Computational Biology Analysis of COVID-19 Receptor-Binding Domains: A Target Site for Indocyanine Green Through Antimicrobial Photodynamic Therapy.

Abstract

Introduction: The receptor-binding domain (RBD) in SARS-CoV-2 binds strongly to angiotensin-converting enzyme 2 (ACE2) receptors and causes coronavirus disease 2019 (COVID-19). Antimicrobial photodynamic therapy (aPDT) is a well-established treatment option for the treatment of several viral infections. This in silico study was conducted to target the RBD of SARS-CoV-2 as a target site for aPDT. Methods: SARS-CoV-2-RBD was selected as a novel target for indocyanine green (ICG) as a photosensitizer during aPDT to exploit its molecular modeling, hierarchical nature of protein structure, and physico-chemical properties using several bioinformatic tools. The binding mode of the RBD to ICG was assessed via protein-ligand docking. Results: The results of a computational biology analysis revealed that SARS-CoV-2-RBD has 223 amino acids with a molecular weight of 25098.40 Da. RBD is most similar to 6W41 with an E-value of 4e-167, identity of 100%, and query cover of 100%. The aliphatic index of the RBD protein sequences was 71.61, suggesting that the protein is stable in a broad spectrum of temperatures. The predicted structure of RBD showed that it is a protein with a positive charge and a random coil structure (69.51%). Four ligands were modeled in this entry, including one N-acetyl-D-glucosamine (NAG), one glycerol (GOL), and two sulfate ions (SO4 ), to which ICG desires to bind in the molecular docking analysis. Conclusion: Molecular modeling and simulation analysis showed that SARS-CoV-2-RBD could be a substrate for binding to ICG during aPDT to control the spread of COVID-19.