Design Protein Binders with NVIDIA's Proteina-Complexa

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or small molecule. The search space for possible amino acid sequence permutations and resulting 3D protein structures for a designed binder is vast, and achieving strong, specific binding requires careful optimization of the interactions between the protein binder and the target.

To address these challenges, NVIDIA has released Proteina-Complexa, a generative model that designs de novo protein binders and enzymes. In this article, we detail the key technologies behind Proteina-Complexa, explore primary use cases, and highlight the extensive experimental validation of generated protein binders. We also provide a step-by-step guide for using the command-line interface to generate your own binders.

The performance of Proteina-Complexa relies on three distinct technical components: the base generative model, the training datasets, and the integration of inference-time compute scaling. Proteina-Complexa generates protein binders through a partially latent flow-matching generation process combined with inference-time compute scaling to steer generation using reward functions. Built on top of the La-Proteina model, Proteina-Complexa uses a partially latent flow-matching framework to generate both fully atomistic binder structures and the corresponding amino acid sequence, called co-design.

This co-design approach enables reasoning at an atomistic level. By generating the amino acid sequence and the fully atomistic structure simultaneously, Proteina-Complexa ensures that the chemical identities and 3D geometry are tightly coupled. This integrated generation allows for the design of precise, high-affinity interfaces that are inherently optimized for folding and synthesis. Training a generative model for protein binder design requires a large amount of structural data on binders and their targets. Proteina-Complexa was trained on over 1 million curated, high-quality experimental and predicted structures from various databases.

Use cases for Proteina-Complexa include protein binders for protein targets and small molecule targets, as well as enzyme design. You can use Proteina-Complexa to design de novo protein binders against disease-relevant targets across indications including oncology, immunology, and neurology. This use case has been experimentally validated with collaborators from various institutions.