIA L40s, A100, or H100 GPUs

Get Started

• Deploy with Docker Compose
• Deploy with Helm
• Source code

Quick Start

Deploy the blueprint using Docker Compose or Helm

cd ./src
jupyter notebook

Set Up With Docker Compose

Navigate to the deploy directory to learn how to start up the NIMs.

Set up with Helm

Follow the instructions in the protein-design-chart directory and deploy the Helm chart

Notebook

An example of how to call each protein binder design step is located in src/protein-binder-design.ipynb