Overview

ProteinMPNN (Protein Message Passing Neural Network) is a cutting-edge, deep learning-based, graph neural network designed to predict amino acid sequences for given protein backbones.

This network leverages evolutionary, functional, and structural information to generate sequences that are likely to fold into the desired 3D structures.

The input to the neural network is a 3D structure of a protein in PDB format, and the output is the amino-acid sequence in Multi-FASTA format.

ProteinMPNN is one of many NIMs that you can apply to tasks in biosciences and drug discovery. NIMs make it easy to chain models together to develop a complete in silico drug discovery pipeline.

For example, you can use a ProteinMPNN NIM as a subsequent step after RFdiffusion generative model NIM in order to determine possible amino acid sequence.

In general, NIMs offer a simple and easy-to-deploy route for self-hosted AI applications. Two major advantages that NIMs offer for system administrators and developers are:

• Increased productivity: NIMs enable developers to build generative AI applications quickly, in minutes rather than weeks, by providing a standardized way to add AI capabilities to their applications.

• Simplified deployment: NIMs provide containers that can be easily deployed on various platforms, including clouds, data centers, or workstations, making it convenient for developers to test and deploy their applications.

In the context of protein design and drug development, these advantages can:

• Accelerate lead optimization: Researchers can use NIMs to accelerate the lead optimization process by quickly generating and testing multiple molecular structures, enabling them to identify potential leads more efficiently.

• Streamline data analysis: Researchers can use NIMs to analyze large datasets generated during the drug discovery process, such as molecular dynamics simulations or high-throughput screening data, to identify patterns and trends that can inform the development of new drugs.

• Improve collaboration: NIMs can facilitate collaboration among researchers by providing a standardized platform for sharing and integrating AI models, enabling teams to work together more effectively and efficiently.

• Enhance predictive modeling: Researchers can use NIMs to develop and deploy predictive models that can accurately predict the properties and behavior of molecules, such as their binding affinity or toxicity, enabling them to make more informed decisions during the drug development process.