RG203KR Mutations in SARS-CoV-2 Nucleocapsid: Assessing the Impact Using a Virus-Like Particle Model System.

The emergence and evolution of SARS-CoV-2 is characterized by the occurrence of diverse sets of mutations that affect virus characteristics, including transmissibility and antigenicity. Recent studies have focused mostly on spike protein mutations; however, SARS-CoV-2 variants of interest (VoI) or concern (VoC) contain significant mutations in the nucleocapsid protein as well. To study the relevance of mutations at the virion level, recombinant baculovirus expression system-based virus-like particles (VLPs) were generated for the prototype Wuhan sequence along with spike protein mutants like D614G and G1124V and the significant RG203KR mutation in nucleocapsid. All four structural proteins were assembled in a particle for which the morphology and size, confirmed by transmission electron microscopy, closely resembled that of the native virion. The VLP harboring RG203KR mutations in nucleocapsid exhibited augmentation of humoral immune responses and enhanced neutralization by immunized mouse sera. Results demonstrate a noninfectious platform to quickly assess the implication of mutations in structural proteins of the emerging variant. IMPORTANCE Since its origin in late 2019, the SARS-CoV-2 virus has been constantly mutating and evolving. Current studies mostly employ spike protein (S) pseudovirus systems to determine the effects of mutations on the infectivity and immunogenicity of variants. Despite its functional importance and emergence as a mutational hot spot, the nucleocapsid (N) protein has not been widely studied. The generation of SARS-CoV-2 VLPs in a baculoviral system in this study, with mutations in the S and N proteins, allowed examination of the involvement of all the structural proteins involved in viral entry and eliciting an immune response. This approach provides a platform to study the effect of mutations in structural proteins of SARS-CoV-2 that potentially contribute to cell infectivity, immune response, and immune evasion, bypassing the use of infectious virus for the same analyses.

Computational prediction of the molecular mechanism of statin group of drugs against SARS-CoV-2 pathogenesis.

Recently published clinical data from COVID-19 patients indicated that statin therapy is associated with a better clinical outcome and a significant reduction in the risk of mortality. In this study by computational analysis, we have aimed to predict the possible mechanism of the statin group of drugs by which they can inhibit SARS-CoV-2 pathogenesis. Blind docking of the critical structural and functional proteins of SARS-CoV-2 like RNA-dependent RNA polymerase, M-protease of 3-CL-Pro, Helicase, and the Spike proteins ( wild type and mutants from different VOCs) were performed using the Schrodinger docking tool. We observed that fluvastatin and pitavastatin showed fair, binding affinities to RNA polymerase and 3-CL-Pro, whereas fluvastatin showed the strongest binding affinity to the helicase. Fluvastatin also showed the highest affinity for the SpikeDelta and a fair docking score for other spike variants. Additionally, molecular dynamics simulation confirmed the formation of a stable drug-protein complex between Fluvastatin and target proteins. Thus our study shows that of all the statins, fluvastatin can bind to multiple target proteins of SARS-CoV-2, including the spike-mutant proteins. This property might contribute to the potent antiviral efficacy of this drug.