Exploring the Active Center of the LSD1/CoREST Complex by Molecular Dynamics Simulation Utilizing Its Co-crystallized Co-factor Tetrahydrofolate as a Probe.

Epigenetic targeting of cancer is a recent effort to manipulate the gene without destroying the genetic material. Lysine-specific demethylase 1 (LSD1) is one of the enzymes associated with the chromatin for post-translational modifications, where it demethylates lysine amino acid in the chromatin H3 tail. Many studies showed that inhibiting LSD1 could potentially be used to treat cancer epigenetically. LSD1 is associated with its corepressor protein CoREST, and it uses tetrahydrofolate as a co-factor to accept CH2 from the demethylation process. In this study, the co-crystallized co-factor tetrahydrofolate was utilized to determine possible binding regions in the active center of the LSD1/CoREST complex. Also, the flexibility of the complex has been investigated by molecular dynamics simulation and subsequent analysis by clustering and principal component analysis. This research supported other studies and showed that LSD1/CoREST complex exists in two main conformational structures: open and closed. Furthermore, this study showed that tetrahydrofolate stably binds to the LSD1/CoREST complex, in its open conformation, at its entrance. It then binds to the core of the complex, inducing the closed conformation. Furthermore, the interactions of tetrahydrofolate to these two binding regions and the corresponding binding mode of tetrahydrofolate were investigated to be used in structure-based drug design.

Ligand-based designing, in silico screening, and biological evaluation of new potent fructose-1,6-bisphosphatase (FBPase) inhibitors.

Fructose-1,6-bisphosphatase - hereafter abbreviated as FBPase has been recently implicated in diabetes prompting several attempts to discover and optimize new FBPase inhibitors. Toward this end we explored the pharmacophoric space of 136 FBPase inhibitors using three diverse sets of inhibitors. This identified 520 pharmacophores that were subsequently clustered into 104 groups. Cluster centers were evaluated by receiver operating characteristic (ROC) curves analysis and correlation with bioactivities of collected compounds. Pharmacophore model Hypo1/7 illustrated the best combination of classification power (ROC-AUC) and correlation with bioactivity. Two other pharmacophores (Hypo2/1 and Hypo2/6) were found to be mergeable and their combined model (Hypo2-1/2-6) illustrated excellent ROC performance. We employed Hypo1/7 and Hypo2-1/2-6 models to screen the National Cancer Institute (NCI) list of compounds. In silico mining identified 18 FBPase inhibitors out of which six were of sub-micromolar IC(50) values.

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis.

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.