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 De Munck,Savvas N. Savvides,David Baker

bioRxiv (Cold Spring Harbor Laboratory)（2022）

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摘要

Abstract We explore the improvement 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.

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protein binder design,deep learning

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