%0 Journal Article
%T ComputationalpredictionofMHCⅡ-peptideligandsbindingspecificitiesbyAUCOptimizedGibbs
%A 盛浩
%A 卢玉
%A 峰张屹
%J 大连理工大学学报
%P 28-36
%D 2014
%R 10.7511/dllgxb201401005
%X Inthedesignofpeptide-basedorotherdefinedantigen-basedvaccines,itisimportanttoknowwhichfragmentsofpathogen-derivedproteinswouldbindtotheMHCⅡmolecules.MoststudiesoftheMHCⅡepitopepredictionrarelygavethequantitativeanalysesofbindingspecificities.Sotheaccuracyofthesemodelsstillneedstobeimproved.AUCOptimizedGibbs(AOG)methodusesthehomologyreducedAUC,ratherthantherelativeentropytoguidethesampler.Itmakesboththepositiveandnegativeinformationofthesamplesbeincorporatedintothemodel.AOGachievesaverageAUCvaluesof0.771and0.713onthetenoriginalandhomologyreducedHLA-DR4(B1*0401)epitopebenchmarks,whicharebetterthan0.744and0.673bytheGibbssamplingmethod.InthequantitativeIEDBMHC-Ⅱbenchmarks,AOGachievesanaverageAUCvalueof0.766,comparedto0.718bytheTEPITOPE.AdetailedinspectionofinformationextractedfromHLA-DR4(B1*0401)dataallowstheidentificationofpositionswithobviousspecificities,i.e.,P1,P4,P6andP9positions,whichhavedistinctinfluenceontheMHC-peptidebinding.
%K Gibbssamplingmethod
%K epitope
%K MHCⅡmolecules
%K reducedhomology
%U http://press.dlut.edu.cn/ch/reader/view_abstract.aspx?file_no=20140105&flag=1