Vital role of water in longevity of SARS-CoV-2 and enhancing its binding with human cells

Review of factors affecting virus inactivation in aerosols and droplets

The inactivation of viruses in aerosol particles (aerosols) and droplets depends on many factors, but the precise mechanisms of inactivation are not known. The system involves complex physical and biochemical interactions. We reviewed the literature to establish current knowledge about these mechanisms and identify knowledge gaps. We identified 168 relevant papers and grouped results by the following factors: virus type and structure, aerosol or droplet size, temperature, relative humidity (RH) and evaporation, chemical composition of the aerosol or droplet, pH and atmospheric composition. These factors influence the dynamic microenvironment surrounding a virion and thus may affect its inactivation. Results indicate that viruses experience biphasic decay as the carrier aerosols or droplets undergo evaporation and equilibrate with the surrounding air, and their final physical state (liquid, semi-solid or solid) depends on RH. Virus stability, RH and temperature are interrelated, but the effects of RH are multifaceted and still not completely understood. Studies on the impact of pH and atmospheric composition on virus stability have raised new questions that require further exploration. The frequent practice of studying virus inactivation in large droplets and culture media may limit our understanding of inactivation mechanisms that are relevant for transmission, so we encourage the use of particles of physiologically relevant size and composition in future research.

Interfacial Water in the SARS Spike Protein: Investigating the Interaction with Human ACE2 Receptor and In Vitro Uptake in A549 Cells

The severity of global pandemic due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has engaged the researchers and clinicians to find the key features triggering the viral infection to lung cells. By utilizing such crucial information, researchers and scientists try to combat the spread of the virus. Here, in this work, we performed in silico analysis of the protein-protein interactions between the receptor-binding domain (RBD) of the viral spike protein and the human angiotensin-converting enzyme 2 (hACE2) receptor to highlight the key alteration that happened from SARS-CoV to SARS-CoV-2. We analyzed and compared the molecular differences between spike proteins of the two viruses using various computational approaches such as binding affinity calculations, computational alanine, and molecular dynamics simulations. The binding affinity calculations showed that SARS-CoV-2 binds a little more firmly to the hACE2 receptor than SARS-CoV. The major finding obtained from molecular dynamics simulations was that the RBD-ACE2 interface is populated with water molecules and interacts strongly with both RBD and ACE2 interfacial residues during the simulation periods. The water-mediated hydrogen bond by the bridge water molecules is crucial for stabilizing the RBD and ACE2 domains. Near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) confirmed the presence of vapor and molecular water phases in the protein-protein interfacial domain, further validating the computationally predicted interfacial water molecules. In addition, we examined the role of interfacial water molecules in virus uptake by lung cell A549 by binding and maintaining the RBD/hACE2 complex at varying temperatures using nanourchins coated with spike proteins as pseudoviruses and fluorescence-activated cell sorting (FACS) as a quantitative approach. The structural and dynamical features presented here may serve as a guide for developing new drug molecules, vaccines, or antibodies to combat the COVID-19 pandemic.

High predictive QSAR models for predicting the SARS coronavirus main protease inhibition activity of ketone-based covalent inhibitors

In this research, a dataset including 29 ketone-based covalent inhibitors with SARS-CoV-1 3CL^pro inhibition activity was used to develop high predictive QSAR models. Twenty-two molecules were put in train set and seven molecules in test set. By using stepwise MLR method for molecules in train set, four molecular descriptors including Mor26p, Hy, GATS7p and Mor04v were selected to build QSAR models. MLR and ANN methods were used to create QSAR models for predicting the activity of molecules in both train and test sets. Both QSAR models were validated by calculating several statistical parameters. R² values for the test set of MLR and ANN models were 0.93 and 0.95, respectively, and RMSE values for their test sets were 0.24 and 0.17, respectively. Other calculated statistical parameters (especially \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Q_{F3}^{2}$$\end{document}QF32 parameter) show that created ANN model has more predictive power with respect to developed MLR model (with four descriptor). Calculated leverages for all molecules show that predicted pIC₅₀ (by both QSAR models) for all molecules is acceptable, and drawn residuals plots show that there is no systematic error in building both QSAR modes. Also, based on developed MLR model, used molecular descriptors were interpreted.