Improving consensus contact prediction via server correlation reduction

doi:10.1186/1472-6807-9-28

	Improving consensus contact prediction via server correlation reduction
	Gao, Xin 1; Bu, Dongbo 1,2; Xu, Jinbo 3; Li, Ming 1
	2009-05-06
发表期刊	BMC STRUCTURAL BIOLOGY
ISSN	1471-2237
卷号	9 页码:14
摘要	Background: Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results: In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion: Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.
DOI	10.1186/1472-6807-9-28
收录类别	SCI
语种	英语
资助项目	China's Ministry of Science and Technology, Canada Research Chair program, MITACS[OGP0046506, 863] ; China's Ministry of Science and Technology, Canada Research Chair program, MITACS[2008AA02Z313] ; SHARCNET ; National Natural Science Foundation of China[30800168]
WOS研究方向	Biophysics
WOS类目	Biophysics
WOS记录号	WOS:000266987200001
出版者	BIOMED CENTRAL LTD
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/11560
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Ming
作者单位	1.Univ Waterloo, David R Cheriton Sch Comp Sci, Waterloo, ON N2L 3G1, Canada 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 3.Toyota Technol Inst, Chicago, IL 60637 USA
推荐引用方式 GB/T 7714	Gao, Xin,Bu, Dongbo,Xu, Jinbo,et al. Improving consensus contact prediction via server correlation reduction[J]. BMC STRUCTURAL BIOLOGY,2009,9:14.
APA	Gao, Xin,Bu, Dongbo,Xu, Jinbo,&Li, Ming.(2009).Improving consensus contact prediction via server correlation reduction.BMC STRUCTURAL BIOLOGY,9,14.
MLA	Gao, Xin,et al."Improving consensus contact prediction via server correlation reduction".BMC STRUCTURAL BIOLOGY 9(2009):14.