Evaluation of flavonoids from banana pseudostem and flower (quercetin and catechin) as potent inhibitors of α-glucosidase: An in silico perspective.

The amelioration of postprandial hyperglycemia in diabetic conditions could be accomplished by the inhibition of α-glucosidases, a set of intestinal carbohydrate digestive enzymes responsible for starch hydrolysis and its absorption. The ethnopharmacological profile of banana depicts the usage of different plant parts in conventional medicinal formulations. The antidiabetic studies of the plant have demonstrated their ability to inhibit α-glucosidase. Besides, our research group has reported the α-glucosidase inhibitory potential of the banana pseudostem and flower extracts in previous studies. In this study, we deliberate on the specific phytoconstituents of banana pseudostem and flower to evaluate their antidiabetic effects through an in silico perspective for the α-glucosidase inhibition. In this context, several phytoconstituents of banana pseudostem and flower identified through GC-MS analysis were retrieved from chemical databases. These phytochemicals were virtually screened through the molecular docking simulation process, from which only two flavonoids (catechin and quercetin) were selected based on their binding affinity and extent of interaction with the α-glucosidase target protein. The lower binding affinities of catechin and quercetin in comparison with that of acarbose as a control proved their binding efficiency with the target protein. In addition, acarbose showed subservient molecular interaction, forming an unfavourable acceptor-acceptor bond. The molecular dynamics simulations also depicted the effective binding and stability of the complexes formed with catechin and quercetin, in comparison with that of acarbose. Further, PASS analysis, druglikeliness, and pharmacokinetic assessments showed that both catechin and quercetin edge over acarbose in terms of drug-score and pharmacokinetic properties. With the positive results obtained from contemporary strategies, the two flavonoids from banana pseudostem and flower might be established as a considerable phototherapeutic approach to inhibit α-glucosidase. Communicated by Ramaswamy H. Sarma.

In silico identification of novel benzophenone-coumarin derivatives as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) inhibitors.

In this study, we propose our novel benzophenone-coumarin derivatives (BCDs) as potent inhibitors of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 virus, one of the key targets that are involved in the viral genome replication. We aim to evaluate the in silico antiviral potential of BCDs against this protein target, which involves molecular docking simulations, druglikeliness and pharmacokinetic evaluations, PASS analysis, molecular dynamics simulations, and computing binding free energy. Out of all the BCDs screened through these parameters, BCD-8 was found to be the most efficient and potent inhibitor of SARS-CoV-2 RdRp. During molecular docking simulation, BCD-8 showed an extensive molecular interaction in comparison with that of the standard control used, remdesivir. The druglikeliness and pharmacokinetic analyses also proved the efficiency of BCD-8 as an effective drug without adverse effects. Further, pharmacological potential analysis through PASS depicted the antiviral property of BCD-8. With these findings, we performed molecular dynamics simulations, where BCD-8 edged out remdesivir with its exemplary stable interaction with SARS-CoV-2 RdRp. Furthermore, binding free energy of both BCD-8 and remdesivir was calculated, where BCD-8 showed a lower binding energy and standard deviations in comparison with that of remdesivir. Moreover, being a non-nucleoside analogue, BCD-8 can be used effectively against SARS-CoV-2, whereas nucleoside analogues like remdesivir may become non-functional or less functional due to exonuclease activity of nsp14 of the virus. Therefore, we propose BCD-8 as a SARS-CoV-2 RdRp inhibitor, showing higher predicted efficiency than remdesivir in all the in silico experiments conducted.Communicated by Ramaswamy H. Sarma.