Document Type

Article

Language

eng

Format of Original

13 p.

Publication Date

11-2013

Publisher

Oxford University Press

Source Publication

Briefings in Bioinformatics

Source ISSN

1467-5463

Original Item ID

doi: 10.1093/bib/bbs052

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.

Comments

Accepted version. Briefings in Bioinformatics, Vol. 14, No. 6 (2013): 724-736. DOI. © Oxford University Press 2013. Used with permission.

Mehdi Maadooliat was affiliated with Texas A&M University at the time of publication.

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