title: Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles.
abstract: 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/∼madoliat/LagSVD) that can be used to produce informative animations.
authors.affiliation: Mathematical and Computer Sciences and Engineering Division, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia, xin.gao@kaust.edu.sa. Jianhua Z. Huang, Department of Statistics, 447 Blocker Building, Texas A&M University, 3143 TAMU, College Station, TX 77843-3143 (USA), E-mail: jianhua@stat.tamu.edu.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Maadooliat
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.forename: Jianhua Z
authors.initials: JZ
authors.lastname: Huang
mesh_terms: D008390:Markov Chains; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins