CAS OpenIR  > 中科院上海应用物理研究所2011-2019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
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume34Issue:7Pages:1132-1140
Subtype期刊论文
AbstractMotivation: 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.
KeywordSecondary Structure Prediction Coevolution Methods Database Stepwise Contact Rosetta Server Algorithm Quality Model
DOI10.1093/bioinformatics/btx722
Indexed BySCI
Language英语
WOS IDWOS:000428840000008
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sinap.ac.cn/handle/331007/29130
Collection中科院上海应用物理研究所2011-2019年
Affiliation1.de Oliveira, SHP
2.Law, EC
3.Shi, JY
4.Deane, CM
Recommended Citation
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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