Faculty Opinions recommendation of Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET.
Year: 2011
Type: peer-review
Abstract: 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. PMID: 21885739 Funding information This work was supported by: NIGMS NIH HHS, United States Grant ID: U54 GM093342 NIGMS NIH HHS, United States Grant ID: R01 GM054762 NIGMS NIH HHS, United States Grant ID: U01 GM061390 NIGMS NIH HHS, United States Grant ID: F32 GM088991 NIGMS NIH HHS, United States Grant ID: P01 GM71790 NIGMS NIH HHS, United States Grant ID: U54 GM094625 NIGMS NIH HHS, United States Grant ID: P01 GM071790 NIGMS NIH HHS, United States Grant ID: R01 GM54762 NIGMS NIH HHS, United States Grant ID: U01 GM61390 More Less keyboard_arrow_down
Author: Lucy Forrest
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W4246935966