Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles
dc.contributor.author | Maadooliat, Mehdi | |
dc.contributor.author | Gao, Xin | |
dc.contributor.author | Huang, Jianhua Z. | |
dc.date.accessioned | 2015-08-03T09:59:13Z | |
dc.date.available | 2015-08-03T09:59:13Z | |
dc.date.issued | 2012-08-27 | |
dc.identifier.citation | Maadooliat, M., Gao, X., & Huang, J. Z. (2012). Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles. Briefings in Bioinformatics, 14(6), 724–736. doi:10.1093/bib/bbs052 | |
dc.identifier.issn | 14675463 | |
dc.identifier.pmid | 22926831 | |
dc.identifier.doi | 10.1093/bib/bbs052 | |
dc.identifier.uri | http://hdl.handle.net/10754/562280 | |
dc.description.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. © The Author 2012. Published by Oxford University Press. | |
dc.description.sponsorship | This work was supported by grants from NCI (CA57030), NSF (DMS-0907170, DMS-1007618), and Award Numbers KUS-CI-016-04 and GRP-CF-2011-19-P-Gao-Huang, made by King Abdullah University of Science and Technology (KAUST). | |
dc.publisher | Oxford University Press (OUP) | |
dc.relation.url | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888108 | |
dc.subject | Assessment tools | |
dc.subject | Dihedral and planar angles | |
dc.subject | Hidden Markov models | |
dc.subject | Parametric models | |
dc.subject | Principal component analysis | |
dc.subject | Protein conformational sampling | |
dc.title | Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles | |
dc.type | Article | |
dc.contributor.department | Applied Mathematics and Computational Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computational Bioscience Research Center (CBRC) | |
dc.contributor.department | Structural and Functional Bioinformatics Group | |
dc.identifier.journal | Briefings in Bioinformatics | |
dc.identifier.pmcid | PMC3888108 | |
dc.contributor.institution | Department of Statistics, Texas A and M University, 447 Blocker Building, 3143 TAMU, College Station, TX 77843-3143, United States | |
kaust.person | Gao, Xin | |
kaust.person | Maadooliat, Mehdi | |
kaust.grant.number | CA57030 | |
kaust.grant.number | GRP-CF-2011-19-P-Gao-Huang | |
kaust.grant.number | KUS-CI-016-04 | |
dc.date.published-online | 2012-08-27 | |
dc.date.published-print | 2013-11-01 |
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Structural and Functional Bioinformatics Group
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Computer Science Program
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Computational Bioscience Research Center (CBRC)
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Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
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