硅藻和其他具有复杂质体的藻类的多类细胞内蛋白靶向预测:ASAFind 2.0

Ansgar GruberBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Cedar McKaySchool of Oceanography, University of Washington, United States of America, Miroslav OborníkBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Gabrielle RocapSchool of Oceanography, University of Washington, United States of America
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引用次数: 0

摘要

源自红藻的复杂质体的硅藻和相关藻类细胞高度区隔。这些质体被四层包膜包围,这些包膜也定义了胞周隔室(PPC),即第二和第三层膜之间的空间。PPC与真核藻类的细胞质相对应,真核藻类是复杂质体的祖先。代谢反应和细胞生物学过程都发生在这个隔室中,然而,它的确切功能仍然是难以捉摸的。在质体蛋白的情况下,蛋白质定位的自动预测被证明对全基因组的代谢探索是有用的,但直到现在,还没有预测ppc蛋白的自动方法。在这里,我们提出了一个更新版本的质体蛋白预测器ASAFind,其中包括可选的PPC蛋白预测。新的ASAFind版本还接受最新版本的signalp(5.0)和TargetP(2.0)输入数据的输出。此外,我们发布了一个python脚本来计算自定义评分矩阵,以便将asafind方法调整到其他藻类组,并包含了在简化的分数截止模式下使用自定义评分矩阵运行预测的选项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0
Cells of diatoms and related algae with complex plastids of red algal origin are highly compartmentalized. These plastids are surrounded by four envelope membranes, which also define the periplastidic compartment (PPC), the space between the second and third membranes. The PPC corresponds to the cytosol of the eukaryotic alga that was the ancestor of the complex plastid. Metabolic reactions as well as cell biological processes take place in this compartment; however, its exact function remains elusive. Automated predictions of protein locations proved useful for genome wide explorations of metabolism in the case of plastid proteins, but until now, no automated method for the prediction of PPC proteins was available. Here, we present an updated version of the plastid protein predictor ASAFind, which includes optional prediction of PPC proteins. The new ASAFind version also accepts the output of the most recent versions of SignalP (5.0) and TargetP (2.0) input data. Furthermore, we release a Python script to calculate custom scoring matrices for adjustment of the ASAFind method to other groups of algae, and included the option to run the predictions with custom scoring matrices in a simplified score cut-off mode.
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