Vaxign 2: Web-based Rational Vaccine Design based on Reverse Vaccinology and Machine Learning

Michael Cooke

Research Mentor(s): Yongqun He, Associate Professor
Research Mentor School/College/Department: Lab Animal Medicine, Microbiology and Immunology, & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 3 (1pm-1:50pm)
Breakout Room: Room 19
Presenter: 6

Event Link

Abstract

Reverse vaccinology (RV) is a technique that allows the prediction of vaccine candidates from a pathogen’s genome enabling the more efficient development of new vaccines and improvements on existing vaccines. Vaxign2 is the second generation of the first web-based vaccine design program leveraging reverse vaccinology with machine learning via Vaxign-ML. Validation benchmarking has shown Vaxign-ML’s superior prediction performance compared to other RV tools. Vaxign2 has also implemented predictive analyses workflows to empower users to quickly refine prediction results based on different vaccine design rationales. A use case is presented with the successful analysis of SARS-CoV-2, the coronavirus that causes COVID-19.

Authors: Michael Cooke, Edison Ong, Anthony Huffman, Yongqun He
Research Method: Data Collection and Analysis