Active site prediction of phosphorylated SARS-CoV-2 N-Protein using molecular simulation

Sreenidhi Sankararaman; John Hamre 3rd; Fahad Almsned; Abdulrhman Aljouie; Yahya Bokhari; Mohammed Alawwad; Lamya Alomair; M Saleet Jafri

doi:10.1016/j.imu.2022.100889

Active site prediction of phosphorylated SARS-CoV-2 N-Protein using molecular simulation

Inform Med Unlocked. 2022:29:100889. doi: 10.1016/j.imu.2022.100889. Epub 2022 Feb 21.

Authors

Sreenidhi Sankararaman^{1

2}, John Hamre 3rd¹, Fahad Almsned^{1

3}, Abdulrhman Aljouie⁴, Yahya Bokhari⁴, Mohammed Alawwad⁴, Lamya Alomair^{1

4}, M Saleet Jafri^{1

5}

Affiliations

¹ School of Systems Biology, George Mason University, Fairfax, VA, 22030, USA.
² Thomas Jefferson High School for Science and Technology, Arlington, VA, 22312, USA.
³ King Fahad Specialist Hospital - Dammam, Dammam, Saudi Arabia.
⁴ King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
⁵ Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, 20201, USA.

Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleocapsid protein (N-protein) is responsible for viral replication by assisting in viral RNA synthesis and attaching the viral genome to the replicase-transcriptase complex (RTC). Numerous studies suggested the N-protein as a drug target. However, the specific N-protein active sites for SARS-CoV-2 drug treatments are yet to be discovered. The purpose of this study was to determine active sites of the SARS-CoV-2 N-protein by identifying torsion angle classifiers for N-protein structural changes that correlated with the respective angle differences between the active and inactive N-protein. In the study, classifiers with a minimum accuracy of 80% determined from molecular simulation data were analyzed by Principal Component Analysis and cross-validated by Logistic Regression, Support Vector Machine, and Random Forest Classification. The ability of torsion angles ψ252 and φ375 to differentiate between phosphorylated and unphosphorylated structures suggested that residues 252 and 375 in the RNA binding domain might be important in N-protein activation. Furthermore, the φ and ψ angles of residue S189 correlated to a 90.7% structural determination accuracy. The key residues involved in the structural changes identified here might suggest possible important functional sites on the N-protein that could be the focus of further study to understand their potential as drug targets.

Keywords: Covid-19; Machine learning; Molecular simulation; N-Protein.