Combined 3D-QSAR modeling and molecular docking study on spiro-derivatives as inhibitors of acetyl-CoA carboxylase

Comparison of various methods for validity evaluation of QSAR models

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature.

METHODS

In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed.

RESULTS

The findings revealed that employing the coefficient of determination (r²) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies.

CONCLUSION

This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

Quercetin Attenuates Podocyte Apoptosis of Diabetic Nephropathy Through Targeting EGFR Signaling

Podocytes injury is one of the leading causes of proteinuria in patients with diabetic nephropathy (DN), and is accompanied by podocytes apoptosis and the reduction of podocyte markers such as synaptopodin and nephrin. Therefore, attenuation of podocyte apoptosis is considered as an effective strategy to prevent the proteinuria in DN. In this study, we evaluated the anti-podocyte-apoptosis effect of quercetin which is a flavonol compound possessing an important role in prevention and treatment of DN and verified the effect by using db/db mice and high glucose (HG)-induced mouse podocytes (MPs). The results show that administration of quercetin attenuated the level of podocyte apoptosis by decreasing the expression of pro-apoptotic protein Bax, cleaved caspase 3 and increasing the expression of anti-apoptotic protein Bcl-2 in the db/db mice and HG-induced MPs. Furthermore, epidermal growth factor receptor (EGFR) was predicted to be the potential physiological target of quercetin by network pharmacology. In vitro and vivo experiments confirmed that quercetin inhibited activation of the EGFR signaling pathway by decreasing phosphorylation of EGFR and ERK1/2. Taken together, this study demonstrates that quercetin attenuated podocyte apoptosis through inhibiting EGFR signaling pathway, which provided a novel approach for further research of the mechanism of quercetin in the treatment of DN.

Molecular modelling of quinoline derivatives as telomerase inhibitors through 3D-QSAR, molecular dynamics simulation, and molecular docking techniques

Rising mortality due to cancer has led to the development and identification of newer targets and molecules to cure the disease. Telomerase is one of the attractive targets for design of many chemotherapeutic drugs. This research highlights the designing of novel telomerase inhibitors using ligand-based (3D-QSAR) and structure-based (molecular docking and molecular dynamics simulation) approaches. For the development of the 3D-QSAR model, 37 synthetic molecules reported earlier as telomerase inhibitors were selected from diversified literature. Three different alignment methods were explored; among them, distill alignment was found to be the best method with good statistical results and was used for the generation of QSAR model. Statistically significant CoMSIA model with a correlation coefficient (r²_ncv) value of 0.974, leave one out (q²) value of 0.662 and predicted correlation coefficient (r²_pred) value of 0.560 was used for the analysis of QSAR. For the MDS study, A-chain of telomerase was stabilised for 50 ns with respect to 1-atm pressure, with an average temperature of 299.98 k and with potential energy of 1,145,336 kJ/m converged in 997 steps. Furthermore, the behaviour study of variants towards the target revealed that active variable gave better affinity without affecting amino acid sequences and dimensions of protein which was accomplished through RMSD, RMSF and Rg analysis. Results of molecular docking study supported the outcomes of QSAR contour maps as ligand showed similar interactions with surrounded amino acids which were identified in contour map analysis. The results of the comprehensive study might be proved valuable for the development of potent telomerase inhibitors.

Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors

INTRODUCTION

Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases.

METHODS

In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again.

RESULTS

Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library.

CONCLUSION

In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.

Insight into the molecular mechanism of yeast acetyl-coenzyme A carboxylase mutants F510I, N485G, I69E, E477R, and K73R resistant to soraphen A

Acetyl-coenzyme A carboxylases (ACCs) is the first committed enzyme of fatty acid synthesis pathway. The inhibition of ACC is thought to be beneficial not only for diseases related to metabolism, such as type-2 diabetes, but also for infectious disease like bacterial infection disease. Soraphen A, a potent allosteric inhibitor of BC domain of yeast ACC, exhibit lower binding affinities to several yeast ACC mutants and the corresponding drug resistance mechanisms are still unknown. We report here a theoretical study of binding of soraphen A to wild type and yeast ACC mutants (including F510I, N485G, I69E, E477R, and K73R) via molecular dynamic simulation and molecular mechanics/generalized Born surface area free energy calculations methods. The calculated binding free energies of soraphen A to yeast ACC mutants are weaker than to wild type, which is highly consistent with the experimental results. The mutant F510I weakens the binding affinity of soraphen A to yeast ACC mainly by decreasing the van der Waals contributions, while the weaker binding affinities of Soraphen A to other yeast ACC mutants including N485G, I69E, E477R, and K73R are largely attributed to the decreased net electrostatic (ΔE_ele + ΔG_GB) interactions. Our simulation results could provide important insights for the development of more potent ACC inhibitors.