Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or…

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NVIDIA has released Proteina-Complexa, a generative model for designing de novo protein binders and enzymes. Built on the La-Proteina model, it uses a partially latent flow-matching framework to co-design fully atomistic protein structures and amino acid sequences simultaneously. The model was trained on over 1 million curated structures and incorporates inference-time compute scaling with search algorithms like Beam Search and Best-of-N to iteratively refine candidates. Experimental validation involved testing ~1 million binder candidates against 133 distinct protein targets, achieving nano- and picomolar affinities on several targets including Activin Receptor Type-2A. The model also demonstrated success in designing binders for small molecule targets and carbohydrate surfaces on red blood cells. The post includes a step-by-step CLI guide for setting up and running the binder design pipeline. Source code, model checkpoints, and datasets are released under open licenses.

Designing Protein Binders Using the Generative Model Proteina-Complexa

How to generate your own protein binders using Proteina-Complexa