Molecular Modeling and Simulation Analysis of Antimicrobial Photodynamic Therapy Potential for Control of COVID-19.
Maryam PourhajibagherPublished in: TheScientificWorldJournal (2022)
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.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- photodynamic therapy
- molecular docking
- protein protein
- angiotensin converting enzyme
- small molecule
- coronavirus disease
- angiotensin ii
- binding protein
- staphylococcus aureus
- induced apoptosis
- single cell
- fluorescence imaging
- climate change
- molecular dynamics simulations
- oxidative stress
- cell therapy
- machine learning
- human immunodeficiency virus