Since the onset of the coronavirus pandemic in late 2019, more than 100 COVID-19 vaccines have entered or completed clinical trials, many of which have been authorized to be used around the world. However, fighting the ever-increasing threat posed by new SARS-CoV-2 variants calls for more efficient ways of developing safe and effective vaccines for COVID-19—and other emerging infectious diseases.
Anthony Huffman, Ph.D., of the Department of Computational Medicine and Bioinformatics, Yongqun He, Ph.D., and their colleagues at the University of Michigan Medical School recently published a review article in the journal Briefings in Bioinformatics that systematically surveys various methods in so-called rational COVID-19 vaccine design—which uses IT to determine potential vaccine targets—and proposes a strategy for effective and efficient COVID-19 vaccine design.
They classified three major stages in computational vaccine design:
- Identification of experimentally verified gold standard protective antigens—the proteins that trigger the immune system to mount a defense—through literature mining
- Rational vaccine design using reverse vaccinology, which uses the virus’ RNA or DNA to identify proteins that could be targets for vaccines and structural vaccinology, which uses the atomic structure of a virus to inform potential vaccines—using the gold standard data
- And further improvement of vaccine design through the surveillance and application of the approved vaccine successes and adverse event reports.
The team developed Protegen, a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. With the support of various machine learning methods, many RV and SV approaches have been developed and applied to COVID-19 vaccine design.