Computational design of vaccines has evolved rapidly over the course of the last decade, driven largely by the pursuit of an HIV vaccine. Researchers employ several strategies when developing vaccines. The shared objective motivating these approaches is to increase the abundance of neutralizing antibodies while decreasing the abundance of non-neutralizing ones.
The recent coronavirus SARS-CoV-2 vaccine development has challenged the field to rapidly develop strategies of presenting several critical epitopes on the SARS-CoV-2 spike protein. Several vaccine approaches centered around stabilizing the receptor binding domain in a prefusion state (prior to interacting with the receptor and undergoing conformational change). As of October 2021 the “New York Times Coronavirus Vaccine Tacker” mentions a total of 52 therapeutic candidates that have entered phase 1 clinical trials, each demonstrating a slightly different design approach or delivery platform. Some platforms decided to administer mRNA using lipid nanoparticles, while others chose to administer recombinantly expressed subunit vaccines, traditional inactivated viruses , or replication defective adenoviruses ,.
Vaccine design is a rapidly advancing field which relies more and more heavily upon computation each year. It has been shown to leverage an increasing number of datasources, ranging from next generation sequencing of antibody repertoires or viral populations, to computational modeling of potential escape mutants and binding data from physical assays from micrographs displaying complex assemblies.
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