Development of bioinformatics tools to analyse peptide-protein interactions: Immunoinformatics applications in next generation vaccine design

Oyarzun, Patricio Alejandro (2013). Development of bioinformatics tools to analyse peptide-protein interactions: Immunoinformatics applications in next generation vaccine design PhD Thesis, School of Chemistry & Molecular Biosciences, The University of Queensland.

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Author Oyarzun, Patricio Alejandro
Thesis Title Development of bioinformatics tools to analyse peptide-protein interactions: Immunoinformatics applications in next generation vaccine design
School, Centre or Institute School of Chemistry & Molecular Biosciences
Institution The University of Queensland
Publication date 2013
Thesis type PhD Thesis
Supervisor Bostjan Kobe
Jonathan Ellis
Total pages 182
Total colour pages 23
Total black and white pages 159
Language eng
Subjects 080301 Bioinformatics Software
030406 Proteins and Peptides
110702 Applied Immunology (incl. Antibody Engineering, Xenotransplantation and T-cell Therapies)
Formatted abstract
Epitope-based vaccines (EVs) make use of short, antigen-derived peptides corresponding to epitopes that are administered to 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 relevant (immunogenic and conserved) antigen regions. While cytotoxic T-cells recognize intracellular peptides displayed by MHC class I molecules (CD8+ T-cell epitopes), T helper cells recognize peptides from the extracellular space, displayed by MHC class II 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 focuses on the peptide recognition process by MHC class II proteins. A computational method for EV design must implement algorithms for the steps of epitope discovery (epitope prediction) and epitope selection, in order to identify putative epitopes and to determine the population coverage potentially afforded by a multi-epitope vaccine based on those 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 II alleles are expressed at dramatically different frequencies in different ethnic groups. As different HLA proteins (allotypes) have different specificity and epitope repertoires (restriction), individuals are likely to respond to a different 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 to aid HLA class II-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 II DRB allotypes (pan-specific approach). The method is based on the specificity-determining residue (SDR) 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 to delivering the most comprehensive allele coverage, Predivac outperformed three available pan-specific approaches on CD4+ T-cell 3 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 the most comprehensive repository of immune gene frequencies in worldwide populations. Predivac allows the definition of the target population at four 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 those ethnicities retrieves their HLA class II 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 different epitopes combinations that  simultaneously maximise 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 to humans. Consequently, the geographic distributions of the viruses are well defined in relation with the natural habitat 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
Keyword Predivac
HLA class II protein
Peptide binding prediction
CD4+ T-cell epitope prediction
Epitope-based vaccination
Pan-specific method
Specificity-determining residues
Emerging infectious disease

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Created: Fri, 19 Jul 2013, 15:20:29 EST by Patricio Oyarzun on behalf of Scholarly Communication and Digitisation Service