Computational methods can lead to better vaccines faster

 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 techniques, many RV and SV approaches have been developed and applied to the design of COVID-19 vaccines.

As an example of COVID-19 vaccine design, Dr. Edison Ong and his teammates in his He lab developed and developed his RV and machine learning methods to identify the SARS-CoV-2 spike (S) protein. applied and successfully predicted the best virus. The protein for COVID-19 vaccine development matches the S protein mRNA currently used in vaccines from Pfizer and Moderna.

In addition, the team also uncovered several other viral proteins that could be useful in developing COVID-19 vaccines, and proposed new cocktail vaccine recipes that could potentially increase the efficacy and safety of next-generation COVID-19 vaccines. .

For more information, see Anthony Huffman et al., Designing a COVID-19 vaccine using reverse and structural vaccinology, ontology-based literature search, and machine learning, Briefings in Bioinformatics (2022). DOI: 10.1093/bib/bbac190

Ong E, Wong M, Huffman A, He Y. Designing a COVID-19 coronavirus vaccine using reverse vaccinology and machine learning. The forefront of immunology. DOI: 10.3389/fimmu.2020.0158

Quote: Computational methods can lead to better vaccines faster (12 Aug 2022) on 17 Aug 2022.

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