Improving <i>de novo</i> Protein Binder Design with Deep Learning-Reference-Cited by-同舟云学术

Improving de novo Protein Binder Design with Deep Learning

Published:2022-06-17 Issue: Volume: Page:
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Author:

Bennett Nathaniel^ORCID,Coventry Brian^ORCID,Goreshnik Inna^ORCID,Huang Buwei^ORCID,Allen Aza^ORCID,Vafeados Dionne^ORCID,Peng Ying Po^ORCID,Dauparas Justas^ORCID,Baek Minkyung^ORCID,Stewart Lance^ORCID,DiMaio Frank^ORCID,De Munck Steven^ORCID,Savvides Savvas N.^ORCID,Baker David^ORCID

Abstract

AbstractWe 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.

Publisher

Cold Spring Harbor Laboratory

Reference16 articles.

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2. Global analysis of protein folding using massively parallel design, synthesis, and testing

3. Improved protein structure refinement guided by deep learning based accuracy estimation;Nat. Commun,2021

4. Highly accurate protein structure prediction with AlphaFold

5. Accurate prediction of protein structures and interactions using a three-track neural network

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