Improving de novo protein binder design with deep learning

Nathaniel R. Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven Munck, Savvas N. Savvides and David Baker ()
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Nathaniel R. Bennett: University of Washington
Brian Coventry: University of Washington
Inna Goreshnik: University of Washington
Buwei Huang: University of Washington
Aza Allen: University of Washington
Dionne Vafeados: University of Washington
Ying Po Peng: University of Washington
Justas Dauparas: University of Washington
Minkyung Baek: University of Washington
Lance Stewart: University of Washington
Frank DiMaio: University of Washington
Steven Munck: VIB-UGent Center for Inflammation Research
Savvas N. Savvides: VIB-UGent Center for Inflammation Research
David Baker: University of Washington

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

Date: 2023
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DOI: 10.1038/s41467-023-38328-5

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