An in Silico Vaccine Design Based on T-Cell Epitope Prediction

An immune system is a system of biological structures and processes within an organism that protects against diseases. When a host is infected by pathogens, physiological functions of the immune system trigger protective responses induced by parts of proteins known as epitopes. However, despite recent technical advances, experimental determination of epitope binding remains time-consuming and labor-intensive. Thus, using computational approaches to extract immunological features from sequences have become highly important to understand relationship between hosts and pathogens and facilitate in silico epitope-based vaccine design. In this project, we propose T-cell epitope prediction methods, in which incorporate various physicochemical properties as feature representation, correspondence analysis as feature reduction, and compact set analysis and support vector machines as classification techniques. This study can help discover new vaccines and therapies for human immunodeficiency virus (HIV), malaria, tuberculosis, and influenza. Moreover, the proposed immunological features can provide valuable insights into the nature of cancer, allergy, and autoimmune diseases. We will propose a systematic approach for qualitative T-cell epitope identification, followed by quantitative major histocompatibility complex (MHC) binding affinity prediction, and further refined by antigen-processing event analysis. First, we will aim at developing predictors for T-cell epitopes, including cytotoxic T lymphocyte and helper T lymphocyte epitopes. First, we will identify the biological, physical, and chemical factors that are key players in determining immunogenicity. Then, feature extraction, feature representation, and feature reduction will be applied on various physicochemical properties. Finally, a hybrid prediction system combined with compact sets and support vector machines will be proposed, and immunological insights will be drawn from our results with references to vaccine design. Second, we will further develop methods to estimate MHC binding affinity based on biological features proposed from T-cell epitope prediction. We will first apply our experience learned from RNA-binding site identification to predict binding affinity for MHC class I and class II molecules. Afterwards, since evolutionary information and physicochemical properties have been shown effective to infer binding affinity, we will incorporate more biological features and extend our approach to predict pan-specific MHC binding. Third, events in antigen-processing pathway also play an important role for epitope prediction. We will develop systems for predicting proteasomal cleavage sites and transporter associated with antigen processing (TAP) binders. Finally, a refined epitope prediction method will be integrated by T-cell identification, MHC binding prediction, and antigen-processing events. In the coming three years, we will endeavor to develop useful bioinformatics tools and propose interpretable biological features that can be used collectively to assist immunologist in epitope-based vaccine design.