A Lean Reverse Vaccinology Pipeline with Publicly Available Bioinformatic Tools

Bart Cuypers; Rino Rappuoli; Alessandro Brozzi

doi:10.1007/978-1-0716-3239-0_24

A Lean Reverse Vaccinology Pipeline with Publicly Available Bioinformatic Tools

Methods Mol Biol. 2023:2673:341-356. doi: 10.1007/978-1-0716-3239-0_24.

Authors

Bart Cuypers^{1

2}, Rino Rappuoli³, Alessandro Brozzi⁴

Affiliations

¹ Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
² Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), Antwerp, Belgium.
³ Fondazione Biotecnopolo, Siena, Italy.
⁴ GSK, Siena, Italy. alessandro.x.brozzi@gsk.com.

PMID: 37258926
DOI: 10.1007/978-1-0716-3239-0_24

Abstract

Reverse vaccinology (RV) marked an outstanding improvement in vaccinology employing bioinformatics tools to extract effective features from protein sequences to drive the selection of potential vaccine candidates (Rappuoli, Curr Opin Microbiol 3(5):445-450, 2000). Pioneered by Rino Rappuoli and first used against serogroup B meningococcus, since then, it has been used on several other bacterial vaccines, varying during time the adopted bioinformatics tools. Based on our experience in the field of RV and following an extensive literature review, we consolidate a lean RV pipeline of publicly available bioinformatic tools whose usage is described in this contribution. The protein features, whose extraction is reported in this contribution, can be also the input in a matrix format for machine learning-based approaches.

Keywords: Antigen abundance; B cell epitopes; Bacteria; Core proteome; Reverse vaccinology; Subcellular location; T cell epitopes.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bacterial Vaccines
Computational Biology*
Machine Learning
Vaccinology*

Substances

Bacterial Vaccines