Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

Journal: BMC bioinformatics
Published Date: Oct 29, 2019

Abstract

BACKGROUND: 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.

Authors

  • Haicang Zhang
    Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Fusong Ju
    Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Jianwei Zhu
    College of Food Science and Engineering, Jilin Agricultural University, Changchun 130118, China.
  • Yujuan Gao
    Center for Quantitative Biology, Peking University, Beijing, China.
  • Ziwei Xie
    Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, Hubei, China.
  • Minghua Deng
    Center for Quantitative Biology, Peking University, Beijing, China. dengmh@pku.edu.cn.
  • Shiwei Sun
    Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Wei-Mou Zheng
    Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China. zheng@itp.ac.cn.
  • Dongbo Bu
    Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

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