New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

Abstract

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure–activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson’s R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Pathogenesis of Alzheimer’s Disease and Diversity of 1,2,3-Triazole Scaffold in Drug Development: Design Strategies, Structural Insights, and Therapeutic Potential

“…Cognitive impairments induced by MAO are linked to their direct or indirect influence on cholinesterase, 5-hydroxytryptamine (5-HT), norepinephrine, and glutamatergic action . Besides having higher sequence similarity, both MAO-A and MAO-B display variations in their substrate specificity as well as selectivity of their inhibitors . MAO-A preferentially interacts with 5-HT and norepinephrine, while MAO-B preferentially interacts with phenylethylamine and benzylamine .…”

“…57 Besides having higher sequence similarity, both MAO-A and MAO-B display variations in their substrate specificity as well as selectivity of their inhibitors. 58 MAO-A preferentially interacts with 5-HT and norepinephrine, while MAO-B preferentially interacts with phenylethylamine and benzylamine. 59 However, direct relationship between neurotransmission system and cognitive impairment in Alzheimer's disease is not reported yet but oxidative deamination catalyzed by MAO generates poisonous metabolic products including ammonia, aldehyde, and hydrogen peroxide.…”

Synthesis and Monoamine Oxidase Inhibitory Activity of Halogenated Flavones

“…Furthermore, this residue (Tyr326) seems to be an important determinant of MAO-B activity. 30,31 Therefore, on the basis of our computational models, ligands that form hydrogen-bonding networks with Tyr435 and Cys172 exhibit greater inhibition. Binda et al reported that Tyr435 is an integral part of the "aromatic cage" that holds water molecules in proximity to the coenzyme, thereby forming the hydrophilic recognition pocket for amines.…”

“…However, in the case of 11g , there is an optimal aromatic π–π stacking interaction between the lone aromatic ring of the ligand and Tyr326. Furthermore, this residue (Tyr326) seems to be an important determinant of MAO-B activity. , …”

Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis

“…A total of 1,149,497 published papers were assembled in the Web of Science Core Collection (WoSCC) until June 2023 on the topics of "diabetes", 930,263 of which have been published after the year 2000 The scientific community's interest in the management of this disease has grown considerably (about 40%), observing a significant increase in scientific publications from approximately 17,000 in the year 2000 to approximately 70,000 in the year 2022 (Figure 1). So, in the early stage of drug design, in silico requirements have been managed by using various computational approaches, such as pharmacophore modeling [28][29][30][31], quantitative structure-activity relationships (QSAR) [32][33][34][35], molecular docking [36][37][38][39], molecular dynamics simulation [40][41][42], DFT simulation [35,[43][44][45][46], etc. These techniques have generated notable interest by reducing the time required for experimental trials, as well as human and resource costs.…”