Algorithm for the Quantitation of Variants of Concern for Rationally Designed Vaccines Based on the Isolation of SARS-CoV-2 Hawaiʻi Lineage B.1.243
Year: 2021
Type: preprint
Abstract: SARS-CoV-2 worldwide emergence and evolution has resulted in variants containing mutations resulting in immune evasive epitopes that decrease vaccine efficacy. We acquired clinical samples, analyzed SARS-CoV-2 genomes, used the most worldwide emerged spike mutations from Variants of Concern/Interest, and developed an algorithm for monitoring the SARS-CoV-2 vaccine platform. The algorithm partitions logarithmic-transformed prevalence data monthly and Pearson’s correlation determines exponential emergence. The SARS-CoV-2 genome evaluation indicated 49 mutations. Nine of the ten most worldwide prevalent (>70%) spike protein changes have r- values >0.9. The tenth, D614G, has a prevalence >99% and r -value of 0.67. The resulting algorithm is based on the patterns these ten substitutions elucidated. The strong positive correlation of the emerged spike protein changes and algorithmic predictive value can be harnessed in designing vaccines with relevant immunogenic epitopes. SARS-CoV-2 is predicted to remain endemic and continues to evolve, so must SARS-CoV-2 monitoring and next-generation vaccine design.
Institution: University of Hawaiʻi at Mānoa
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3194959602
