ITP OpenIR  > SCI期刊论文
Zhang, Haicang1; Zhang, Qi1; Ju, Fusong1; Zhu, Jianwei1; Gao, Yujuan2; Xie, Ziwei4; Deng, Minghua2; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo1
Predicting protein inter-residue contacts using composite likelihood maximization and deep learning
Source PublicationBMC BIOINFORMATICS
Language英语
KeywordCOUPLING ANALYSIS SEQUENCE EVOLUTIONARY INFORMATION NETWORKS RANK
AbstractBackground Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. Results In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. Conclusions Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.
2019
ISSN1471-2105
Volume20Issue:1Pages:537
Cooperation Status国内
Subject AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
MOST Discipline CatalogueBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
DOI10.1186/s12859-019-3051-7
Indexed BySCIE
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/27055
CollectionSCI期刊论文
Affiliation1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Peking Univ, Sch Math Sci, Ctr Quantitat Biol, Ctr Stat Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Theoret Phys, Beijing, Peoples R China
5.Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Wuhan, Hubei, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Haicang,Zhang, Qi,Ju, Fusong,et al. Predicting protein inter-residue contacts using composite likelihood maximization and deep learning[J]. BMC BIOINFORMATICS,2019,20(1):537.
APA Zhang, Haicang.,Zhang, Qi.,Ju, Fusong.,Zhu, Jianwei.,Gao, Yujuan.,...&Bu, Dongbo.(2019).Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.BMC BIOINFORMATICS,20(1),537.
MLA Zhang, Haicang,et al."Predicting protein inter-residue contacts using composite likelihood maximization and deep learning".BMC BIOINFORMATICS 20.1(2019):537.
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