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- W1812852826 abstract "Major histocompatibility complex (MHC) molecules bind antigenic peptides and present them forrecognitionbytheimmunesystem. MHCmoleculesplayanessentialrolein theinitiationandreg-ulationofTcellmediatedimmuneresponses. PeptidesthatbindMHCrepresentpromisingtargetsforthedevelopmentofvaccinesandtherapiesforautoimmunediseasesandcancer. AtypicalhumanMHCclassII(HLA-DR)bindingpeptideis9-35aminoacidlong. Ithasa9-residuebindingcoreandflanking sequences that protrude from the HLA peptide-binding groove. Prediction methods usingweightmatrices,artificialneuralnetworks(ANN),orfuzzyneuralnetworkswerereportedpreviously[1,2,3]. Allofthesemethodsrequiretheidentificationofpeptidebindingcoresformodelbuilding.The peptide alignment step is critical because it is computationally difficult and in some cases anincorrectregionofapeptidemaybeassignedasabindingcore. Inthisstudy,wedescribeahiddenMarkovmodel(HMM)-basedmethodforpredictionofHLA-DR-bindingpeptides. Thismethoddoesnotrequirethealignmentstepformodelbuilding." @default.
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- W1812852826 date "2000-01-01" @default.
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- W1812852826 title "Computational Design of Proteinous Drug Employing Hidden Markov Model" @default.
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- W1812852826 doi "https://doi.org/10.11234/gi1990.11.394" @default.
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