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In drug discovery, even if you know which protein to target that would treat a disease, designing a therapeutic molecule that specifically binds that protein is a staggering challenge. Imagine searching for a single, perfectly shaped key in a warehouse of nearly infinite keys—each with a unique three-dimensional shape. This isn't just a metaphor; for a protein of length ‘n’, there can be 20^n possible sequences, each capable of adopting countless conformations. Since the average human protein is 430 amino acids, this represents 20^430 possible sequences, a practically infinite number and more than the number of atoms in the universe (10^80). This potential diversity is so important in the evolution of life but presents a challenge for researchers. In traditional workflows, this complexity means painstaking trial and error—iterating through thousands of candidates, each synthesis and validation round taking months, if not years. The process is expensive, slow, and fraught with uncertainty. Researchers often use educated guesses and hope that a binder emerges from the colossal search. Here, we bring generative AI to bear on the problem, pre-optimizing molecules and screening their interaction with the target protein. This BioNeMo blueprint shows how protein binder design can be recast using NIM microservices for protein folding, structure generation, and sequence generation to speed up the development cycle and produce better binders faster.
NVIDIA AI Blueprints are customizable AI workflow examples that equip enterprise developers with NIM microservices, reference code, documentation, and a Helm chart for deployment.
See a complete example of how to get started with this blueprint on the NVIDIA BioNeMo Blueprints GitHub repository
RFdiffusion is capable of generating protein backbones, and ProteinMPNN can label the amino acid sequence. The combination yields sequences that should fold into the protein structure that binds to the specified static target protein structure. Note that proteins are flexible and adopt multiple conformations. This is especially true of antibodies where the binding interface is disordered. This poses a challenge for these models. While AI is making great strides in predicting protein sequences and structures that should bind to target proteins, it’s not perfect.
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This blueprint shows how generative AI and accelerated NIM microservices can design protein binders smarter and faster.