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.

Towards Reliable Automatic Protein Structure Alignment

“…Such experiment settings are balanced between the accuracy and the speed of our protein structure alignment algorithm because only a minor improvement on accuracy is gained by increasing the sizes, while slowing down the running time. The local fragment size of 9 was previously shown to be the optimal balance between the complexity of the model and the amount of data required to train the model [35,36]. Other experiment settings remained the same as in the previous experiment.…”

Collective Estimation of Multiple Bivariate Density Functions With Application to Angular-Sampling-Based Protein Loop Modeling

“…To take into account the circular nature of the angular data, Maadooliat et al (2013a) suggested the following modified bivariate kernel estimator…”

“…Although most problems in the protein structure field depend on coordinate-based methods, backbone-angle-based methods have provided an attractive alternative approach in various protein structure-related problems, such as protein structure prediction (Simons et al, 1999;Hamelryck et al, 2006;Boomsma et al, 2008;Zhao et al, 2010), protein loop modeling (Ting et al, 2010), model quality assessment (Benkert et al, 2008;Gao et al, 2009;Archie and Karplus, 2009), prediction server ranking (Qiu et al, 2008;Maadooliat et al, 2013a), protein structure alignment (Miao et al, 2008;Challis and Schmidler, 2012), free energy function learning (Mu et al, 2005;Altis et al, 2008;Riccardi et al, 2009), and molecular dynamics simulation (Altis et al, 2007). In this paper, we focus on statistical modeling of the bivariate distribution of protein backbone angles.…”

Compare local pocket and global protein structure models by small structure patterns

“…The list of popular scoring functions includes root mean squared deviation (RMSD), MaxSub score [21], TM-score [34], GDT-TS and GDT-HA scores [32], and LagSVDi [15]. Unfortunately, methods, to optimize the TM-score and the GDT-TS/GDT-HA score, are either unknown or with high time complexity.…”