Combining Structure‐Based Pharmacophore andIn SilicoApproaches to Discover Novel Selective Serotonin Reuptake Inhibitors

Abstract

Inhibition of human serotonin transporter (hSERT) has been reported to be a potent strategy for the treatment for depression. To discover novel selective serotonin reuptake inhibitors (SSRIs), a structure-based pharmacophore model (SBPM) was developed using the docked conformations of six highly active SSRIs. The best SBPM, consisting of four chemical features: two ring aromatics (RAs), one hydrophobic (HY), and one positive ionizable (PI), was further validated using Gunner-Henry (GH) scoring and receiver operating characteristic (ROC) curve methods. This well-validated SBPM was then used as a 3D-query in virtual screening to identify potential hits from National Cancer Institute (NCI) database. These hits were subsequently filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction and molecular docking, and their binding stabilities were validated by 20-ns MD simulations. Finally, only two compounds (NSC175176 and NSC705841) were identified as potential leads, which exhibited higher binding affinities in comparison with the paroxetine. Our results also suggest that cation-π interaction plays a crucial role in stabilizing the hSERT-inhibitor complex. To our knowledge, the present work is the first structure-based virtual screening study for new SSRI discovery, which should be a useful guide for the rapid identification of novel therapeutic agents from chemical database.

Exploring the Inhibitory Mechanism of Approved Selective Norepinephrine Reuptake Inhibitors and Reboxetine Enantiomers by Molecular Dynamics Study

“…In the meantime, computational methods have been proposed and frequently used to elaborate the binding mode between sNRIs and hNET with great efficiency and accuracy 28 . These methods were applied (1) to elucidate binding mechanisms of substrates and inhibitors to monoamine transporter (MAT) 29 30 31 32 33 34 35 36 (2) to discover novel scaffolds of MAT inhibitors by virtual screening 37 38 39 , and (3) to distinguish various molecular mechanisms of enantiomers binding to MAT 40 41 . As one of these powerful computational methods, the molecular dynamics (MD) providing atomic description of protein dynamics and flexibility 42 43 44 45 was employed to simulate the large scale motions of MAT 27 46 47 .…”

Discovery of Novel-Scaffold Monoamine Transporter Ligands via in Silico Screening with the S1 Pocket of the Serotonin Transporter

“…The affinity values, however, were extrapolated from computational modeling predictions as opposed to being pharmacologically verified. 62 Very recently, SERT structure-based VS has been employed utilizing an outward-facing (extracellular-facing) SERT model conformation 63 that allowed simultaneous access to the S1 and S2 pockets; thus, both pockets and the extracellular vestibule served as potential hit compound binding sites. Several VS hits were obtained that displayed nanomolar to low-micromolar K i values and a degree of structural uniqueness; SERT selectivity was not addressed.…”

“…More recently, the SERT S1 pocket was used as a VS tool to identify two novel compounds proposed to possess better SERT affinity than that of paroxetine (Paxil), a classic SSRI with subnanomolar SERT affinity. The affinity values, however, were extrapolated from computational modeling predictions as opposed to being pharmacologically verified . Very recently, SERT structure-based VS has been employed utilizing an outward-facing (extracellular-facing) SERT model conformation that allowed simultaneous access to the S1 and S2 pockets; thus, both pockets and the extracellular vestibule served as potential hit compound binding sites.…”

Computationally Guided Design of Novel Selective Serotonin Reuptake Inhibitors

“…1A). 9 The study also stated that cation-pi interactions could play a role in stabilizing the molecule in the active site by binding to nearby residues ( Fig. 1B).…”

“…1A), a set of functional groups common to all SSRIs. 9 Although novel drugs may contain different functional groups compared to existing drugs, the pharmacophore is essential for activity. 9 A previous study proposed a pharmacophore model that contains two aromatic rings, one hydrophobic group and one positive ionizable group (Fig.…”