title: Artificial intelligence methods for predicting T-cell epitopes.
abstract: Identifying epitopes that elicit a major histocompatibility complex (MHC)-restricted T-cell response is critical for designing vaccines for infectious diseases and cancers. We have applied two artificial intelligence approaches to build models for predicting T-cell epitopes. We developed a support vector machine to predict T-cell epitopes for an MHC class I-restricted T-cell clone (TCC) using synthesized peptide data. For predicting T-cell epitopes for an MHC class II-restricted TCC, we built a shift model that integrated MHC-binding data and data from T-cell proliferation assay against a combinatorial library of peptide mixtures.
authors.affiliation: National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
authors.forename: Yingdong
authors.initials: Y
authors.lastname: Zhao
authors.forename: Myong-Hee
authors.initials: MH
authors.lastname: Sung
authors.forename: Richard
authors.initials: R
authors.lastname: Simon
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000378:metabolism; D000949:Histocompatibility Antigens Class II / Q000378:metabolism; D006801:Humans; D007125:Immunogenetics; D019151:Peptide Library; D011485:Protein Binding
chemical_list: D018984:Epitopes, T-Lymphocyte; D000949:Histocompatibility Antigens Class II; D019151:Peptide Library