Deep learning and protein structure modeling

被引：78

作者：

Baek, Minkyung ^{[1
,2
]}

Baker, David ^{[1
,2
,3
]}

机构：

[1] Univ Washington, Dept Biochem, Seattle, WA 98195 USA

[2] Univ Washington, Inst Prot Design, Seattle, WA 98195 USA

[3] Univ Washington, Howard Hughes Med Inst, Seattle, WA 98195 USA

来源：

NATURE METHODS | 2022年 / 19卷 / 01期

关键词：

PREDICTION; DESIGN;

D O I：

10.1038/s41592-021-01360-8

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Deep learning has transformed protein structure modeling. Here we relate AlphaFold and RoseTTAFold to classical physically based approaches to protein structure prediction, and discuss the many areas of structural biology that are likely to be affected by further advances in deep learning.

引用

页码：13 / 14

页数：2

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