Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

Department of Bioengineering and Therapeutic Sciences, and California Institute for Quantitative Biosciences, University of California, San Francisco, CA 94158, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 09/2011; 108(38):15810-5. DOI: 10.1073/pnas.1106030108
Source: PubMed


The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., β2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer's (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.

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Available from: Hao Fan, Oct 07, 2015
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    • "NE undergoing nonneuronal uptake is metabolized to O-methylated derivatives by catechol-O-methyltransferase (COMT). substances as cocaine and amphetamines (Iversen, 2000; Kristensen et al., 2011; Schlessingera et al., 2011). "
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    ABSTRACT: Neuronal norepinephrine (NE) uptake is a crucial step in noradrenergic neurotransmission that regulates NE concentration in the synaptic cleft. It is a key mechanism mediated by the NE transporter (NET) which takes the neurotransmitter into the presynaptic neuron terminal or the adrenal medulla chromaffin cell. The activity of NET is short and long terms modulated by phosphorylation mediated by protein kinases A, C, and G and calcium-calmodulin-dependent protein kinase, whereas the transporter availability at the cell surface is regulated by glycosylation. Several neuropeptides like angiotensins II, III, and 1-7, bradykinin, natriuretic peptides, as well as endothelins (ETs) regulate a wide variety of biological effects, including noradrenergic transmission and in particular neuronal NE uptake. Diverse reports, including studies from our laboratory, show that ETs differentially modulate the activity and expression of NET not only in normal conditions but also in diverse cardiovascular diseases such as congestive heart failure and hypertension. Current literature supports a key role for the interaction between ETs and NE in maintaining neurotransmission homeostasis and further suggests that this interaction may represent a potential therapeutic target for various diseases, particularly hypertension. © 2015 Elsevier Inc. All rights reserved.
    Vitamins & Hormones 03/2015; 98:371-405. DOI:10.1016/bs.vh.2014.12.013 · 2.04 Impact Factor
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    • "Consequently, the correlation between the docking score obtained for the best poses with experimentally determined binding free energies is usually poor. Nevertheless, in a recent study, Schlessinger et al. used a homology model of a norepinephrine transporter and molecular docking to successfully predict the prescription drugs which specifically bind to it [173]. With the availability of data from chemical genomics and high-throughput screening [28], [174], the combination of multiple flexible docking tools with chemoinformatics may boost the performance of structure-based virtual screening [175]. "
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    ABSTRACT: Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
    PLoS Computational Biology 05/2014; 10(5):e1003554. DOI:10.1371/journal.pcbi.1003554 · 4.62 Impact Factor
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    • "Among the 3 drugs examined, it was found that NB cells uptake MIBG via a receptor-mediated process due to the expression of NET on their surface. Phenformin may use a similar mechanism as MIBG since it has been shown to bind NET (18). Phenformin can also easily diffuse into the cells due to the presence of the phenol ring. "
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    ABSTRACT: In the present study, we investigated the anticancer effects of the mitochondrial inhibitors, metaiodobenzylguanidine (MIBG), metformin and phenformin. 131I-MIBG has been used for scintigraphic detection and the targeted radiotherapy of neuroblastoma (NB), a pediatric malignancy. Non-radiolabeled MIBG has been reported to be cytotoxic to NB cells in vitro and in vivo. However, the mechanisms behind its growth suppressive effects have not yet been fully elucidated. Metformin and phenformin are diabetes medications that are being considered in anticancer therapeutics. We investigated the anticancer mechanisms of action of MIBG and metformin in NB. Our data revealed that both drugs suppressed NB cell growth and that the combination drug treatment was more potent. MIBG reduced MYCN and MYC expression in MYCN-amplified and non-MYCN-amplified NB cells in a dose- and time-dependent manner. Metformin was less effective than MIBG in destabilizing MYC/MYCN. The treatment of NB cells with metformin or MIBG resulted in an increased expression of genes encoding biomarkers for favorable outcome in NB [(ephrin (EFN)B2, EFNB3, EPH receptor B6 (EPHB6), neurotrophic tyrosine kinase, receptor, type 1 (NTRK1), CD44 and Myc-interacting zinc finger protein (MIZ-1)] and tumor suppressor genes [(early growth response 1 (EGR1), EPH receptor A2 (EPHA2), growth arrest and DNA-damage-inducible, beta (GADD45B), neuregulin 1 (NRG1), TP53 apoptosis effector (PERP) and sel-1 suppressor of lin-12-like (C. elegans) (SEL1L)]. Accordingly, metformin and MIBG augmented histone H3 acetylation in these cells. Phenformin also exhibited histone modification and was more effective than metformin in destabilizing MYC/MYCN in NB cells. Our data suggest that the destabilization of MYC/MYCN by MIBG, metformin and phenformin and their effects on histone modification are important mechanisms underlying their anticancer effects.
    International Journal of Molecular Medicine 11/2013; 33(1). DOI:10.3892/ijmm.2013.1545 · 2.09 Impact Factor
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