Computational modeling of the N-terminus of the human dopamine transporter and its interaction with PIP2 -containing membranes

人类多巴胺转运蛋白N端及其与含PIP2膜相互作用的计算模型

阅读:1

Abstract

The dopamine transporter (DAT) is a transmembrane protein belonging to the family of neurotransmitter:sodium symporters (NSS). Members of the NSS are responsible for the clearance of neurotransmitters from the synaptic cleft, and for their translocation back into the presynaptic nerve terminal. The DAT contains long intracellular N- and C-terminal domains that are strongly implicated in the transporter function. The N-terminus (N-term), in particular, regulates the reverse transport (efflux) of the substrate through DAT. Currently, the molecular mechanisms of the efflux remain elusive in large part due to lack of structural information on the N-terminal segment. Here we report a computational model of the N-term of the human DAT (hDAT), obtained through an ab initio structure prediction, in combination with extensive atomistic molecular dynamics (MD) simulations in the context of a lipid membrane. Our analysis reveals that whereas the N-term is a highly dynamic domain, it contains secondary structure elements that remain stable in the long MD trajectories of interactions with the bilayer (totaling >2.2 μs). Combining MD simulations with continuum mean-field modeling we found that the N-term engages with lipid membranes through electrostatic interactions with the charged lipids PIP2 (phosphatidylinositol 4,5-Biphosphate) or PS (phosphatidylserine) that are present in these bilayers. We identify specific motifs along the N-term implicated in such interactions and show that differential modes of N-term/membrane association result in differential positioning of the structured segments on the membrane surface. These results will inform future structure-based studies that will elucidate the mechanistic role of the N-term in DAT function.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。