ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks
Year: 2019
Type: article
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.
Source: Bioinformatics
Authors: Yang Li, Jun Hu, Chengxin Zhang, Dong-Jun Yu, Yang Zhang
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Awards: G179862484, G1953852693, G2540165657, G3226045097, XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement, Template-based docking refinement approach to protein-protein structure modeling, 蛋白质-配体绑定区域预测的特征抽取及学习算法研究, G5396060342, G6056929667, I-TASSER based atomic level protein structure prediction, Collaborative Research: ABI Development: Integrated platforms for protein structure and function predictions, G8864257478, 大规模多源异质蛋白质数据挖掘中的若干关键问题研究, Structure-based functional annotation of microbial genomes
OpenAlex ID: W2944535254