多类内细胞蛋白靶向预测在硅藻及其他藻类中的应用：ASAFind 2.0
Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0
AI总结：
ASAFind 2.0用于预测硅藻及其他藻类中复杂质体间 compartment的蛋白质定位，支持自定义评分矩阵调整。
摘要：
硅藻及相关藻类的细胞具有复杂的红藻起源的质体，这些质体被四层膜包围，定义了质体间 compartment（PPC），即第二与第三膜之间的空间。PPC对应于复杂质体祖先的真核藻类的细胞质。代谢反应及细胞生物学过程发生在该 compartment；然而其确切功能仍不清楚。自动预测蛋白质位置在质体蛋白质基因组水平探索代谢方面证明是有用的，但迄今为止，尚无自动预测PPC蛋白质的 方法。本文提出ASAFind 2.0的更新版本，包含可选的PPC蛋白质预测。新的ASAFind版本也接受SignalP（5.0）和TargetP（2.0）最新版本的输出数据。此外，我们发布了一个Python脚本，用于计算自定义评分矩阵，以调整ASAFind方法以适应其他藻类群，并包含在简化评分截断模式下运行预测的选项。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
