Structural insights into NS5B protein of novel equine hepaciviruses and pegiviruses complexed with polymerase inhibitors

Synthesis of Fluorinated Nucleosides/Nucleotides and Their Antiviral Properties

The FDA has approved several drugs based on the fluorinated nucleoside pharmacophore, and numerous drugs are currently in clinical trials. Fluorine-containing nucleos(t)ides offer significant antiviral and anticancer activity. The insertion of a fluorine atom, either in the base or sugar of nucleos(t)ides, alters its electronic and steric parameters and transforms the lipophilicity, pharmacodynamic, and pharmacokinetic properties of these moieties. The fluorine atom restricts the oxidative metabolism of drugs and provides enzymatic metabolic stability towards the glycosidic bond of the nucleos(t)ide. The incorporation of fluorine also demonstrates additional hydrogen bonding interactions in receptors with enhanced biological profiles. The present article discusses the synthetic methodology and antiviral activities of FDA-approved drugs and ongoing fluoro-containing nucleos(t)ide drug candidates in clinical trials.

Structural and energetic analysis of NS5 protein inhibition by small molecules in Japanese encephalitis virus using machine learning and steered molecular dynamics approach

One of the viral diseases that affect millions of people around the world, particularly in developing countries, is Japanese encephalitis (JE). In this study, the conserved protein of this virus, that is, non-structural protein 5 (NS5), was used as a target protein for this study, and a compound library of 749 antiviral molecules was screened against NS5. The current study employed machine learning-based virtual screening combined with molecular docking. Here, three hits (24360, 123519051 and 213039) had lower binding energies (< -8 kcal/mol) than the control, S-Adenosyl-L-homocysteine (SAH). All the compounds showed significant H-bond interactions with functional residues, which were also observed by the control. Molecular dynamics simulation, MM/GBSA for binding free energy analysis, principal component analysis and free energy landscape were also performed to study the stability of the complex formation. All three compounds had similar root mean square deviation trends, which were comparable to the control, SAH. Post-MD, the 123519051-receptor complex had the highest number of H-bonds (4 to 5) after the control, out of which three exhibited the highest percentage occupancy (50%, 24% and 79%). Both docking and MD, 123519051 showed an H-bond with the residue Gly111, which was also found for the control-protein complex. 123519051 showed the lowest binding free energy with ΔGbind of -89 kJ/mol. Steered molecular dynamics depicted that 123519051 had the maximum magnitude of dissociation (1436.43 kJ/mol/nm), which was more than the control, validating its stable complex formation. This study concluded that 123519051 is a binder and could inhibit the protein NS5 of JE.Communicated by Ramaswamy H. Sarma.

Sofosbuvir and its tri-phosphate metabolite inhibit the RNA-dependent RNA polymerase activity of non-structural protein 5 from the Kyasanur forest disease virus

Kyasanur forest disease is a neglected zoonotic disease caused by a single-stranded RNA-based flavivirus, the incidence of which was first recorded in 1957 in the Southern part of India. Kyasanur forest disease virus is transmitted to monkeys and humans through the infected tick bite of Haemophysalis spinigera. Kyasanur forest disease is a febrile illness, which in severe cases, results in neurological complications leading to mortality. The current treatment regimens are symptomatic and supportive, and no targeted therapies are available for this disease. In this study, we evaluated the ability of FDA-approved drugs sofosbuvir (and its active metabolite) and Dasabuvir to inhibit the RNA-dependent RNA polymerase activity of NS5 protein from the Kyasanur forest disease virus. NS5 protein containing the N-terminal methyl transferase domain and C-terminal RNA-dependent RNA polymerase domain was expressed in Escherichia coli, and RNA-dependent RNA polymerase activity was demonstrated with the purified protein. The RNA-dependent RNA polymerase assay conditions were optimized, followed by the determination of apparent K_m,ATP to validate the enzyme preparation. Half maximal-inhibitory concentrations against RNA-dependent RNA polymerase activity were determined for Sofosbuvir and its active metabolite. Dasabuvir did not show detectable inhibition with the tested conditions. This is the first demonstration of the inhibition of RNA-dependent RNA polymerase activity of NS5 protein from the Kyasanur forest disease virus with small molecule inhibitors. These initial findings can potentially facilitate the discovery and development of targeted therapies for treating Kyasanur forest disease.

Structural Homology-Based Drug Repurposing Approach for Targeting NSP12 SARS-CoV-2

The severe acute respiratory syndrome coronavirus 2, also known as SARS-CoV-2, is the causative agent of the COVID-19 global pandemic. SARS-CoV-2 has a highly conserved non-structural protein 12 (NSP-12) involved in RNA-dependent RNA polymerase (RdRp) activity. For the identification of potential inhibitors for NSP-12, computational approaches such as the identification of homologous proteins that have been previously targeted by FDA-approved antivirals can be employed. Herein, homologous proteins of NSP-12 were retrieved from Protein DataBank (PDB) and the evolutionary conserved sequence and structure similarity of the active site of the RdRp domain of NSP-12 was characterized. The identified homologous structures of NSP-12 belonged to four viral families: Coronaviridae, Flaviviridae, Picornaviridae, and Caliciviridae, and shared evolutionary conserved relationships. The multiple sequences and structural alignment of homologous structures showed highly conserved amino acid residues that were located at the active site of the RdRp domain of NSP-12. The conserved active site of the RdRp domain of NSP-12 was evaluated for binding affinity with the FDA-approved antivirals, i.e., Sofosbuvir and Dasabuvir in a molecular docking study. The molecular docking of Sofosbuvir and Dasabuvir with the active site that contains conserved motifs (motif A-G) of the RdRp domain of NSP-12 revealed significant binding affinity. Furthermore, MD simulation also inferred the potency of Sofosbuvir and Dasabuvir. In conclusion, targeting the active site of the RdRp domain of NSP-12 with Dasabuvir and Sofosbuvir might reduce viral replication and pathogenicity and could be further studied for the treatment of SARS-CoV-2.

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

COVID-19: CADD to the rescue

The recent outbreak of the deadly COVID-19 disease, being caused by the novel coronavirus (SARS-CoV-2), has put the world on red alert as it keeps spreading and recording more fatalities. Research efforts are being carried out to curtail the disease from spreading as it has been declared as of global health emergency. Hence, there is an exigent need to identify and design drugs that are capable of curing the infection and hinder its continual spread across the globe. Herein, a computer-aided drug design tool known as the virtual screening method was used to screen a database of 44 million compounds to find compounds that have the potential to inhibit the surface glycoprotein responsible for virus entry and binding. The consensus scoring approach selected three compounds with promising physicochemical properties and favorable molecular interactions with the target protein. These selected compounds can undergo lead optimization to be further developed as drugs that can be used in treating the COVID-19 disease.

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure-activity relationship (CSAR) model for identifying substructures giving rise to anti-HCV activities among a set of 578 non-redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in-depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.