CD4+ T-cell epitope prediction using antigen processing constraints

• Published in: Journal of immunological methods Vol. 432; pp. 72 - 81
• Main Authors: Mettu, Ramgopal R., Charles, Tysheena, Landry, Samuel J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2016
• Subjects: Algorithms; alleles; Animals; Antigen Presentation; Antigens, Plant - chemistry; Antigens, Plant - immunology; Antigens, Plant - metabolism; artificial intelligence; Capsid Proteins - chemistry; Capsid Proteins - immunology; Capsid Proteins - metabolism; CD4+ T-cell response; CD4-positive T-lymphocytes; CD4-Positive T-Lymphocytes - immunology; CD4-Positive T-Lymphocytes - metabolism; data collection; Databases, Protein; Epitope Mapping - methods; Epitope prediction; epitopes; Epitopes, T-Lymphocyte - chemistry; Epitopes, T-Lymphocyte - immunology; Epitopes, T-Lymphocyte - metabolism; genotype; HLA-DR4 Antigen - genetics; HLA-DR4 Antigen - immunology; HLA-DR4 Antigen - metabolism; Humans; immunity; Immunodominant Epitopes - chemistry; Immunodominant Epitopes - immunology; Immunodominant Epitopes - metabolism; infectious diseases; MHC class II; mice; Mice, Inbred C57BL; Mice, Transgenic; Models, Immunological; neoplasms; peptides; prediction; Protein Binding; Protein Conformation; Protein Stability; Protein structure; proteolysis; Structure-Activity Relationship; CD4+ T-cell response; MHC class II; Epitope prediction; Protein structure
• ISSN: 0022-1759; 1872-7905; 1872-7905
• DOI: 10.1016/j.jim.2016.02.013

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.