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
ISSN1367-4803
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
DOI10.1093/bioinformatics/btx722
收录类别SCI
语种英语
WOS记录号WOS:000428840000008
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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
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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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