气候变化领域集成服务门户(CCPortal): Protein sequence design by conformational landscape optimization

CCPortal

DOI	10.1073/PNAS.2017228118
	Protein sequence design by conformational landscape optimization
	Norn C.; Wicky B.I.M.; Juergens D.; Liu S.; Kim D.; Tischer D.; Koepnick B.; Anishchenko I.; Players F.; Baker D.; Ovchinnikov S.
发表日期	2021
ISSN	00278424
卷号	118 期号:11
英文摘要	The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Energy landscape; Machine learning; Protein design; Sequence optimization; Stability prediction
语种	英语
scopus关键词	alternative energy; amino acid sequence; article; calculation; machine learning; prediction
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/181179
作者单位	Department of Biochemistry, University of Washington, Seattle, WA 98105, United States; Institute for Protein Design, University of Washington, Seattle, WA 98105, United States; Graduate Program in Molecular Engineering, University of Washington, Seattle, WA 98105, United States; Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA 02138, United States; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105, United States; John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138, United States
推荐引用方式 GB/T 7714	Norn C.,Wicky B.I.M.,Juergens D.,et al. Protein sequence design by conformational landscape optimization[J],2021,118(11).
APA	Norn C..,Wicky B.I.M..,Juergens D..,Liu S..,Kim D..,...&Ovchinnikov S..(2021).Protein sequence design by conformational landscape optimization.Proceedings of the National Academy of Sciences of the United States of America,118(11).
MLA	Norn C.,et al."Protein sequence design by conformational landscape optimization".Proceedings of the National Academy of Sciences of the United States of America 118.11(2021).