Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants

Eric Wang, Arup K. Chakraborty

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2’ cleavage site and fusion peptide as potential pan-coronavirus vaccine targets.

Original languageEnglish
Article numbere1010563
JournalPLoS Computational Biology
Volume18
Issue number9
DOIs
StatePublished - Sep 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Wang, Chakraborty. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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