An In-Silico Identification of Potential Flavonoids against Kidney Fibrosis Targeting TGFβR-1

Abstract

Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor β (TGF-β) due to excessive inflammation and tissue damage, induces kidney fibrosis. The inhibition of TGF-β signaling is markedly limited in experimental disease models. Targeting TGF-β signaling, therefore, offers a prospective strategy for the management of kidney fibrosis. Presently, the marketed drugs have numerous side effects, but plant-derived compounds are relatively safer and more cost-effective. In this study, TGFβR-1 was targeted to identify the lead compounds among flavonoids using various computational approaches, such as ADME/T (absorption, distribution, metabolism, and excretion/toxicity) analysis, molecular docking, and molecular dynamics simulation. ADME/T screening identified a total of 31 flavonoids with drug-like properties of 31 compounds, a total of 5 compounds showed a higher binding affinity to TGFβR-1, with Epicatechin, Fisetin, and Luteolin ranking at the top three (−13.58, −13.17, and −10.50 kcal/mol, respectively), which are comparable to the control drug linagliptin (−9.074 kcal/mol). The compounds also exhibited outstanding protein–ligand interactions. The molecular dynamic simulations revealed a stable interaction of these compounds with the binding site of TGFβR-1. These findings indicate that flavonoids, particularly Epicatechin, Fisetin, and Luteolin, may compete with the ligand-binding site of TGFβR-1, suggesting that these compounds can be further evaluated for the development of potential therapeutics against kidney fibrosis. Further, in-vitro and in-vivo studies are recommended to support the current findings.

Identification of antidiabetic inhibitors from Allophylus villosus and Mycetia sinensis by targeting α-glucosidase and PPAR-γ: In-vitro, in-vivo, and computational evidence

“…The RMSF is essential for monitoring local protein changes because it computes the average change detected across a large number of atoms and assesses the displacement of a specific atoms in comparison to the reference structure. This numerical calculation is similar to root mean square deviation and is significant for protein characterization because it may be used to evaluate the flexibility and fluctuation of the residues throughout the simulation ( Rahman et al, 2022 , Sarker et al, 2022 ). Consequently, the RMSF values of the experimental drug candidate Compound CID-102506517 (blue), CID-6327703(orange), and the Control Drug (CID-441314) (ash) in complex with human alpha-glucosidase (PDB ID: 3WY1) were calculated in order to analyze the alteration in protein structural flexibility caused by the attachment of the selected ligand compounds to a specific residual position, as illustrated in Fig.…”

Profiling of secondary metabolite and evaluation of anti-diabetic potency of Crotalaria quinquefolia (L): In-vitro, in-vivo, and in-silico approaches

“…Furthermore, it was claimed to drastically lower the blood glucose levels of mice that were diabetic and loaded with glucose. The necessity for in-silico screening of natural chemicals for drug creation has arisen as a result of the high cost, long screening times, and arduous screening techniques ( Abdullah et al, 2023 , Arefin et al, 2021 , Biswas et al, 2021 , Khan et al, 2021 , Rahman et al, 2022 ). Unfocused laboratory processes that lack structural knowledge of medications and target molecules result in waste of time and money.…”

Molecular Dynamics Simulation and Pharmacoinformatic Integrated Analysis of Bioactive Phytochemicals from Azadirachta indica (Neem) to Treat Diabetes Mellitus

“…ADME, linked to pharmacokinetic characteristics, signifcantly impacts a drug's activity. Te pharmacokinetic parameters are obligatory to create a medication candidate that will successfully complete the required clinical trials and must be adjusted in time for the drug design process [75][76][77]. [78][79][80].…”