CandiVF – Candida albicans Virulence Factor Database

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

In silico prediction and immunological validation of common HLA-DRB1-restricted T cell epitopes of Candida albicans secretory aspartyl proteinase 2

ABSTRACT Sap2 is the most abundant virulence factor expressed during Candida infection, and the principal protein known to induce antibody response during Candida infection in humans. Its role in T-cell activation however, has not yet been determined. Sequence analysis revealed that Sap2 contains two variable regions: Var1 and Var2. Computational predictions by the Hotspot Hunter program identified that Var1 contains three candidate T-cell epitopes, whereas Var2 contains four. Thirty-nine overlapping peptides of Sap2 were then synthesized, and tested for their ability to induce proliferation of PBMC from 12 donors. Peptides P11, P17 and P31 exhibited significantly higher proliferative indices when compared with those of other peptides or controls. P17 and P31 are located in the areas of prediction, while P11 is not. There were other peptides outside the prediction areas that could stimulate PBMC proliferation at low levels. Nevertheless, the proliferative noise caused by such peptides was ruled out by IL-2 ELISpot analysis. Only P17 and P31 were shown to induce clonal proliferation of IFN-gamma producing lymphocytes, suggesting that these two peptides contain T cell epitopes. P11, which stimulated IL-2 producing clones, contains a known B-cell epitope. Interestingly, P17 and P31 elicited both Th1 and Th2 cell responses with significant numbers of IL-13 secreting clones in response to stimulation. Taken together, the computer-based T cell epitope prediction method could identify the immunogenic T cell epitopes of C. albicans Sap2 that promiscuously bind to the HLA-DRB1 supertype.

Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes

BACKGROUND

T-cell epitopes that promiscuously bind to multiple alleles of a human leukocyte antigen (HLA) supertype are prime targets for development of vaccines and immunotherapies because they are relevant to a large proportion of the human population. The presence of clusters of promiscuous T-cell epitopes, immunological hotspots, has been observed in several antigens. These clusters may be exploited to facilitate the development of epitope-based vaccines by selecting a small number of hotspots that can elicit all of the required T-cell activation functions. Given the large size of pathogen proteomes, including of variant strains, computational tools are necessary for automated screening and selection of immunological hotspots.

RESULTS

Hotspot Hunter is a web-based computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes through analysis of antigenic diversity. It allows screening and selection of hotspots specific to four common HLA supertypes, namely HLA class I A2, A3, B7 and class II DR. The system uses Artificial Neural Network and Support Vector Machine methods as predictive engines. Soft computing principles were employed to integrate the prediction results produced by both methods for robust prediction performance. Experimental validation of the predictions showed that Hotspot Hunter can successfully identify majority of the real hotspots. Users can predict hotspots from a single protein sequence, or from a set of aligned protein sequences representing pathogen proteome. The latter feature provides a global view of the localizations of the hotspots in the proteome set, enabling analysis of antigenic diversity and shift of hotspots across protein variants. The system also allows the integration of prediction results of the four supertypes for identification of hotspots common across multiple supertypes. The target selection feature of the system shortlists candidate peptide hotspots for the formulation of an epitope-based vaccine that could be effective against multiple variants of the pathogen and applicable to a large proportion of the human population.

CONCLUSION

Hotspot Hunter is publicly accessible at http://antigen.i2r.a-star.edu.sg/hh/. It is a new generation computational tool aiding in epitope-based vaccine design.

Promiscuous T cell epitope prediction of Candida albicans secretory aspartyl protienase family of proteins

Candida albicans is one of the most important opportunistic dimorphic fungi responsible for hospital acquired fungal infection in humans. Candida infection rarely occurs in healthy individuals but it is frequently associated with patients who suffer from acquired immunodeficiency syndromes. To date, there is no effective vaccine against this fungal infection. Herein we demonstrated the use of immunomics to characterize promiscuous T cell epitope of C. albicans virulence factors by utilizing CandiVF, a C. albicans database previously constructed to be equipped with protein sequence analysis tool, three dimensional structure visualization software, sequence variable analysis program and Hotspot Hunter epitope prediction tool. Secretory aspartyl proteinase (Sap) family was chosen as a model to validate the Hotspot Hunter prediction. Analysis of Saps1-10 protein entries from CandiVF database revealed that a consensus T cell epitope was located at the C-terminal region of Saps1-10. The result of the in silico prediction was subsequently validated by conventional immunological methods. By using overlapping peptides span the predicted consensus T cell epitopes of Saps1-10 as stimulators, it was demonstrated that peptides S6 and S7 could stimulate PBMC proliferation in 9 of 12 blood donors. Interestingly, S2, the predicted T cell epitope of Sap2, was able to induce proliferation of all donors' PBMC. ELISpot assay for the detection of gamma-interferon producing clones confirmed that the peptide S2 actually stimulated T cell proliferation. The results suggest that S2 might be a potential candidate for vaccine development against C. albicans infection or to be utilized as an adjuvant to stimulate the pre-existing CD4+ T cell in other vaccine development.