Development of bióinformatics tools to analyse peptide-protein interactións: immunoinformatics applicatións in next generatión vaccine design

Abstract

Epitope-based vaccines (EVs) make use of short, antigen-derived peptides corresponding to epitopes that are administered lo trigger a protective humoral (B-cell epitopes) and/or cellular (T-cell epitopes) immune response. They potentially allow for precise control over the immune response activation by focusing on most relevan! (immunogenic and conserved) antigen regions. While cytotoxic T-cells recognize intracellular peptides displayed by MHC class 1 molecules (CD8+ T-cell epitopes), T helper cells recognize peptides from the extracellular space, displayed by MHC class 11 molecules (CD4+ T-cell epitopes). As CD4+ T-cell epitopes play a key role in eliciting vigorous humoral and cytotoxic T-cell responses, their inclusion is essential for a successful EV formulation. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate CD4+ T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of CD4+ T-cell epitopes !acuses on the peptide recognition process by MHC class 11 proteins. A computational method for EV design must implement algorithms for the steps of epitope discovery (epitope prediction) and epitope selection, in arder to identify putative epitopes and to determine the population coverage potentially afforded by a multi-epitope vaccine based on !hose peptides. Both steps, however, involve their own set of challenges; (i) human MHC (HLA) genes are the most polymorphic in the genome (epitope prediction) and (ii) HLA class 11 alleles are expressed al dramatically difieren! frequencies in different ethnic groups. As difieren! HLA proteins (allotypes) have difieren! specificity and epitope repertoires (restriction), individuals are likely to respond lo a difieren! set of peptides from a given pathogen (epitope selection). This thesis describes the development, validation and application of the method Predivac, a new computational tool lo aid HLA class 11-restricted epitope-based vaccine design in the context of a genetically heterogeneous human population, which can cope with both problems previously outlined: 1) Epitope prediction: Predivac performs CD4+ T-cell epitope prediction for 95% of all HLA class 11 DRB allotypes (pan-specific approach). The method is based on the specificity-determining residue (SOR) concept. SDRs are a small group of conserved positions in the peptide-binding interaction interface that are responsible for the specific recognition of the peptide. In addition lo delivering the most comprehensive allele coverage, Predivac outperformed three available pan-specific approaches on CD4+ T-cell epitope prediction (delivering the highest specificity), particularly for immunodominant epitope identification. 2) Epitope selection: We integrated Predivac with the Allele Frequency Net Database (AFND), which is !he most comprehensive repository of immune gene frequencies in worldwide populations. Predivac allows the definition of the target population al tour levels: world, geographic regions, countries and ethnic groups (population samples), according to the information contained in the AFND. Once the user sets the geographic region, the program retrieves all the ethnicities associated with these areas, and from !hose ethnicities retrieves their HLA class 11 allele frequencies. The fraction of individuals predicted to respond to a given epitope in a given population is calculated either by implementing an algorithm that turns genotypic (allele) frequencies into population coverage (simple search; quick and default) or by implementing an optimization (genetic) algorithm (optimised search), which explores in depth difieren! epitopes combinations that simultaneously rnaximise population coverage in all the ethnicities comprising the target population. Predivac accounts comprehensively for the human genetic diversity, thereby it is especially suited for emerging infectious diseases (EIDs). EIDs are mostly zoonoses, i.e. transmitted from animals lo humans. Consequently, the geographic distributions of the viruses are well defined in relation with the natural habita! of the host reservoir and the ethnic populations in need of vaccination can be determined. The performance of the tool in the identification of promiscuous and immunodominant CD4+ T-cell epitopes was tested using validated epitope maps of the human immunodeficiency virus protein Gag. To demonstrate the utility of Predivac, EV design was carried out for the EIDs caused by Lassa, Nipah and Hendra viruses. Putative CD4+ T-cell epitopes were mapped in surface glycoproteins of these pathogens, which are good candidates to be experimentally tested, as they hold potential to provide cognate help in vaccination settings in their respective target populations. Predivac is accessible through the website http://predivac.biosci.ug.edu.au/.