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SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence.

Manoj Bhasin·G P S Raghava
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Abstract

Prediction of peptides binding with MHC class II allele HLA-DRB1(*)0401 can effectively reduce the number of experiments required for identifying helper T cell epitopes. This paper describes support vector machine (SVM) based method developed for identifying HLA-DRB1(*)0401 binding peptides in an antigenic sequence. SVM was trained and tested on large and clean data set consisting of 567 binders and equal number of non-binders. The accuracy of the method was 86% when evaluated through 5-fold cross-validation technique.

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AlgorithmsAmino Acid SequenceAntigensArtificial IntelligenceBinding SitesBinding Sites, AntibodyCluster AnalysisComputing MethodologiesDrug DesignEpitopes, T-LymphocyteHLA-DR AntigensHLA-DRB1 ChainsMolecular Sequence DataNeural Networks, ComputerPattern Recognition, AutomatedProtein BindingSequence AlignmentSequence Analysis, ProteinSoftwareComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Manoj Bhasin, G P S Raghava. 2004-01-22. SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence.. https://doi.org/10.1093/bioinformatics%2Fbtg424

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