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bioRxiv · 10.1101/2024.11.13.623451

NERVE 2.0: boosting the New Enhanced Reverse Vaccinology Environment via artificial intelligence and a user-friendly web interface

Conte, A.·Gulmini, N.·Costa, F.·Cartura, M.·Brohl, F.·Patane, F.·Filippini, F.
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Abstract

BackgroundVaccines development in this millennium started by the milestone work on Neisseria meningitidis B, reporting the invention of Reverse Vaccinology (RV), which allows to identify vaccine candidates (VCs) by screening bacterial pathogens genome or proteome through computational analyses. When NERVE (New Enhanced RV Environment), the first RV software integrating tools to perform the selection of VCs, was released, it prompted further development in the field. However, the problem-solving potential of most, if not all, RV programs is still largely unexploited by experimental vaccinologists that impaired by somehow difficult interfaces, requiring bioinformatic skills. ResultsWe report here on the development and release of NERVE 2.0 (available at: https://nerve-bio.org) which keeps the original integrative and modular approach of NERVE, while showing higher predictive performance than its previous version and other web-RV programs (Vaxign and Vaxijen). We renewed some of its modules and added innovative ones, such as Loop Razor, to recover fragments of promising vaccine candidates or epitopepredict for the epitope prediction binding affinities and population coverage. Along with two newly built AI (Artificial Intelligence)-based models: ESPAAN and Virulent. To improve user-friendliness, NERVE was shifted to a tutored, web-based interface, with a noSQL-database to consent the user to submit, obtain and retrieve analysis results at any moment. ConclusionsWith its redesigned and updated environment, NERVE 2.0 allows customisable and refinable bacterial protein vaccine analyses to all different kinds of users.

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Conte, A., Gulmini, N., Costa, F., Cartura, M., Brohl, F., Patane, F., Filippini, F.. 2024-11-15. NERVE 2.0: boosting the New Enhanced Reverse Vaccinology Environment via artificial intelligence and a user-friendly web interface. https://doi.org/10.1101/2024.11.13.623451

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