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1、BullMathBiol(2013)75:124160DOI10.1007/s11538-012-9797-yORIGINALARTICLECoarseGrainedNormalModeAnalysisvs.RefinedGaussianNetworkModelforProteinResidue-LevelStructuralFluctuationsJun-KooPark·RobertJernigan·ZhijunWuReceived:9May2012/Accepted:8November2012/Publishedonline:8January2
2、013©SocietyforMathematicalBiology2012AbstractWeinvestigateseveralapproachestocoarsegrainednormalmodeanal-ysisonproteinresidual-levelstructuralfluctuationsbychoosingdifferentwaysofrepresentingtheresiduesandtheforcesamongthem.Single-atomrepresentationsusingthebackboneatomsCα,C,N
3、,andCβareconsidered.Combinationsofsomeoftheseatomsarealsotested.TheforceconstantsbetweentherepresentativeatomsareextractedfromtheHessianmatrixoftheenergyfunctionandservedastheforceconstantsbetweenthecorrespondingresidues.Theresiduemean-square-fluctuationsandtheircorrelationswi
4、ththeexperimentalB-factorsarecalculatedforalargesetofproteins.Theresultsarecomparedwithall-atomnormalmodeanalysisandtheresidue-levelGaussianNetworkModel.Thecoarse-grainedmethodsperformmoreefficientlythanall-atomnormalmodeanalysis,whiletheirB-factorcorrelationsarealsohigher.The
5、irB-factorcorrelationsarecomparablewiththoseestimatedbytheGaussianNetworkModelandinmanycasesbetter.Theextractedforceconstantsaresurveyedfordifferentpairsofresidueswithdifferentnumbersofseparationresiduesinsequence.ThestatisticalaveragesareusedtobuildarefinedGaussianNetworkMode
6、l,whichisabletopredictresidue-levelstructuralfluctuationssignificantlybet-terthantheconventionalGaussianNetworkModelinmanytestcases.J.-K.Park()·Z.WuDepartmentofMathematics,IowaStateUniversity,Ames,IA50010,USAe-mail:jun-koo.park@houghton.eduR.JerniganDepartmentofBiochemistry,Bi
7、ophysics,andMolecularBiology,IowaStateUniversity,Ames,IA50010,USAR.Jernigan·Z.WuProgramonBioinformaticsandComputationalBiology,IowaStateUniversity,Ames,IA50010,USACoarse-grainedNMAandrefinedGNM125KeywordsProteinstructuralfluctuation·Normalmodeanalysis·GaussianNetworkModel·Atomi
8、cB-factors·Atomicmean-square-fluctuations·Residuemean-square-fluctuati