7-Aminoalkoxy-Quinazolines from Epigenetic Focused Libraries Are Potent and Selective Inhibitors of DNA Methyltransferase 1

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Discovery of potent DNMT1 inhibitors against sickle cell disease using structural-based virtual screening, MM-GBSA and molecular dynamics simulation-based approaches

Sickle cell disease (SCD) is an autosomal recessive genetic disorder affecting millions of people worldwide. A reversible and selective DNMT1 inhibitor, GSK3482364, has been known to decrease the overall methylation activity of DNMT1, resulting in the increase of HbF levels and percentage of HbF-expressing erythrocytes in an in vitro and in vivo model. In this study, a structure-based virtual screening was done with GSK3685032, a co-crystalized ligand of DNMT1 (PDB ID: 6X9K) with an IC50 value of 0.036 μM and identified 3988 compounds from three databases (ChEMBL, PubChem and Drug Bank). Using this screening method, we identified around 15 compounds with XP docking scores greater than -8 kcal/mol. Further, prime MM-GBSA calculations have been performed and found compound SCHEMBL19716714 with the highest binding free energy of -83.31 kcal/mol. Finally, four compounds were identified based on glide energy and ΔG bind scores that have the most binding with DG7, DG19, DG20 bases and Lys1535, His1507, Trp1510, Ser1230, which were required for the target enzyme inhibition. Furthermore, molecular dynamics simulation studies of top ligands validate the stability of the docked complexes by examining root mean square deviations, root mean square fluctuations, solvent accessible surface area, and radius of gyration graphs from simulation trajectories. These findings suggest that the top four hit compounds may be capable of inhibiting DNMT1 and that additional in vitro and in vivo studies will be essential to prove the clinical effectiveness of the selected lead compounds.Communicated by Ramaswamy H. Sarma.

In silico study of selective inhibition mechanism of S-adenosyl-L-methionine analogs for human DNA methyltransferase 3A

Epigenetic mechanisms leading to transcriptional regulation, including DNA methylation, are frequently dysregulated in diverse cancers. Interfering with aberrant DNA methylation performed by DNA cytosine methyltransferases (DNMTs) is a clinically validated approach. In particular, the selective inhibition of the de novo DNMT3A and DNMT3B enzymes, whose expression is limited to early embryogenesis, adult stem cells, and in cancers, is particularly attractive; such selectivity is likely to attenuate the dose limiting toxicity shown by current, non-selective DNMT inhibitors. We use molecular dynamics (MD) based computational analysis to study known small molecule binders of DNMT3A, then propose reversible, tight binding, and selective inhibitors that exploit the Asn¹¹⁹²/Arg⁶⁸⁸ difference between the maintenance DNMT1 and DNMT3A near the active site. A similar strategy exploiting the presence of a unique active site cysteine Cys⁶⁶⁶ is used to propose DNMT3A-selective irreversible inhibitors. We report our results of relative binding energies of the known and proposed compounds estimated using MM/GBSA and umbrella sampling (US) techniques, and our evaluation of other end-point binding free energy calculation methods for these receptors. These calculations offer insight into the potential for small molecules to selectively target the active site of DNMT3A.