A large-scale comparative assessment of methods for residue–residue contact prediction. (31st October 2016)

• Record Type: Journal Article
• Authors: Wuyun, Qiqige; Zheng, Wei; Peng, Zhenling; Yang, Jianyi
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 2(2018:Mar.)
• Page Start: 219
• Page End: 230
• Publication Date: 2016-10-31
• Subjects: residue–residue contact -- correlated mutation -- direct-coupling -- protein structure prediction -- CASP; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw106
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Sequence-based prediction of residue–residue contact in proteins becomes increasingly more important for improving protein structure prediction in the big data era. In this study, we performed a large-scale comparative assessment of 15 locally installed contact predictors. To assess these methods, we collected a big data set consisting of 680 nonredundant proteins covering different structural classes and target difficulties. We investigated a wide range of factors that may influence the precision of contact prediction, including target difficulty, structural class, the alignment depth and distribution of contact pairs in a protein structure. We found that: (1) the machine learning-based methods outperform the direct-coupling-based methods for short-range contact prediction, while the latter are significantly better for long-range contact prediction. The consensus-based methods, which combine machine learning and direct-coupling methods, perform the best. (2) The target difficulty does not have clear influence on the machine learning-based methods, while it does affect the direct-coupling and consensus-based methods significantly. (3) The alignment depth has relatively weak effect on the machine learning-based methods. However, for the direct-coupling-based methods and consensus-based methods, the predicted contacts for targets with deeper alignment tend to be more accurate. (4) All methods perform relatively better on β and α + β proteins than on α proteins. (5) Residues buried in the core of protein structure are more prone to be in contact than residues on the surface (22 versus 6%). We believe these are useful results for guiding future development of new approach to contact prediction.