Probing the binding mechanism of substituted pyridine derivatives as effective and selective lysine-specific demethylase 1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations.

Lysine-specific demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. In this work, we carried out a molecular modeling study on the substituted pyridine derivatives as LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations. Molecular docking studies predicted the probable binding mode of ligands, and suggested Lys661 and Asp555 might be key residues. Our 3D-QSAR models exhibited satisfactory internal and external predicted capacity. For the comparative molecular field analysis (CoMFA) model, its training set had q2  of 0.595 and r2 of 0.959, while test set had q2 of 0.512 and r2 of 0.846. For the best comparative molecular similarity indices analysis (CoMSIA) model, its training set had q2 of 0.733 and r2 of 0.982, while test set had q2 of 0.695 and r2 of 0.922. MD simulations result revealed the detailed binding process and found an important conserved water-bridge motif between ligands and protein. The binding free energies calculation using Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach coincided well with the experimental bioactivity and demonstrated that the electrostatic interaction was the major driving force for binding. The energy decomposition pointed out some significant residues (Asp555, Lys661, Trp695, Tyr761 and FAD) for the LSD1 potency increase. Based on these results, five new inhibitors were designed, and their activities were predicted using our 3D-QSAR models. Communicated by Ramaswamy H. Sarma.

Identifying the novel inhibitors of lysine-specific demethylase 1 (LSD1) combining pharmacophore-based and structure-based virtual screening.

Lysine-specific demethylase 1 (LSD1) has been reported to connect with a range of solid tumors. Thus, the exploration of LSD1 inhibitors has emerged as an effective strategy for cancer treatment. In this study, we constructed a pharmacophore model based on a series of flavin adenine dinucleotide (FAD)-competing inhibitors bearing triazole - dithiocarbamate scaffold combining docking, structure-activity relationship (SAR) study, and molecular dynamic (MD) simulation. Meanwhile, another pharmacophore model was also constructed manually, relying on several speculated substrate-competing inhibitors and reported putative vital interactions with LSD1. On the basis of the two pharmacophore models, multi-step virtual screenings (VSs) were performed against substrate-binding pocket and FAD-binding pocket, respectively, combining pharmacophore-based and structure-based strategy to exploit novel LSD1 inhibitors. After bioassay evaluation, four compounds among 21 hits with diverse and novel scaffolds exhibited inhibition activity at the range of 3.63-101.43 µM. Furthermore, substructure-based enrichment was performed, and four compounds with a more potent activity were identified. After that, the time-dependent assay proved that the most potent compound with IC50 2.21 µM inhibits LSD1 activity in a manner of time-independent. In addition, the compound exhibited a cellular inhibitory effect against LSD1 in MGC-803 cells and may inhibit cell migration and invasion by reversing EMT in cultured gastric cancer cells. Considering the binding mode and SAR of the series of compounds, we could roughly deem that these compounds containing 3-methylxanthine scaffold act through occupying substrate-binding pocket competitively. This study presented a new starting point to develop novel LSD1 inhibitors.

Design and synthesis of tranylcypromine derivatives as novel LSD1/HDACs dual inhibitors for cancer treatment.

Lysine specific demethylase 1 (LSD1) and Histone deacetylases (HDACs) are promising drug targets for cancers. Recent studies reveal an important functional interplay between LSD1 and HDACs, and there is evidence for the synergistic effect of combined LSD1 and HDAC inhibitors on cancers. Therefore, development of inhibitors targeting both LSD1 and HDACs might be a promising strategy for epigenetic therapy of cancers. We report herein the synthesis of a series of tranylcypromine derivatives as LSD1/HDACs dual inhibitors. Most compounds showed potent LSD1 and HDACs inhibitory activity, especially compound 7 displayed the most potent inhibitory activity against HDAC1 and HDAC2 with IC50 of 15 nM and 23 nM, as well as potent inhibition against LSD1 with IC50 of 1.20 µM. Compound 7 demonstrated stronger anti-proliferative activities than SAHA with IC50 values ranging from 0.81 to 4.28 µM against MGC-803, MCF-7, SW-620 and A-549 human cancer cell lines. Further mechanistic studies showed that compound 7 treatment in MGC-803 cells dose-dependently increased cellular H3K4 and H3K9 methylation, as well as H3 acetylation, decreased the mitochondrial membrane potential and induced remarkable apoptosis. Docking studies showed that compound 7 can be well docked into the active binding sites of LSD1 and HDAC2. This finding highlights the potential for the development of LSD1/HDACs dual inhibitors as novel anticancer drugs.

3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q2=0.802, r2ncv=0.979, and the best CoMSIA model has q2=0.799, r2ncv=0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design.