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 language | English |
|---|---|
| Article number | e1010563 |
| Journal | PLoS Computational Biology |
| Volume | 18 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2022 |
| Externally published | Yes |
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.
Funding
EW was supported by the National Science Foundation Graduate Research Fellowship Grant No. 1745302 (https://www.nsfgrfp.org/). EW and AKC were supported by NIH Grant No. 1-R61-AI161805-01 (https://grants.nih.gov/grants/oer. htm) and the Ragon Institute of MGH, MIT, and Harvard (https://ragoninstitute.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors acknowledge the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing HPC resources that have contributed to the research results reported within this work.
| Funders | Funder number |
|---|---|
| National Science Foundation | 1745302 |
| National Institutes of Health | 1-R61-AI161805-01 |
| Ragon Institute of MGH, MIT and Harvard |
