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. 2022:2414:1-16.
doi: 10.1007/978-1-0716-1900-1_1.

Vaccine Design by Reverse Vaccinology and Machine Learning

Edison Ong  1   2 , Yongqun He  3
Affiliations

Vaccine Design by Reverse Vaccinology and Machine Learning

Edison Ong et al. Methods Mol Biol. 2022.

Abstract

Reverse vaccinology (RV) is the state-of-the-art vaccine development strategy that starts with predicting vaccine antigens by bioinformatics analysis of the whole genome of a pathogen of interest. Vaxign is the first web-based RV vaccine prediction method based on calculating and filtering different criteria of proteins. Vaxign-ML is a new Vaxign machine learning (ML) method that predicts vaccine antigens based on extreme gradient boosting with the advance of new technologies and cumulation of protective antigen data. Using a benchmark dataset, Vaxign-ML showed superior performance in comparison to existing open-source RV tools. Vaxign-ML is also implemented within the web-based Vaxign platform to support easy and intuitive access. Vaxign-ML is also available as a command-based software package for more advanced and customizable vaccine antigen prediction. Both Vaxign and Vaxign-ML have been applied to predict SARS-CoV-2 (cause of COVID-19) and Brucella vaccine antigens to demonstrate the integrative approach to analyze and select vaccine candidates using the Vaxign platform.

Keywords: Antigen; Machine learning; Reverse vaccinology; Vaccine; Vaxign; Vaxign-ML; Vaxitop.

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References

    1. Pizza M, Scarlato V, Masignani V, Giuliani MM, Aricò B, Comanducci M, Jennings GT, Baldi L, Bartolini E, Capecchi B, Galeotti CL, Luzzi E, Manetti R, Marchetti E, Mora M, Nuti S, Ratti G, Santini L, Savino S, Scarselli M, Storni E, Zuo P, Broeker M, Hundt E, Knapp B, Blair E, Mason T, Tettelin H, Hood DW, Jeffries AC, Saunders NJ, Granoff DM, Venter JC, Moxon ER, Grandi G, Rappuoli R (2000) Identification of vaccine candidates against serogroup B meningococcus by whole-genome sequencing. Science 287:1816–1820. https://doi.org/10.1126/science.287.5459.1816 - DOI - PubMed
    1. Vernikos G, Medini D (2014) Bexsero H chronicle. Pathog Glob Health 108:305–311. https://doi.org/10.1179/2047773214Y.0000000162 - DOI - PubMed - PMC
    1. Folaranmi T, Rubin L, Martin SW, Patel M, MacNeil JR (2015) Use of serogroup B meningococcal vaccines in persons aged≥ 10 years at increased risk for serogroup B meningococcal disease: recommendations of the advisory committee on immunization practices, 2015. MMWR Morb Mortal Wkly Rep 64:608–612 - PubMed - PMC
    1. Ong E, Wong MU, He Y (2017) Identification of new features from known bacterial protective vaccine antigens enhances rational vaccine design. Front Immunol 8:1–11. https://doi.org/10.3389/fimmu.2017.01382 - DOI
    1. Vivona S, Bernante F, Filippini F (2006) NERVE: new enhanced reverse vaccinology environment. BMC Biotechnol 6:35. https://doi.org/10.1186/1472-6750-6-35 - DOI - PubMed - PMC

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