Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction

doi:10.1093/bioinformatics/btx722

CAS OpenIR > 中科院上海应用物理研究所2011-2018年

	Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction
	de Oliveira, SHP ; Law, EC ; Shi, JY ; Deane, CM
	2018
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	34 期号:7 页码:1132-1140
文章类型	期刊论文
摘要	Motivation: Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. Results: We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models ( TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy.
关键词	Secondary Structure Prediction Coevolution Methods Database Stepwise Contact Rosetta Server Algorithm Quality Model
DOI	10.1093/bioinformatics/btx722
收录类别	SCI
语种	英语
WOS记录号	WOS:000428840000008
引用统计
文献类型	期刊论文
条目标识符	http://ir.sinap.ac.cn/handle/331007/29130
专题	中科院上海应用物理研究所2011-2018年
作者单位	1.de Oliveira, SHP 2.Law, EC 3.Shi, JY 4.Deane, CM
推荐引用方式 GB/T 7714	de Oliveira, SHP,Law, EC,Shi, JY,et al. Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction[J]. BIOINFORMATICS,2018,34(7):1132-1140.
APA	de Oliveira, SHP,Law, EC,Shi, JY,&Deane, CM.(2018).Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.BIOINFORMATICS,34(7),1132-1140.
MLA	de Oliveira, SHP,et al."Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction".BIOINFORMATICS 34.7(2018):1132-1140.

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