In-silico study of peptide-protein interaction of antimicrobial peptides potentially targeting SARS and SARS-CoV-2 nucleocapsid protein

Molecular docking analysis of peptide-based antiviral agents against SARS-CoV-2 main protease: an approach towards drug repurposing

Aim: Utilizing the therapeutic potentials of previously approved medications against a new target or pharmacological response is known as drug repurposing. The health and scientific communities are under continual pressure to discover new compounds with antiviral potential due to the rising reports of viral resistance and the occurrence and re-emergence of viral outbreaks. The use of antiviral peptides has emerged as an intriguing option in this search. Here, this article includes the current United States Food and Drug Administration (FDA)-approved antiviral peptides that might be enforced for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and carried out docking study of the viral protease inhibitors. Methods: In silico techniques like molecular docking was carried out using Autodock Vina software. Results: The molecular docking studies of peptide-based antiviral agents against SARS-CoV-2 [Protein Data Bank (PDB) ID: 7P35] using docking software AutoDockTools 1.5.6. Among all the docked ligands, compound velpatasvir showed interaction with residues ILE213, GLN256, LEU141, GLN189, GLU166, HIS41, CYS145, and ASN142, and displayed the highest docking score of –8.2 kcal/mol. This medication could be a novel treatment lead or candidate for treating SARS-CoV-2. Conclusions: To conclude, a docking study of peptide based antiviral compounds for their binding mode in the catalytic domain of SARS-CoV-2 receptor is reported. On molecular docking, the compounds have showed remarkable binding affinity with the amino acids of receptor chain A. The compounds occupied the same binding cavity as the reference compound maintaining the interactions with conserved amino acid residues essential for significant inhibitory potential, especially for compound velpatasvir with binding score of –8.2 kcal/mol.

Molecular Docking and In-Silico Analysis of Natural Biomolecules against Dengue, Ebola, Zika, SARS-CoV-2 Variants of Concern and Monkeypox Virus

The emergence and rapid evolution of human pathogenic viruses, combined with the difficulties in developing effective vaccines, underline the need to develop innovative broad-spectrum antiviral therapeutic agents. The present study aims to determine the in silico antiviral potential of six bacterial antimicrobial peptides (AMPs), two phytochemicals (silvestrol, andrographolide), and two bacterial secondary metabolites (lyngbyabellin A, hapalindole H) against dengue virus, Zika virus, Ebola virus, the major variants of SARS-CoV-2 and monkeypox virus. The comparison of docking scores obtained with natural biomolecules was performed with specific neutralizing antibodies (positive controls for ClusPro) and antiviral drugs (negative controls for Autodock Vina). Glycocin F was the only natural biomolecule tested to show high binding energies to all viral surface proteins and the corresponding viral cell receptors. Lactococcin G and plantaricin ASM1 also achieved high docking scores with all viral surface proteins and most corresponding cell surface receptors. Silvestrol, andrographolide, hapalindole H, and lyngbyabellin A showed variable docking scores depending on the viral surface proteins and cell receptors tested. Three glycocin F mutants with amino acid modifications showed an increase in their docking energy to the spike proteins of SARS-CoV-2 B.1.617.2 Indian variant, and of the SARS-CoV-2 P.1 Japan/Brazil variant, and the dengue DENV envelope protein. All mutant AMPs indicated a frequent occurrence of valine and proline amino acid rotamers. AMPs and glycocin F in particular are the most promising biomolecules for the development of broad-spectrum antiviral treatments targeting the attachment and entry of viruses into their target cell.

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.

Antimicrobial Mechanisms and Clinical Application Prospects of Antimicrobial Peptides

Antimicrobial peptides are a type of small-molecule peptide that widely exist in nature and are components of the innate immunity of almost all living things. They play an important role in resisting foreign invading microorganisms. Antimicrobial peptides have a wide range of antibacterial activities against bacteria, fungi, viruses and other microorganisms. They are active against traditional antibiotic-resistant strains and do not easily induce the development of drug resistance. Therefore, they have become a hot spot of medical research and are expected to become a new substitute for fighting microbial infection and represent a new method for treating drug-resistant bacteria. This review briefly introduces the source and structural characteristics of antimicrobial peptides and describes those that have been used against common clinical microorganisms (bacteria, fungi, viruses, and especially coronaviruses), focusing on their antimicrobial mechanism of action and clinical application prospects.