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线粒体内部靶向信号的分析与预测。

Analysis and prediction of internal mitochondrial targeting signals.

机构信息

Computational Systems Biology, RPTU University of Kaiserslautern-Landau, Kaiserslautern, Germany.

Cell Biology, RPTU University of Kaiserslautern-Landau, Kaiserslautern, Germany.

出版信息

Methods Enzymol. 2024;706:263-283. doi: 10.1016/bs.mie.2024.07.038. Epub 2024 Aug 17.

Abstract

Mitochondria consist of several hundreds of proteins, the vast majority of which are synthesized in the cytosol as precursor proteins from where they are targeted to and imported into mitochondria. The transport of proteins into mitochondria relies on specific targeting information encoded within the protein sequence, known as mitochondrial targeting sequences (MTSs). These N-terminal extensions are usually between 8 and 80 residues long and form amphipathic helices with one hydrophobic and one positively charged surface. Receptors on the mitochondrial surface recognize the MTSs and direct precursors through protein-conducting channels in the outer and inner membrane to the mitochondrial matrix, where presequences are often removed by proteases. In addition to these MTSs, many mitochondrial proteins contain internal matrix targeting sequences (iMTSs) which share the same structural features with MTSs. These iMTSs are neither necessary nor sufficient for mitochondrial targeting, however, they help to increase the import-competence of precursor proteins as they bind to the TOM receptors and presumably facilitate the unfolding of precursors on the mitochondrial surface. Prediction algorithms allow the identification of iMTSs in protein sequences. In this chapter, we present iMLP, an agnostic algorithm for the prediction of iMTS propensity profiles. This iMTS prediction tool is provided via an iMLP webservice at http://iMLP.bio.uni-kl.de and is also available as a BioFSharp application that can be executed locally. We describe and explain the usage of this prediction algorithm and how to interpret the results of this valuable tool.

摘要

线粒体包含数百种蛋白质,其中绝大多数是在细胞质中作为前体蛋白合成的,然后这些前体蛋白被靶向并导入线粒体。蛋白质进入线粒体的运输依赖于蛋白质序列中编码的特定靶向信息,称为线粒体靶向序列(MTS)。这些 N 端延伸通常长 8 到 80 个残基,形成具有一个疏水性和一个正电荷表面的两亲性螺旋。线粒体表面上的受体识别 MTS 并通过外膜和内膜中的蛋白导通道将前体定向到线粒体基质,在前体中通常通过蛋白酶去除前导序列。除了这些 MTS 之外,许多线粒体蛋白还含有内部基质靶向序列(iMTS),它们与 MTS 具有相同的结构特征。这些 iMTS 对于线粒体靶向既不是必需的也不是充分的,但是它们有助于增加前体蛋白的导入能力,因为它们与 TOM 受体结合,并可能有助于在线粒体表面展开前体。预测算法允许在蛋白质序列中识别 iMTS。在本章中,我们介绍了 iMLP,这是一种用于预测 iMTS 倾向分布的不可知算法。这个 iMTS 预测工具可以通过 iMLP webservice 在 http://iMLP.bio.uni-kl.de 上获得,也可以作为一个 BioFSharp 应用程序在本地执行。我们描述并解释了这个预测算法的用法以及如何解释这个有价值工具的结果。

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