De novo design of high-affinity protein binders with AlphaProteo

TL;DR

AlphaProteo is a machine learning framework that significantly improves the design of high-affinity protein binders, reducing experimental effort and achieving superior binding affinities compared to previous methods.

Contribution

Introduction of AlphaProteo, a novel machine learning approach for de novo protein binder design that delivers high-affinity binders with minimal experimental rounds.

Findings

Achieves 3- to 300-fold better binding affinities.

Higher success rates than existing methods.

Generates binders ready for research use after one screening round.

Abstract

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.