Computer aided selection of candidate vaccine antigens

Abstract

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Figure 1

A schema summarising the components of a generic vaccine. The immunogenic component will typical be an attenuated or chemically-inactivated microbe, a protein antigen, or a poly-epitope or conjugate. This component will be combined with an appropriate delivery mechanism and an adjuvant. Delivery mechanisms and adjuvants overlap in their ability to increase the immunogenicity of a weakly-active vaccine.

Figure 2

Whole Antigen Discovery. Taking a reverse vaccinology dynamic, the process of discovering candidate subunit vaccines begins with a microbial genome, perhaps newly sequence, progresses through an extensive computational stage, ultimately to deliver a shortlist of antigens which can be validated through subsequent laboratory examination. The computational stage can be empirical in nature; this is typified by the statistical approach embodied in vaxijen [98]. Or this stage can be bioinformatic; this involves predicting subcellular location and expression levels and the like. Or, this stage can take the form of a complex mathematical model which uses immunoinformatic models combined with mathematical methods, such as metabolic control theory [116], to predict cell-surface epitope populations.

Figure 3

Factors underlying Immunogenicity As elaborated in the text, the phenomenon of Immunogenicity can be explored through the diversity of underlying individual factors contributing to the instigation of the immune response. These factors can be assigned to the host (epitope recognition), the pathogen (location and expression level), and also factors intrinsic to the protein antigen itself, such as the possession of post-translational danger signals.