Document Type
Article
Publication Date
6-9-2021
Abstract
We developed and applied a computational approach to simulate functional effects of the global circulating mutation D614G of the SARS-CoV-2 spike protein. All-atom molecular dynamics simulations are combined with deep mutational scanning and analysis of the residue interaction networks to investigate conformational landscapes and energetics of the SARS-CoV-2 spike proteins in different functional states of the D614G mutant. The results of conformational dynamics and analysis of collective motions demonstrated that the D614 site plays a key regulatory role in governing functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of protein residues, we identified the stability hotspots in the SARS-CoV-2 spike structures of the mutant trimers. The results suggest that the D614G mutation can induce the increased stability of the open form acting as a driver of conformational changes, which may result in the increased exposure to the host receptor and promote infectivity of the virus. The network community analysis of the SARS-CoV-2 spike proteins showed that the D614G mutation can enhance long-range couplings between domains and strengthen the interdomain interactions in the open form, supporting the reduced shedding mechanism. This study provides the landscape-based perspective and atomistic view of the allosteric interactions and stability hotspots in the SARS-CoV-2 spike proteins, offering a useful insight into the molecular mechanisms underpinning functional effects of the global circulating mutations.
Recommended Citation
Verkhivker, G. M.; Agajanian, S.; Oztas, D. Y.; Gupta, G. Landscape-based mutational sensitivity cartography and network community analysis of the SARS-CoV-2 spike protein structures: Quantifying functional effects of the circulating D614G variant. ACS Omega 2021, 6, 16216−16233. https://doi.org/10.1021/acsomega.1c02336
Supporting Information
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Epidemiology Commons, Numerical Analysis and Scientific Computing Commons, Other Computer Sciences Commons, Virus Diseases Commons
Comments
This article was originally published in ACS Omega, volume 6, in 2021. https://doi.org/10.1021/acsomega.1c02336
This scholarship is part of the Chapman University COVID-19 Archives.