title: Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties.
abstract: Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .
authors.affiliation: Bioinformatics and Drug Design Group, Department of Pharmacy and Department of Computational Science, National University of Singapore, Singapore 117543, Republic of Singapore.
authors.forename: J
authors.initials: J
authors.lastname: Cui
authors.forename: L Y
authors.initials: LY
authors.lastname: Han
authors.forename: H H
authors.initials: HH
authors.lastname: Lin
authors.forename: H L
authors.initials: HL
authors.lastname: Zhang
authors.forename: Z Q
authors.initials: ZQ
authors.lastname: Tang
authors.forename: C J
authors.initials: CJ
authors.lastname: Zheng
authors.forename: Z W
authors.initials: ZW
authors.lastname: Cao
authors.forename: Y Z
authors.initials: YZ
authors.lastname: Chen
mesh_terms: D000483:Alleles; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D019295:Computational Biology / Q000379:methods; D018984:Epitopes, T-Lymphocyte / Q000276:immunology; D005544:Forecasting; D005805:Genes, MHC Class I; D005802:Genes, MHC Class II; D015497:HIV-1 / Q000235:genetics; D006680:HLA Antigens / Q000235:genetics / Q000276:immunology*; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000737:chemistry / Q000276:immunology*
chemical_list: D000596:Amino Acids; D018984:Epitopes, T-Lymphocyte; D006680:HLA Antigens; C408902:MHC binding peptide; D009842:Oligopeptides