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Tat 蛋白转运系统:有趣的问题和难题。

The Tat protein transport system: intriguing questions and conundrums.

机构信息

Department of Molecular and Cellular Medicine, College of Medicine, The Texas A&M Health Science Center, Texas A&M University, 1114 TAMU, College Station, TX 77843, USA.

出版信息

FEMS Microbiol Lett. 2018 Jun 1;365(12). doi: 10.1093/femsle/fny123.

Abstract

The Tat machinery catalyzes the transport of folded proteins across the cytoplasmic membrane in bacteria and the thylakoid membrane in plants. Transport occurs only in the presence of an electric field (Δψ) and/or a pH (ΔpH) gradient, and thus, Tat transport is considered to be dependent on the proton motive force (pmf). This presents a fundamental and major challenge, namely, that the Tat system catalyzes the movement of large folded protein cargos across a membrane without collapse of ion gradients. Current models argue that the active translocon assembles de novo for each cargo transported, thus providing an effective gating mechanism to minimize ion leakage. A limited structural understanding of the intermediates occurring during transport and the role of the pmf in stabilizing and/or driving this process have hindered the development of more detailed models. A fundamental question that remains unanswered is whether the pmf is actually 'consumed', providing an energetic driving force for transport, or alternatively, whether its presence is instead necessary to provide the appropriate environment for the translocon components to become active. Including addressing this issue in greater detail, we explore a series of additional questions that challenge current models, and, hopefully, motivate future work.

摘要

Tat 机器在细菌中催化折叠蛋白穿过细胞质膜,在植物中催化折叠蛋白穿过类囊体膜的运输。只有在电场(Δψ)和/或 pH(ΔpH)梯度的存在下,转运才会发生,因此,Tat 转运被认为依赖于质子动力势(pmf)。这提出了一个基本的、主要的挑战,即 Tat 系统催化大型折叠蛋白货物在不崩溃离子梯度的情况下穿过膜的运动。目前的模型认为,活性转运蛋白为每个转运的货物重新组装,从而提供了一种有效的门控机制,以最小化离子泄漏。对转运过程中发生的中间产物的有限结构理解以及 pmf 在稳定和/或驱动该过程中的作用阻碍了更详细模型的发展。一个悬而未决的基本问题是 pmf 是否实际上“被消耗”,为转运提供能量驱动力,或者其存在是否相反是为了为转运蛋白组件提供适当的环境使其变得活跃。除了更详细地解决这个问题,我们还探讨了一系列挑战当前模型的其他问题,并希望能激发未来的工作。

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