Studies on the major histocompatibility complex-peptide binding problem based on bioinformatics approaches

Abstract

The major histocompatibility complex (MHC) molecules are a population of cell membrane proteins in all vertebrates which are capable of binding to antigens and presenting them to the T-cell receptors that are antigen-binding molecules expressed on the membrane of T cells. Different from B-cell receptors which can bind to free antigens, T-cell receptors only recognize antigens that are bound to MHC molecules. The interaction of T cells with MHC-peptide complexes is necessary for the T or B lymphocytes to proliferate and differentiate into effector cells or memory cells. Identification and characterization of MHC binding peptides, especially T-cell epitopes greatly enhance the ability to study their fundamental role in controlling immune responses and their involvement in vaccination, transplantation and autoimmunity. This work focuses on studying MHC-peptide interaction and characterizing MHC binding specificities by using computational approaches. The first specific aim of this work focuses on designing promising tools to predict MHC (class I and II) binding peptides. These approaches contributed to uncovering the relationship between MHC molecules and antigenic peptides and also predicting candidate T-cell epitopes for immunotherapies and vaccines. The second specific aim of this work focuses on understanding the binding specificities of MHC molecules. Based on this understanding, this part of the thesis contributed to helping the designation of high population coverage vaccines. Overall, the computational approaches described in this thesis enabled us to get greater insights into MHC, peptide and their interactions. Such a study enabled us to understand specificity of immune responses. We have also provided promising tools for T-cell epitope discovery as well as suitable targets for immunotherapies and vaccines. This thesis has hence contributed to answering the fundamental question to possibly predict the immunogenicity of any protein antigen of interest.

Date of Award	14 Feb 2014
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Hau San WONG (Supervisor)