Department of Bioengineering and Therapeutic Sciences, and California Institute for Quantitative Biosciences, University of California, San Francisco, CA 94158, USA.
Proc Natl Acad Sci U S A. 2011 Sep 20;108(38):15810-5. doi: 10.1073/pnas.1106030108. Epub 2011 Sep 1.
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.
去甲肾上腺素转运体（NET）将去甲肾上腺素从突触中转运到突触前神经元，去甲肾上腺素通过与各种受体（例如β2-肾上腺素能受体）结合来调节与心血管效应和行为特征相关的信号通路。NET 是各种处方药物的已知靶点，包括抗抑郁药和精神兴奋剂，并且可能介导其他处方药物的非靶向效应。在这里，我们使用虚拟配体筛选，然后对预测配体进行实验验证，来鉴定与 NET 结合的处方药物。我们首先基于 NET 与原核 NET 同源物亮氨酸转运蛋白 LeuT 的氨基酸序列比对构建了 NET 的比较结构模型。通过确认已知的 NET 配体可以比非结合分子更有利地对接来验证建模的结合位点。然后，我们针对 NET 模型从京都基因与基因组百科全书（KEGG DRUG）计算筛选了 6436 种药物。在实验中测试的 18 种高分药物中有 10 种被发现是 NET 抑制剂；其中 5 种是 NET 的化学新颖配体。这些结果可能使几种交感神经（tuaminoheptane）和抗抑郁药（tranylcypromine）药物的疗效以及糖尿病（phenformin）和阿尔茨海默病（talsaclidine）药物的副作用合理化。这些观察结果突出了针对比较模型进行虚拟筛选的效用，即使在目标与模板结构的序列同一性小于 30%并且在主要结合位点中没有已知配体的情况下也是如此。
