Screening and identification of potential MERS-CoV papain-like protease (PLpro) inhibitors; Steady-state kinetic and Molecular dynamic studies.

MERS-CoV belongs to the coronavirus group. Recent years have seen a rash of coronavirus epidemics. In June 2012, MERS-CoV was discovered in the Kingdom of Saudi Arabia, with 2,591 MERSA cases confirmed by lab tests by the end of August 2022 and 894 deaths at a case-fatality ratio (CFR) of 34.5% documented worldwide. Saudi Arabia reported the majority of these cases, with 2,184 cases and 813 deaths (CFR: 37.2%), necessitating a thorough understanding of the molecular machinery of MERS-CoV. To develop antiviral medicines, illustrative investigation of the protein in coronavirus subunits are required to increase our understanding of the subject. In this study, recombinant expression and purification of MERS-CoV (PLpro), a primary goal for the development of 22 new inhibitors, were completed using a high throughput screening methodology that employed fragment-based libraries in conjunction with structure-based virtual screening. Compounds 2, 7, and 20, showed significant biological activity. Moreover, a docking analysis revealed that the three compounds had favorable binding mood and binding free energy. Molecular dynamic simulation demonstrated the stability of compound 2 (2-((Benzimidazol-2-yl) thio)-1-arylethan-1-ones) the strongest inhibitory activity against the PLpro enzyme. In addition, disubstitutions at the meta and para locations are the only substitutions that may boost the inhibitory action against PLpro. Compound 2 was chosen as a MERS-CoV PLpro inhibitor after passing absorption, distribution, metabolism, and excretion studies; however, further investigations are required.

Immunoinformatics-guided design of a multi-epitope vaccine based on the structural proteins of severe acute respiratory syndrome coronavirus 2† † Electronic supplementary information (ESI) available. See DOI: 10.1039/d1ra02885e

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in a contagious respiratory tract infection that has become a global burden since the end of 2019. Notably, fewer patients infected with SARS-CoV-2 progress from acute disease onset to death compared with the progression rate associated with two other coronaviruses, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). Several research organizations and pharmaceutical industries have attempted to develop successful vaccine candidates for the prevention of COVID-19. However, increasing evidence indicates that the SARS-CoV-2 genome undergoes frequent mutation;thus, an adequate analysis of the viral strain remains necessary to construct effective vaccines. The current study attempted to design a multi-epitope vaccine by utilizing an approach based on the SARS-CoV-2 structural proteins. We predicted the antigenic T- and B-lymphocyte responses to four structural proteins after screening all structural proteins according to specific characteristics. The predicted epitopes were combined using suitable adjuvants and linkers, and a secondary structure profile indicated that the vaccine shared similar properties with the native protein. Importantly, the molecular docking analysis and molecular dynamics simulations revealed that the constructed vaccine possessed a high affinity for toll-like receptor 4 (TLR4). In addition, multiple descriptors were obtained from the simulation trajectories, including the root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg), demonstrating the rigid nature and inflexibility of the vaccine and receptor molecules. In addition, codon optimization, based on Escherichia coli K12, was used to determine the GC content and the codon adaptation index (CAI) value, which further followed for the incorporation into the cloning vector pET28+(a). Collectively, these findings suggested that the constructed vaccine could be used to modulate the immune reaction against SARS-CoV-2. COVID-19 is caused by SARS-CoV-2, resulting in a contagious respiratory tract infection. For designing a multi-epitope vaccine, we utilized the four structural proteins from the SARS-CoV-2 by using bioinformatics and immunoinformatics analysis.

Selection of SARS-CoV-2 main protease inhibitor using structure-based virtual screening.

Background: Conserved domains within SARS-CoV-2 nonstructural proteins represent key targets for the design of novel inhibitors. Methods: The authors aimed to identify potential SARS-CoV-2 NSP5 inhibitors using the ZINC database along with structure-based virtual screening and molecular dynamics simulation. Results: Of 13,840 compounds, 353 with robust docking scores were initially chosen, of which ten hit compounds were selected as candidates for detailed analyses. Three compounds were selected as coronavirus NSP5 inhibitors after passing absorption, distribution, metabolism, excretion and toxicity study; root and mean square deviation; and radius of gyration calculations. Conclusion: ZINC000049899562, ZINC000169336666 and ZINC000095542577 are potential NSP5 protease inhibitors that warrant further experimental studies.

Virtual Screening and Molecular Docking Studies for Discovery of Potential RNA-Dependent RNA Polymerase Inhibitors

The current COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Globally, this pandemic has affected over 111 million individuals and posed many health and economic challenges. Much research effort is dedicated to discovering new treatments to address the associated challenges and restrict the spread of SARS-CoV-2. Since SARS-CoV-2 is a positive-strand RNA virus, its replication requires the viral RNA-dependent RNA polymerase (RdRp) enzyme. In this study, we report the discovery of new potential RdRp enzyme inhibitors based on computer modeling and simulation methodologies. The antiviral ZINC database was utilized for covalent docking virtual screening followed by molecular interaction analyses based on reported hot spots within the RdRp binding pocket (PDB: 7BV2). Eleven molecules, ZINC000014944915, ZINC000027556215, ZINC000013556344, ZINC000003589958, ZINC000003833965, ZINC000001642252, ZINC000028525778, ZINC000027557701, ZINC000013781295, ZINC000001651128 and ZINC000013473324, were shown to have the highest binding interactions. These molecules were further assessed by molecular dynamics (MD) simulations and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies. The results showed that all 11 molecules except ZINC000027557701 formed stable complexes with the viral RdRp and fell within the accepted ADMET parameters. The identified molecules can be used to design future potential RdRp inhibitors.