VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.

Recombinant protein subunit vaccine synthesis in microbes:a role for yeast?

Objectives Recombinant protein subunit vaccines are formulated using protein antigens that have been synthesized in heterologous host cells. Several host cells are available for this purpose, ranging from Escherichia coli to mammalian cell lines. This article highlights the benefits of using yeast as the recombinant host. Key findings The yeast species, Saccharomyces cerevisiae and Pichia pastoris, have been used to optimize the functional yields of potential antigens for the development of subunit vaccines against a wide range of diseases caused by bacteria and viruses. Saccharomyces cerevisiae has also been used in the manufacture of 11 approved vaccines against hepatitis B virus and one against human papillomavirus; in both cases, the recombinant protein forms highly immunogenic virus-like particles. Summary Advances in our understanding of how a yeast cell responds to the metabolic load of producing recombinant proteins will allow us to identify host strains that have improved yield properties and enable the synthesis of more challenging antigens that cannot be produced in other systems. Yeasts therefore have the potential to become important host organisms for the production of recombinant antigens that can be used in the manufacture of subunit vaccines or in new vaccine development.

Improving T cell-induced response to subunit vaccines:opportunities for a proteomic systems approach

Prophylactic vaccines are an effective strategy to prevent development of many infectious diseases. With new and re-emerging infections posing increasing risks to food stocks and the health of the population in general, there is a need to improve the rationale of vaccine development. One key challenge lies in development of an effective T cell-induced response to subunit vaccines at specific sites and in different populations. Objectives: In this review, we consider how a proteomic systems-based approach can be used to identify putative novel vaccine targets, may be adopted to characterise subunit vaccines and adjuvants fully. Key findings: Despite the extensive potential for proteomics to aid our understanding of subunit vaccine nature, little work has been reported on identifying MHC 1-binding peptides for subunit vaccines generating T cell responses in the literature to date. Summary: In combination with predictive and structural biology approaches to mapping antigen presentation, proteomics offers a powerful and as yet un-tapped addition to the armoury of vaccine discovery to predict T-cell subset responses and improve vaccine design strategies.