A large-scale comparative assessment of methods for residue-residue contact prediction.
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.
School of Mathematical Sciences, Nankai University, Tianjin, China.
Qiqige
Q
Wuyun
School of Mathematical Sciences, Nankai University, Tianjin, China.
Wei
W
Zheng
Center for Applied Mathematics, Tianjin University, Tianjin, China.
Zhenling
Z
Peng
School of Mathematical Sciences, Nankai University, Tianjin, China.
Jianyi
J
Yang
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins