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. 2022 May 31:2022:7089576.
doi: 10.1155/2022/7089576. eCollection 2022.

Molecular Modeling and Simulation Analysis of Antimicrobial Photodynamic Therapy Potential for Control of COVID-19

Maryam Pourhajibagher  1
Affiliations

Molecular Modeling and Simulation Analysis of Antimicrobial Photodynamic Therapy Potential for Control of COVID-19

Maryam Pourhajibagher. ScientificWorldJournal. .

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can enter the host cells by binding the viral surface spike glycoprotein (SG) to angiotensin-converting enzyme 2. Since antiviral photodynamic therapy (aPDT) has been described as a new method for inhibiting viral infections, it is important to evaluate whether it can be used as a photoactivated disinfectant to control COVID-19. In this in silico study, SARS-CoV-2-SG was selected as a novel target for curcumin as a photosensitizer during aPDT to exploit its physicochemical properties, molecular modeling, hierarchical nature of protein structure, and functional analysis using several bioinformatics tools and biological databases. The results of a detailed computational investigation revealed that SARS-CoV-2-SG is most similar to 6VXX_A, with 100% query cover and identity. The predicted structure of SARS-CoV-2-SG displayed that it is a protein with a positive charge and random coil dominates other secondary structures located outside the viral cell. The protein-protein interaction network showed that SARS-CoV-2-SG interacted with ten potential interacting partners. In addition, primary screening of binding modes through molecular docking showed that curcumin desires to bind and interact with residues of SARS-CoV-2-SG as the main site to enhance the yield of aPDT. Overall, the computer simulation reveals that SARS-CoV-2-SG can be a suitable target site for interaction with curcumin during aPDT.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
Secondary structure prediction of SARS-CoV-2-SG using (a) PSIPRED and (b) GOR IV.
Figure 2
Figure 2
Bioinformatics analysis and computer simulation molecular modeling of SARS-CoV-2-SG. (a) Three-dimensional structures, (b) QMEAN value, (c) local model quality overall model quality, and (d) comparison with a nonredundant set of SARS-CoV-2-SG structure.
Figure 3
Figure 3
Ramachandran plot of SARS-CoV-2-SG by PROCHECK server.
Figure 4
Figure 4
Evaluation of protein model of SARS-CoV-2-SG by ERRAT.
Figure 5
Figure 5
Evaluation of protein model of SARS-CoV-2-SG by SAVES.
Figure 6
Figure 6
Evaluation of protein model of SARS-CoV-2-SG by VERIFY3D.
Figure 7
Figure 7
ProSA output for SARS-CoV-2-SG. (a) Overall model quality with ProsaWeb server and (b) local model quality with ProsaWeb server.
Figure 8
Figure 8
Protein-protein interaction map of SARS-CoV-2-SG by STRING.
Figure 9
Figure 9
Identification of signal peptide in SARS-CoV-2-SG by PROTTER.
Figure 10
Figure 10
Result of motif finder in SARS-CoV-2-SG.
Figure 11
Figure 11
Docking analysis between ligands of the modeled SARS-CoV-2-SG doped with curcumin.
See this image and copyright information in PMC

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