Y95 and E444 interaction required for high-affinity S-citalopram binding in the human serotonin transporter

Toward high-resolution modeling of small molecule-ion channel interactions

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters

Background:

The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs.

Objective:

This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted.

Methods:

PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs.

Results:

Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models.

Conclusion:

The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.

Insights to ligand binding to the monoamine transporters-from homology modeling to LeuBAT and dDAT

Understanding of drug binding to the human biogenic amine transporters (BATs) is essential to explain the mechanism of action of these pharmaceuticals but more importantly to be able to develop new and improved compounds to be used in the treatment of depression or drug addiction. Until recently no high resolution structure was available of the BATs and homology modeling was a necessity. Various studies have revealed experimentally validated binding modes of numerous ligands to the BATs using homology modeling. Here we examine and discuss the similarities between the binding models of substrates, antidepressants, psychostimulants, and mazindol in homology models of the human BATs and the recently published crystal structures of the Drosophila dopamine transporter and the engineered protein, LeuBAT. The comparison reveals that careful computational modeling combined with experimental data can be utilized to predict binding of molecules to proteins that agree very well with crystal structures.

Drosophila melanogaster as a genetic model system to study neurotransmitter transporters

The model genetic organism Drosophila melanogaster, commonly known as the fruit fly, uses many of the same neurotransmitters as mammals and very similar mechanisms of neurotransmitter storage, release and recycling. This system offers a variety of powerful molecular-genetic methods for the study of transporters, many of which would be difficult in mammalian models. We review here progress made using Drosophila to understand the function and regulation of neurotransmitter transporters and discuss future directions for its use.

Molecular basis for selective serotonin reuptake inhibition by the antidepressant agent fluoxetine (Prozac)

Inhibitors of the serotonin transporter (SERT) are widely used antidepressant agents, but the structural mechanism for inhibitory activity and selectivity over the closely related norepinephrine transporter (NET) is not well understood. Here we use a combination of chemical, biological, and computational methods to decipher the molecular basis for high-affinity recognition in SERT and selectivity over NET for the prototypical antidepressant drug fluoxetine (Prozac; Eli Lilly, Indianapolis, IN). We show that fluoxetine binds within the central substrate site of human SERT, in agreement with recent X-ray crystal structures of LeuBAT, an engineered monoamine-like version of the bacterial amino acid transporter LeuT. However, the binding orientation of fluoxetine is reversed in our experimentally supported model compared with the LeuBAT structures, emphasizing the need for careful experimental verification when extrapolating findings from crystal structures of bacterial transporters to human relatives. We find that the selectivity of fluoxetine and nisoxetine, a NET selective structural congener of fluoxetine, is controlled by residues in different regions of the transporters, indicating a complex mechanism for selective recognition of structurally similar compounds in SERT and NET. Our findings add important new information on the molecular basis for SERT/NET selectivity of antidepressants, and provide the first assessment of the potential of LeuBAT as a model system for antidepressant binding in human transporters, which is essential for future structure-based drug development of antidepressant drugs with fine-tuned transporter selectivity.

Stereoselective inhibition of serotonin transporters by antimalarial compounds

The serotonin (5-HT) transporter (SERT) is an integral membrane protein that functions to reuptake 5-HT released into the synapse following neurotransmission. This role serves an important regulatory mechanism in neuronal homeostasis. Previous studies have demonstrated that several clinically important antimalarial compounds inhibit serotonin (5-hydroxytryptamine, 5-HT) reuptake. In this study, we examined the details of antimalarial inhibition of 5-HT transport in both Drosophila (dSERT) and human SERT (hSERT) using electrophysiologic, biochemical and computational approaches. We found that the cinchona alkaloids quinidine and cinchonine, which have identical stereochemistry about carbons 8 and 9, exhibited the greatest inhibition of dSERT and hSERT transporter function whereas quinine and cinchonidine, enantiomers of quinidine and cinchonine, respectively, were weaker inhibitors of dSERT and hSERT. Furthermore, SERT mutations known to decrease the binding affinity of many antidepressants affected the cinchona alkaloids in a stereo-specific manner where the similar inhibitory profiles for quinine and cinchonidine (8S,9R) were distinct from quinidine and cinchonine (8R,9S). Small molecule docking studies with hSERT homology models predict that quinine and cinchonidine bind to the central 5-HT binding site (S1) whereas quinidine and cinchonine bind to the S2 site. Taken together, the data presented here support binding of cinchona alkaloids to two different sites on SERT defined by their stereochemistry which implies separate modes of transporter inhibition. Notably, the most potent antimalarial inhibitors of SERT appear to preferentially bind to the S2 site. Our findings provide important insight related to how this class of drugs can modulate the serotonergic system as well as identify compounds that may discriminate between the S1 and S2 binding sites and serve as lead compounds for novel SERT inhibitors.

Small-molecule ligand docking into comparative models with Rosetta

Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models.

Interaction of antidepressants with the serotonin and norepinephrine transporters: mutational studies of the S1 substrate binding pocket

The serotonin transporter (SERT) and the norepinephrine transporter (NET) are sodium-dependent neurotransmitter transporters responsible for reuptake of released serotonin and norepinephrine, respectively, into nerve terminals in the brain. A wide range of inhibitors of SERT and NET are used as treatment of depression and anxiety disorders or as psychostimulant drugs of abuse. Despite their clinical importance, the molecular mechanisms by which various types of antidepressant drugs bind and inhibit SERT and NET are still elusive for the majority of the inhibitors, including the molecular basis for SERT/NET selectivity. Mutational analyses have suggested that a central substrate binding site (denoted the S1 pocket) also harbors an inhibitor binding site. In this study, we determine the effect of mutating six key S1 residues in human SERT (hSERT) and NET (hNET) on the potency of 15 prototypical SERT/NET inhibitors belonging to different drug classes. Analysis of the resulting drug sensitivity profiles provides novel information on drug binding modes in hSERT and hNET and identifies specific S1 residues as important molecular determinants for inhibitor potency and hSERT/hNET selectivity.