Ligand-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulations of New β-Estrogen Receptor Activators with Potential for Pharmacological Obesity Treatment

Molecular Docking-Based Virtual Screening of FDA-Approved Drugs Using Trypanothione Reductase Identified New Trypanocidal Agents

American trypanosomiasis or Chagas disease, caused by Trypanosoma cruzi (T. cruzi), affects approximately 6-7 million people worldwide. However, its pharmacological treatment causes several uncomfortable side effects, causing patients' treatment abandonment. Therefore, there is a need for new and better treatments. In this work, the molecular docking of nine hundred twenty-four FDA-approved drugs on three different sites of trypanothione reductase of T. cruzi (TcTR) was carried out to find potential trypanocidal agents. Finally, biological evaluations in vitro and in vivo were conducted with the selected FDA-approved drugs. Digoxin, alendronate, flucytosine, and dihydroergotamine showed better trypanocidal activity than the reference drugs benznidazole and nifurtimox in the in vitro evaluation against the trypomastigotes form. Further, these FDA-approved drugs were able to reduce 20-50% parasitemia in a short time in an in vivo model, although with less efficiency than benznidazole. Therefore, the results suggest a combined therapy of repurposed and canonical drugs against T. cruzi infection.

Flavonoids as potential KRAS inhibitors: DFT, molecular docking, molecular dynamics simulation and ADMET analyses

KRAS mutations linked with cancer. Flavonoids were docked against KRAS G12C and G12D receptors. Abyssinone III, alpha naphthoflavone, beta naphthoflavone, abyssinone I, abyssinone II and beta naphthoflavone, genistin, daidzin showed good docking scores against KRAS G12C and G12D receptors, respectively. The MD simulation data revealed that Rg, RMSD, RMSF, and SASA values were within acceptable limits. Alpha and beta naphthoflavone showed good binding energies with KRAS G12C and G12D receptors. DFT and MEP analysis highlighted the nucleophilic and electrophilic zones of best-docked flavonoids. A novel avenue for the control of KRAS G12C and G12D mutations is made possible by flavonoids.

S-Dihydrodaidzein and 3-(1,3-benzoxazol-2-yl)-benzamide, Two New Potential β-estrogen Receptor Ligands with Anti-adipogenic Activity

BACKGROUND

The elucidation of molecular pathways associated with adipogenesis has evidenced the relevance of estrogen and estrogen receptor beta (ERβ). The positive effects of ERβ ligands on adipogenesis, energy expenditure, lipolysis, food intake, and weight loss, make ERβ an attractive target for obesity control. From ligand-based virtual screening, molecular docking, and molecular dynamic simulations, six new likely ERβ ligands (C1 to C6) have been reported with potential for pharmacological obesity treatment.

OBJECTIVE

In this study, the effect of molecules C1-C6 on adipogenesis using the murine 3T3-L1 cell line was evaluated.

METHODS

Cell viability was assessed by MTT assays. Lipid accumulation and gene expression were investigated by ORO staining and real-time quantitative RT-PCR experiments, respectively.

RESULTS

Cell viability was not significantly affected by C1-C6 at concentrations up to 10 μM. Interestingly, treatment with 10 μM of C1 (S-Dihydrodaidzein) and C2 (3-(1,3-benzoxazol-2-yl)- benzamide) for 72 h inhibited adipocyte differentiation; moreover, ORO staining evidenced a reduced intracellular lipid accumulation (40% at day 7). Consistently, mRNA expression of the adipogenic markers, PPARγ and C/EBPα, was reduced by 50% and 82%, respectively, in the case of C1, and by 83% and 59%, in the case of C2.

CONCLUSION

Altogether, these results show the two new potential β-estrogen receptor ligands, C1 and C2, to exhibit anti-adipogenic activity. They could further be used as lead structures for the development of more efficient drugs for obesity control.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15-30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829-0.847 and 0.712-0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92-8.29 and 1.59-3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

Exploring potent aldose reductase inhibitors for anti-diabetic (anti-hyperglycemic) therapy: integrating structure-based drug design, and MMGBSA approaches

Aldose reductase (AR) is an important target in the development of therapeutics against hyper-glycemia-induced health complications such as retinopathy, etc. In this study, we employed a combination of structure-based drug design, molecular simulation, and free energy calculation approaches to identify potential hit molecules against anti-diabetic (anti-hyperglycemic)-induced health complications. The 3D structure of aldoreductase was screened for multiple compound libraries (1,00,000 compounds) and identified as ZINC35671852, ZINC78774792 from the ZINC database, Diamino-di nitro-methyl dioctyl phthalate, and Penta-o-galloyl-glucose from the South African natural compounds database, and Bisindolylmethane thiosemi-carbazides and Bisindolylme-thane-hydrazone from the Inhouse database for this study. The mode of binding interactions of the selected compounds later predicted their aldose reductase inhibitory potential. These com-pounds interact with the key active site residues through hydrogen bonds, salt bridges, and π-π interactions. The structural dynamics and binding free energy results further revealed that these compounds possess stable dynamics with excellent binding free energy scores. The structures of the lead inhibitors can serve as templates for developing novel inhibitors, and in vitro testing to confirm their anti-diabetic potential is warranted. The current study is the first to design small molecule inhibitors for the aldoreductase protein that can be used in the development of therapeutic agents to treat diabetes.

Establishing the Role of Iridoids as Potential Kirsten Rat Sarcoma Viral Oncogene Homolog G12C Inhibitors Using Molecular Docking; Molecular Docking Simulation; Molecular Mechanics Poisson-Boltzmann Surface Area; Frontier Molecular Orbital Theory; Molecular Electrostatic Potential; and Absorption, Distribution, Metabolism, Excretion, and Toxicity Analysis

The RAS gene family is one of the most frequently mutated oncogenes in human cancers. In KRAS, mutations of G12D and G12C are common. Here, 52 iridoids were selected and docked against 8AFB (KRAS G12C receptor) using Sotorasib as the standard. As per the docking interaction data, 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester (dock score: -9.9 kcal/mol), 6'-O-trans-para-coumaroyl geniposidic acid (dock score: -9.6 kcal/mol), 6-O-trans-cinnamoyl-secologanoside (dock score: -9.5 kcal/mol), Loganic acid 6'-O-beta-d-glucoside (dock score: -9.5 kcal/mol), 10-O-succinoylgeniposide (dock score: -9.4), Loganic acid (dock score: -9.4 kcal/mol), and Amphicoside (dock score: -9.2 kcal/mol) showed higher dock scores than standard Sotorasib (dock score: -9.1 kcal/mol). These common amino acid residues between iridoids and complexed ligands confirmed that all the iridoids perfectly docked within the receptor's active site. The 100 ns MD simulation data showed that RMSD, RMSF, radius of gyration, and SASA values were within range, with greater numbers of hydrogen bond donors and acceptors. MM/PBSA analysis showed maximum binding energy values of -7309 kJ/mol for 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester. FMO analysis showed that 6-O-trans-p-coumaroyl-8-O-acetylshanzhiside methyl ester was the most likely chemically reactive molecule. MEP analysis data highlighted the possible electrophilic and nucleophilic attack regions of the best-docked iridoids. Of all the best-docked iridoids, Loganic acid passed Lipinski, Pfizer, and GSK filters with a similar toxicity profile to Sotorasib. Thus, if we consider these iridoids to be KRAS G12C inhibitors, they will be a boon to mankind.