Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles

作者：Maadooliat Mehdi; Gao Xin^*; Huang Jianhua Z

来源：Briefings in Bioinformatics, 2013, 14(6): 724-736.

DOI：10.1093/bib/bbs052

• protein conformational sampling
• parametric models
• assessment tools
• hidden Markov models
• principal component analysis
• dihedral and planar angles

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence-structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/similar to madoliat/LagSVD) that can be used to produce informative animations.

• 出版日期2013-11