De novo design of high-affinity protein binders with AlphaProteo
Authors:
Vinicius Zambaldi,
David La,
Alexander E. Chu,
Harshnira Patani,
Amy E. Danson,
Tristan O. C. Kwan,
Thomas Frerix,
Rosalia G. Schneider,
David Saxton,
Ashok Thillaisundaram,
Zachary Wu,
Isabel Moraes,
Oskar Lange,
Eliseo Papa,
Gabriella Stanton,
Victor Martin,
Sukhdeep Singh,
Lai H. Wong,
Russ Bates,
Simon A. Kohl,
Josh Abramson,
Andrew W. Senior,
Yilmaz Alguel,
Mary Y. Wu,
Irene M. Aspalter
, et al. (7 additional authors not shown)
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 learni…
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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.
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Submitted 12 September, 2024;
originally announced September 2024.