Vaccine Design by Reverse Vaccinology and Machine Learning
- PMID: 34784028
- DOI: 10.1007/978-1-0716-1900-1_1
Vaccine Design by Reverse Vaccinology and Machine Learning
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.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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