Aldose reductase (AR, ALR2) is a member of the aldo-keto reductase superfamily. It is the first and rate-limiting enzyme of the polyol pathway. AR plays an essential role in the development of diabetic complications. It is designated as the most legitimate target for managing diabetic complications. One of the key challenges to the successful development of target-specific AR inhibitors is target selectivity. In this research, target-specific drug-like ALR2 inhibito
The DPP-4 enzyme degrades incretin hormones GLP-1 and GIP. DPP-4 inhibitors are found effective in the prevention of the degradation of incretins. Xanthine scaffold-bearing molecules are reported as potential DPP-4 inhibitors for treating type 2 diabetes mellitus, e.g. the marketed drug linagliptin. In this work, structure-guided alignment-dependent atom- and Gaussian field-based 3D-QSAR have been performed on a dataset of 75 molecules. The robustness and predictive ability of the developed multifacet 3D-QSAR models were validated on different statistical parameters and found to be statistically fit. The favorable and unfavorable pharmacophoric features were mapped for each multifacet 3D-QSAR model based on three alignment sets (1–3). A five-point common pharmacophore hypothesis was generated separately for each set of alignments. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (Compounds 12, 40 and 57) compared to reference standard linagliptin to study the binding energy and stability of target-ligand complexes. The MM-PBSA calculations revealed that the binding free energy and stability of compounds 12 (−40.324 ± 17.876 kJ/mol), 40 (−80.543 ± 21.782 kJ/mol) and 57 (−50.202 ± 16.055 kJ/mol) were better than the reference drug linagliptin (−20.390 ± 63.200 kJ/mol). The generated contour maps from structure-guided alignment-dependent multifacet 3D-QSAR models offer information about the structure–activity relationship (SAR) and ligand-target binding energy and stability data from MD simulation may be utilized to design and develop target selective xanthine-based novel DPP-4 inhibitors.
Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes’ binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. −87.437 ± 19.728 and −73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure–activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity.
Background: Protein tyrosine phosphatase 1B (PTP 1B) is a recognized legitimate target for type 2 diabetes and obesity, collectively designated as ‘diabesity’, even though first-in-class inhibitor is still awaited. The main cause behind the unachieved target selectivity of investigated inhibitors is the high degree of sharing of structural homology between PTP 1B and other members of the PTP family. Objective: The present work aimed to discover target-specific inhibitors of PTP 1B with bidentate binding features on both the allosteric and active sites. Materials and Methods: We have implicated the amalgamated de novo designing, ADMET screening, and molecular docking simulations to discover novel drug-like allosteric inhibitors of PTP 1B. The LEA3D de novo designing platform was used to design novel thiazolidinediones (TZDs) from scratch in the core of the target on the strict constraints of defined molecular properties of drug-likeness. Molecular modelling and geometry optimization were done using the ChemOffice package. The druglikeness/ ADMET screening was performed using the TSAR package based on Lipinski’s filter. Molegro Virtual Docker (MVD) was used for the prediction of binding cavities in the target, estimation of ligandtarget binding affinities as well as mode of binding interactions. Results and Discussion: Novel TZDs (Molecules 1-8) were de novo designed successfully as drug-like target-specific inhibitors of PTP 1B. The interaction pattern and the energy contribution of ligand (Etotal, Eintra, Epair) and target (Epair) supported that the generated TZDs showed bidentate inhibition. Conclusion: The discovered TZDs can be developed as novel target-specific allosteric inhibitors of PTP 1B after the accomplishment of synthetic and pre-clinical interventions.
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