Inhibitory activity of marine sponge metabolites on SARS-CoV-2 RNA dependent polymerase: virtual screening and molecular dynamics simulation

Discovery of Melittin as Triple-Action Agent: Broad-Spectrum Antibacterial, Anti-Biofilm, and Potential Anti-Quorum Sensing Activities

The development of antibiotic-resistant microorganisms is a major global health concern. Recently, there has been an increasing interest in antimicrobial peptides as a therapeutic option. This study aimed to evaluate the triple-action (broad-spectrum antibacterial, anti-biofilm, and anti-quorum sensing activities) of melittin, a membrane-active peptide present in bee venom. The minimum inhibitory concentration and minimum bactericidal concentration of the melittin were determined using the microdilution method and agar plate counting. Growth curve analysis revealed that melittin showed a concentration-dependent antibacterial activity. Scanning electron microscope analysis revealed that melittin treatment altered the morphology. Confocal laser scanning microscope revealed that melittin increased the membrane permeability and intracellular ROS generation in bacteria, all of which contribute to bacterial cell death. In addition, the crystal violet (CV) assay was used to test the anti-biofilm activity. The CV assay demonstrated that melittin inhibited biofilm formation and eradicated mature biofilms. Biofilm formation mediated by quorum sensing (QS) plays a major role in this regard, so molecular docking and molecular dynamics analysis confirmed that melittin interacts with LasR receptors through hydrogen bonds, and further evaluates the anti-QS activity of melittin through the production of virulence factors (pyocyanin, elastase, and rhamnolipid), exopolysaccharides secretion, and bacterial motility, that may be the key to inhibiting the biofilm formation mechanism. The present findings highlight the promising role of melittin as a broad-spectrum antibacterial, anti-biofilm agent, and potential QS inhibitor, providing a new perspective and theoretical basis for the development of alternative antibiotics.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.