Yang Li1,2, Jun Hu1,2, Chengxin Zhang2, Dong-Jun Yu1, Yang Zhang2. 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. 2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA.
Abstract
MOTIVATION: Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. RESULTS: We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do not have close homology templates in the Protein Data Bank. RESULTS: We developed a new method, ResPRE, to predict residue-level protein contacts using inverse covariance matrix (or precision matrix) of multiple sequence alignments (MSAs) through deep residual convolutional neural network training. The approach was tested on a set of 158 non-homologous proteins collected from the CASP experiments and achieved an average accuracy of 50.6% in the top-L long-range contact prediction with L being the sequence length, which is 11.7% higher than the best of other state-of-the-art approaches ranging from coevolution coupling analysis to deep neural network training. Detailed data analyses show that the major advantage of ResPRE lies at the utilization of precision matrix that helps rule out transitional noises of contact-maps compared with the previously used covariance matrix. Meanwhile, the residual network with parallel shortcut layer connections increases the learning ability of deep neural network training. It was also found that appropriate collection of MSAs can further improve the accuracy of final contact-map predictions. The standalone package and online server of ResPRE are made freely available, which should bring important impact on protein structure and function modeling studies in particular for the distant- and non-homology protein targets. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/ResPRE and https://github.com/leeyang/ResPRE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Faruck Morcos; Andrea Pagnani; Bryan Lunt; Arianna Bertolino; Debora S Marks; Chris Sander; Riccardo Zecchina; José N Onuchic; Terence Hwa; Martin Weigt Journal: Proc Natl Acad Sci U S A Date: 2011-11-21 Impact factor: 11.205
Authors: Chengxin Zhang; Wei Zheng; Micah Cheng; Gilbert S Omenn; Peter L Freddolino; Yang Zhang Journal: J Proteome Res Date: 2021-01-04 Impact factor: 4.466