VIR251 for COVID-19

The COVID-19 pandemic, instigated by the emergence of the novel coronavirus, SARS-CoV-2, created an incomparable global health crisis. Due to its highly virulent nature, identifying potential therapeutic agents against this lethal virus is crucial. PLpro is a key protein involved in viral polyprotein processing and immune system evasion, making it a prime target for the development of antiviral drugs to combat COVID-19. To expedite the search for potential therapeutic candidates, this review delved into computational studies. Recent investigations have harnessed computational methods to identify promising inhibitors targeting PLpro, aiming to suppress the viral activity. Molecular docking techniques were employed by researchers to explore the binding sites for antiviral drugs within the catalytic region of PLpro. The review elucidates the functional and structural properties of SARS-CoV-2 PLpro, underscoring its significance in viral pathogenicity and replication. Through comprehensive all-atom molecular dynamics (MD) simulations, the stability of drug–PLpro complexes was assessed, providing dynamic insights into their interactions. By evaluating binding energy estimates from MD simulations, stable drug–PLpro complexes with potential antiviral properties were identified. This review offers a comprehensive overview of the potential drug/lead candidates discovered thus far against PLpro using diverse in silico methodologies, encompassing drug repurposing, structure-based, and ligand-based virtual screenings. Additionally, the identified drugs are listed based on their chemical structures and meticulously examined according to various structural parameters, such as the estimated binding free energy (ΔG), types of intermolecular interactions, and structural stability of PLpro–ligand complexes, as determined from the outcomes of the MD simulations. Underscoring the pivotal role of targeting SARS-CoV-2 PLpro in the battle against COVID-19, this review establishes a robust foundation for identifying promising antiviral drug candidates by integrating molecular dynamics simulations, structural modeling, and computational insights. The continual imperative for the improvement of existing drugs and exploring novel compounds remains paramount in the global efforts to combat COVID-19. The evolution and management of COVID-19 hinge on the symbiotic relationship between computational insights and experimental validation, underscoring the interdisciplinary synergy crucial to this endeavor.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped respiratory β coronavirus that causes coronavirus disease (COVID-19), leading to a deadly pandemic that has claimed millions of lives worldwide. Like other coronaviruses, the SARS-CoV-2 genome also codes for non-structural proteins (NSPs). These NSPs are found within open reading frame 1a (ORF1a) and open reading frame 1ab (ORF1ab) of the SARS-CoV-2 genome and encode NSP1 to NSP11 and NSP12 to NSP16, respectively. This study aimed to collect the available literature regarding NSP inhibitors. In addition, we searched the natural product database looking for similar structures. The results showed that similar structures could be tested as potential inhibitors of the NSPs.