Development of immunoinformatics tools for the discovery of T-cell epitope recognition rules.

Herpes viruses are ubiquitous in human society and cause several common diseases, such as cold sores (Herpes simplex) and chickenpox (Varicella). The eight species of herpes viruses known to primarily infect humans are all clinically relevant and of these, five are known to be extremely widespread amongst humans with seroprevalence rates as high as 90%. Not all individuals are equally susceptible to equivalent viral pathogens. After infection, some individuals do not become symptomatic, while others experience a high severity of the disease with serious complications. For example, a relatively benign disease such as chickenpox can become life-threatening in a small set of individuals. These differences in disease susceptibility are likely to be caused in part due to the variation in the human immune system, but remain largely unknown up to date. A key step in the activation of the adaptive immune system is the presentation of viral epitopes, usually peptides (p), by the major histocompatibility complex (MHC) present on antigen presenting cells (APC) and the recognition of this complex by a T-cell receptor (TCR). There exist many allelic variants of the genes coding for the MHC genes within the population and each variant has a different propensity to bind immunogenic (viral) peptides. This variability in the MHC alleles is one of the underlying factors that leads to differences in disease susceptibility. Previous research has demonstrated that high accuracy models can be established for the affinity of the MHC molecules for the presentation of peptides, based on machine learning methods. The resulting affinity prediction models have made it possible to assess the affinity for almost all human MHC alleles for any given peptide. However, the MHC recognition variability is only part of the story, as each individual has a unique repertoire of T-cells with a large diversity of TCR variants. The variability in TCR epitope recognition is also an important factor in differences between individual immune responses. Unfortunately, few TCR recognition models exist and they are all very limited in scope and accuracy. Therefore, the scope of this project is to develop, evaluate and apply state-of-the-art computational approaches to enable the interpretation of complex MHC-p-TCR interaction data and to elucidate the patterns that govern this system. Within this scope, a key point of interest will be the modelling of the molecular interaction between the MHC complex, encoded by its corresponding HLA allele, the antigen-specific TCR and the peptide antigen itself. Ultimately, this will result in the development of computational tools capable of predicting personalized immune responses to Herpes viruses and the efficacy of vaccine-induced viral protection.

Keywords:PATTERN MINING, BIOINFORMATICS, IMMUNOLOGY, DATA MINING

Disciplines:Scientific computing, Bioinformatics and computational biology, Immunology, Public health care, Public health services