Abstract
New generation vaccines are in demand to include only the key antigens sufficient to confer protective immunity among the plethora of pathogen molecules. In the last decade, large-scale genomics-based technologies have emerged. Among them, the Reverse Vaccinology approach was successfully applied to the development of an innovative vaccine against Neisseria meningitidis serogroup B, now available on the market with the commercial name BEXSERO® (Novartis Vaccines). The limiting step of such approaches is the number of antigens to be tested in in vivo models. Several laboratories have been trying to refine the original approach in order to get to the identification of the relevant antigens straight from the genome. Here we report a new bioinformatics tool that moves a first step in this direction. The tool has been developed by identifying structural/functional features recurring in known bacterial protective antigens, the so called "Protectome space," and using such "protective signatures" for protective antigen discovery. In particular, we applied this new approach to Staphylococcus aureus and Group B Streptococcus and we show that not only already known protective antigens were re-discovered, but also two new protective antigens were identified.
Highlights
protective signatures (PS) are launched against the conserved pan-genome of the pathogen of interest with the aim at identifying proteins that share some of the signatures identified by Protectome analysis
The best we can achieve is to define criteria that make a protective antigen protective and to use high throughput technologies to identify from all pathogen components those fulfilling such criteria
The number of vaccine candidates to be tested in the animal models of protection, the most demanding and critical part of the whole vaccine discovery process, is remarkably reduced: from a few thousand to a few tens and, in the case of the “immunogenic/compartmentalization combined criterion,” to less than ten [9]
Summary
Protectome Analysis: A New Selective Bioinformatics Tool for Bacterial Vaccine Candidate Discovery□S. Because most of the assays available for protective antigen selection involve animal immunization and challenge, the number of antigens to be tested represents a severe bottleneck of the entire process For this reason, despite the fact that RV is a brute force, inclusive approach (“test-all-to-lose-nothing” type of approach) in their pioneered work of MenB vaccine discovery, Pizza and co-workers did not test the entire collection of MenB proteins but rather restricted their analysis to the ones predicted to be surface-localized. We like to refer to as the “Holy Grail of Vaccinology,” is to identify protective antigens by “” scanning the genome sequence of any given pathogen, avoiding time consuming “wet science” and “move straight from genome to the clinic” [6] With this objective in mind, we have developed a series of proteomics-based protocols that, in combination with bioinformatics tools, have substantially reduced the number of antigens to be tested in the surrogate-of-protection assays [7, 8]. When the approach was applied to Staphylococcus aureus and Streptococcus agalactiae (Group B Streptococcus, GBS) already known protective antigens were rediscovered, and two new protective antigens were identified
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