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

Handle URI:
http://hdl.handle.net/10754/562280
Title:
Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles
Authors:
Maadooliat, Mehdi; Gao, Xin ( 0000-0002-7108-3574 ) ; Huang, Jianhua Z.
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.
KAUST Department:
Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC); Structural and Functional Bioinformatics Group
Publisher:
Oxford University Press (OUP)
Journal:
Briefings in Bioinformatics
Issue Date:
27-Aug-2012
DOI:
10.1093/bib/bbs052
PubMed ID:
22926831
PubMed Central ID:
PMC3888108
Type:
Article
ISSN:
14675463
Sponsors:
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).
Additional Links:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888108
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMaadooliat, Mehdien
dc.contributor.authorGao, Xinen
dc.contributor.authorHuang, Jianhua Z.en
dc.date.accessioned2015-08-03T09:59:13Zen
dc.date.available2015-08-03T09:59:13Zen
dc.date.issued2012-08-27en
dc.identifier.issn14675463en
dc.identifier.pmid22926831en
dc.identifier.doi10.1093/bib/bbs052en
dc.identifier.urihttp://hdl.handle.net/10754/562280en
dc.description.abstractDespite 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.en
dc.description.sponsorshipThis 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).en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3888108en
dc.subjectAssessment toolsen
dc.subjectDihedral and planar anglesen
dc.subjectHidden Markov modelsen
dc.subjectParametric modelsen
dc.subjectPrincipal component analysisen
dc.subjectProtein conformational samplingen
dc.titleAssessing protein conformational sampling methods based on bivariate lag-distributions of backbone anglesen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalBriefings in Bioinformaticsen
dc.identifier.pmcidPMC3888108en
dc.contributor.institutionDepartment of Statistics, Texas A and M University, 447 Blocker Building, 3143 TAMU, College Station, TX 77843-3143, United Statesen
kaust.authorGao, Xinen
kaust.authorMaadooliat, Mehdien

Related articles on PubMed

All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.