RRCRank: a fusion method using rank strategy for residue-residue contact prediction

doi:10.1186/s12859-017-1811-9

CSpace > 数学所

	RRCRank: a fusion method using rank strategy for residue-residue contact prediction
	Jing,Xiaoyang 1; Dong,Qiwen 2; Lu,Ruqian1
	2017-09-02
发表期刊	BMC Bioinformatics
ISSN	1471-2105
卷号	18 期号:1
摘要	AbstractBackgroundIn structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair.ResultsFirst, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics.ConclusionsThe learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment.
关键词	Protein residue-residue contact prediction Learning-to-rank Fusion method
DOI	10.1186/s12859-017-1811-9
语种	英语
WOS记录号	BMC:10.1186/s12859-017-1811-9
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/381
专题	数学所
通讯作者	Dong,Qiwen
作者单位	1. 2.
推荐引用方式 GB/T 7714	Jing,Xiaoyang,Dong,Qiwen,Lu,Ruqian. RRCRank: a fusion method using rank strategy for residue-residue contact prediction[J]. BMC Bioinformatics,2017,18(1).
APA	Jing,Xiaoyang,Dong,Qiwen,&Lu,Ruqian.(2017).RRCRank: a fusion method using rank strategy for residue-residue contact prediction.BMC Bioinformatics,18(1).
MLA	Jing,Xiaoyang,et al."RRCRank: a fusion method using rank strategy for residue-residue contact prediction".BMC Bioinformatics 18.1(2017).