Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.

Journal: Nucleic acids research
PMID: 34009334

Abstract

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

Authors

  • Edison Ong
    University of Michigan Medical School, Ann Arbor, MI 48109, USA.
  • Michael F Cooke
    School of Information, University of Michigan, Ann Arbor, MI 48109, USA.
  • Anthony Huffman
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
  • Zuoshuang Xiang
    University of Michigan Medical School, Ann Arbor, MI 48109 USA.
  • Mei U Wong
    University of Michigan, Ann Arbor, MI, USA.
  • Haihe Wang
    University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
  • Meenakshi Seetharaman
    Undergraduate Research Opportunity Program, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA.
  • Ninotchka Valdez
    Undergraduate Research Opportunity Program, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA.
  • Yongqun He
    University of Michigan Medical School, Ann Arbor, MI 48109 USA ; Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, 1301 MSRB III, 1150 W. Medical Dr., Ann Arbor, MI 48109 USA.

Keywords

External Resources

Popular Topics
Recent Journals