ABSTRACT
The high mortality rate from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in humans and the lack of effective therapeutic regime for its treatment necessitates the identification of new antivirals. SARS-CoV-2 relies on non-structural proteins such as Nsp13 helicase and nsp14 which are the key components of the replication-transcription complex (RTC) to complete its infectious life cycle. Therefore, targeting these essential viral proteins with small molecules will most likely to halt the disease pathogenesis. The lack of experimental structures of these proteins deters the process of structure-based identification of their specific inhibitors. In the present study, the in silico models of SARS-CoV-2 nsp13 helicase and nsp14 protein were elucidated using a comparative homology modelling approach. These in silico model structures were validated using various parameters such as Ramachandran plot, Verify 3D score, ERRAT score, knowledge-based energy and Z-score. The in silico models were further used for virtual screening of the Food and Drug Administration (FDA) approved antiviral drugs. Simeprevir (SMV), Paritaprevir (PTV) and Grazoprevir (GZR) were the common leads identified which show higher binding affinity to both nsp13 helicase and nsp14 as compared to the control inhibitors and therefore, they might be potential dual-target inhibitors. The leads also establish a network of hydrogen bonds and hydrophobic interactions with the key residues lining the active site pockets. The present findings suggest that these FDA approved antiviral drugs can be subjected to repurposing against SARS-CoV-2 infection after verifying the in silico results through in vitro and in vivo studies.
ABSTRACT
The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 (COVID-19), researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5'-ends of viral genomic RNA and sub genomic RNAs, to escape the host's innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5',5'-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5'-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.[Formula: see text]The focus of this study is to screen for antiviral inhibitors blocking guanine-N7 methyltransferase (N7-MTase), one of the key drug targets involved in the first methylation step of the SARS-CoV-2 RNA capping mechanism. Compounds binding the substrate-binding site can interfere with enzyme catalysis and impede 5'-end cap formation, which is crucial to mimic host RNA and evade host cellular immune responses. Therefore, our study proposes the top hit compounds from the Traditional Chinese Medicine (TCM) database using a combination of several computational approaches.Communicated by Ramaswamy H. Sarma.