title: CD4+ T-cell epitope prediction using antigen processing constraints.
abstract: T-cell CD4+ epitopes are important targets of immunity against infectious diseases and cancer. State-of-the-art methods for MHC class II epitope prediction rely on supervised learning methods in which an implicit or explicit model of sequence specificity is constructed using a training set of peptides with experimentally tested MHC class II binding affinity. In this paper we present a novel method for CD4+ T-cell eptitope prediction based on modeling antigen-processing constraints. Previous work indicates that dominant CD4+ T-cell epitopes tend to occur adjacent to sites of initial proteolytic cleavage. Given an antigen with known three-dimensional structure, our algorithm first aggregates four types of conformational stability data in order to construct a profile of stability that allows us to identify regions of the protein that are most accessible to proteolysis. Using this profile, we then construct a profile of epitope likelihood based on the pattern of transitions from unstable to stable regions. We validate our method using 35 datasets of experimentally measured CD4+ T cell responses of mice bearing I-Ab or HLA-DR4 alleles as well as of human subjects. Overall, our results show that antigen processing constraints provide a significant source of predictive power. For epitope prediction in single-allele systems, our approach can be combined with sequence-based methods, or used in instances where little or no training data is available. In multiple-allele systems, sequence-based methods can only be used if the allele distribution of a population is known. In contrast, our approach does not make use of MHC binding prediction, and is thus agnostic to MHC class II genotypes.
authors.affiliation: Department of Computer Science, Tulane University, New Orleans, LA, USA; Vector-Borne Infectious Diseases Research Center, Tulane University, New Orleans, LA, USA. Electronic address: rmettu@tulane.edu.
authors.forename: Ramgopal R
authors.initials: RR
authors.lastname: Mettu
authors.affiliation: Department of Biochemistry and Molecular Biology, Tulane School of Medicine, New Orleans, LA, USA.
authors.forename: Tysheena
authors.initials: T
authors.lastname: Charles
authors.affiliation: Department of Biochemistry and Molecular Biology, Tulane School of Medicine, New Orleans, LA, USA.
authors.forename: Samuel J
authors.initials: SJ
authors.lastname: Landry
mesh_terms: D000465:Algorithms; D000818:Animals; D017951:Antigen Presentation; D052179:Antigens, Plant / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology* / Q000378:metabolism; D036022:Capsid Proteins / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D030562:Databases, Protein; D018604:Epitope Mapping / Q000379:methods*; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D015804:HLA-DR4 Antigen / Q000235:genetics / Q000276:immunology* / Q000378:metabolism; D006801:Humans; D016056:Immunodominant Epitopes / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D008810:Mice, Inbred C57BL; D008822:Mice, Transgenic; D018448:Models, Immunological; D011485:Protein Binding; D011487:Protein Conformation; D055550:Protein Stability; D013329:Structure-Activity Relationship
chemical_list: D052179:Antigens, Plant; D036022:Capsid Proteins; D018984:Epitopes, T-Lymphocyte; D015804:HLA-DR4 Antigen; D016056:Immunodominant Epitopes; C018315:hexon capsid protein, Adenovirus; C061312:Bet v 1 allergen, Betula
keywords: CD4+ T-cell response; Epitope prediction; MHC class II; Protein structure