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关注人类单胺转运体选择性。新型人 DAT 和 NET 模型,实验验证及 SERT 亲和力研究。

Focus on Human Monoamine Transporter Selectivity. New Human DAT and NET Models, Experimental Validation, and SERT Affinity Exploration.

机构信息

Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy.

Research Center "E. Piaggio", University of Pisa, Pisa 56122, Italy.

出版信息

ACS Chem Neurosci. 2020 Oct 21;11(20):3214-3232. doi: 10.1021/acschemneuro.0c00304. Epub 2020 Oct 13.

Abstract

The most commonly used antidepressant drugs are the serotonin transporter inhibitors. Their effects depend strongly on the selectivity for a single monoamine transporter compared to other amine transporters or receptors, and the selectivity is roughly influenced by the spatial protein structure. Here, we provide a computational study on three human monoamine transporters, i.e., DAT, NET, and SERT. Starting from the construction of hDAT and hNET models, whose three-dimensional structure is unknown, and the prediction of the binding pose for 19 known inhibitors, 3D-QSAR models of three human transporters were built. The training set variability, which was high in structure and activity profile, was validated using a set of in-house compounds. Results concern more than one aspect. First of all, hDAT and hNET three-dimensional structures were built, validated, and compared to the hSERT one; second, the computational study highlighted the differences in binding site arrangement statistically correlated to inhibitor selectivity; third, the profiling of new inhibitors pointed out a conservation of the inhibitory activity trend between rabbit and human SERT with a difference of about 1 order of magnitude; fourth, binding and functional studies confirmed 4-(benzyloxy)-4-phenylpiperidine and as potent SERT inhibitors. In particular, one of the compounds (compound revealed a higher affinity for SERT than paroxetine in human platelets.

摘要

最常用的抗抑郁药是血清素转运体抑制剂。它们的作用强烈依赖于对单一单胺转运体的选择性,而不是对其他胺转运体或受体的选择性,而选择性大致受到空间蛋白结构的影响。在这里,我们对三种人类单胺转运体,即 DAT、NET 和 SERT 进行了计算研究。从构建三维结构未知的 hDAT 和 hNET 模型以及预测 19 种已知抑制剂的结合构象开始,构建了三种人类转运体的 3D-QSAR 模型。该模型的训练集变异性很高,在结构和活性谱方面得到了验证,使用了一组内部化合物。结果涉及多个方面。首先,构建、验证了 hDAT 和 hNET 的三维结构,并与 hSERT 进行了比较;其次,计算研究突出了与抑制剂选择性统计学相关的结合位点排列的差异;第三,对新抑制剂的分析指出了兔和人 SERT 之间抑制活性趋势的保守性,差异约为 1 个数量级;第四,结合和功能研究证实了 4-(苯甲氧基)-4-苯基哌啶 和 是有效的 SERT 抑制剂。特别是,其中一种化合物(化合物 )在人血小板中对 SERT 的亲和力高于帕罗西汀。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6f9/8015229/670af5263666/cn0c00304_0010.jpg

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