Deciphering the protein motion of S1 subunit in SARS-CoV-2 spike glycoprotein through integrated computational methods

Abstract

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major worldwide public health emergency that has infected over 8 million people. Spike glycoprotein, especially the partially open state of S1 subunit, in SARS-CoV-2 is considered vital for its infection with human host cell. However, the mechanism elucidating the transition from the closed state to the partially open state still remains unclear. In this study, we applied a series of computational methods, including Markov state model, transition path theory and random forest to analyze the S1 motion. Our results showed a promising complete conformational movement of the receptor-binding domain, from buried, partially open, to detached states. We also estimated the transition probability among these states. Based on the asymmetry in both the dynamics behavior and the accumulated alpha carbon (Cα) importance, we further suggested a relation among chains in the trimer spike protein, which leads to a deeper understanding on protein motions of the S1 subunit.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• spike glycoprotein
• Markov state model
• random forest
• transition path theory

Acknowledgements

Computational time was generously provided by Southern Methodist University’s Center for Research Computing. The authors thank D. E. Shaw for sharing the SARS-CoV-2 spike glycoprotein trajectories. The authors thank Ms. Xi Jiang from the Biostatistics Ph.D. program in the Statistics department of SMU for her help in manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available at https://www.deshawresearch.com/downloads/download_trajectory_sarscov2.cgi/.

Additional information

Funding

Research reported in this paper was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R15GM122013.