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. 2024 Feb 14;9(8):8923-8939.
doi: 10.1021/acsomega.3c06968. eCollection 2024 Feb 27.

Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data

Affiliations

Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data

Lenin González-Paz et al. ACS Omega. .

Abstract

Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike protein-mediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
To illustrate, we present the most stable conformation of two compounds: (a) nafamostat (PubChem CID_4413) and (b) pyronaridine (control) (PubChem CID_107771) within the binding pocket of the interface connecting the ACE2 receptor and the receptor-binding domain (RBD). The position and orientation of the nafamostat and pyronaridine structures are indicated within a circle, while the closest residues are displayed in the lower right corner. These interactions were predicted by using the BIOVIA Discovery Studio Visualizer tool. In the context of this study, nafamostat exhibited the most favorable pose within the interface formed between the RBD of SARS-CoV-2 and ACE2.
Figure 2
Figure 2
MD simulation for each complex (total time 100 ns). (a) Root mean squared deviations (RMSD) of Cα during each 10 ns, (b) root means square fluctuation (RMSF), and (c) radius of gyration-guided motions (Rg). RBD, receptor-binding domain; HCQ, hydroxychloroquine.
Figure 3
Figure 3
MD simulation of compounds with the most favorable pose in the interface formed between the RBD of SARS-CoV-2 and cellular receptors like ACE2 under the conditions of this study (total time of 100 ns). (a) Root mean squared deviations (RMSDs) of Cα during each 10 ns, (b) root means square fluctuation (RMSF), (c) radius of gyration-guided motions (Rg), (d) number and type of interactions of nafamostat, and (e) pyronaridine. RBD, receptor-binding domain.
Figure 4
Figure 4
Mechanistic model is employed to estimate the rates of uninfected cells (U), infected cells (I), and death of infected cells (δ). For illustrative purposes, a representative graph of the expression systems mentioned in the text is provided for each cell line studied using the limited target cell model. Additional graphics can be found in Supporting Information Figures S1–S6. (A) Vero E6 cell line and (B) Vero E6 + nafamostat.
Figure 5
Figure 5
Mechanistic model is utilized to provide estimates of the rates of uninfected cells (U), infected cells (I), and death of infected cells (δ). To enhance our understanding, a representative graph depicting the expression systems mentioned in the text is included for each cell line studied using the limited target cell model. Supporting Information Figures S1–S6 contain additional graphics. (A) HEK293 cell line, (B) HEK293 + camostat, (C) CaLu3 cell line, and (D) CaLu3 + nafamostat.

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