ASAFind 2.0:具有复杂质体的硅藻和藻类多类蛋白靶向预测

IF 5.7 1区 生物学 Q1 PLANT SCIENCES
Ansgar Gruber, Marta Vohnoutová, Cedar McKay, Gabrielle Rocap, Miroslav Oborník
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引用次数: 0

摘要

硅藻和相关藻类的质体与红藻起源的复杂质体被四层膜包围,这四层膜也定义了周围质体隔室(PPC),即第二和第三层膜之间的空间。代谢反应和细胞生物学过程发生在PPC中;然而,到目前为止,针对该隔室的蛋白质的全基因组预测是基于手动注释工作。利用已发表的实验蛋白定位作为参考数据,我们开发了第一个PPC蛋白的自动预测方法,并将其作为一个新功能纳入到质体蛋白预测器ASAFind的更新版本中。通过我们的方法,至少有一部分PPC蛋白可以高特异性地预测,估计至少有81个蛋白(占预测蛋白质组的0.7%)针对模型硅藻褐指藻的PPC。PPC蛋白的比例各不相同,因为在假海藻(thalassisira pseudonana)的硅藻基因组中预测了180个PPC蛋白(预测蛋白质组的1.3%)。新的ASAFind版本还可以生成新设计的图形输出,将序列中每个位置对分数的贡献可视化,并接受最新版本的SignalP(5.0)和TargetP(2.0)的输出作为输入数据。此外,我们发布了一个脚本来计算可用于简化分数截止模式预测的自定义得分矩阵。这允许对其他种类的藻类调整方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ASAFind 2.0: multi-class protein targeting prediction for diatoms and algae with complex plastids

ASAFind 2.0: multi-class protein targeting prediction for diatoms and algae with complex plastids

Plastids of diatoms and related algae with complex plastids of red algal origin are surrounded by four membranes, which also define the periplastidic compartment (PPC), the space between the second and third membranes. Metabolic reactions as well as cell biological processes take place in the PPC; however, genome-wide predictions of the proteins targeted to this compartment were so far based on manual annotation work. Using published experimental protein localizations as reference data, we developed the first automatic prediction method for PPC proteins, which we included as a new feature in an updated version of the plastid protein predictor ASAFind. With our method, at least a subset of the PPC proteins can be predicted with high specificity, with an estimate of at least 81 proteins (0.7% of the predicted proteome) targeted to the PPC in the model diatom Phaeodactylum tricornutum. The proportion of PPC proteins varies, since 180 PPC proteins (1.3% of the predicted proteome) were predicted in the genome of the diatom Thalassiosira pseudonana. The new ASAFind version can also generate a newly designed graphical output that visualizes the contribution of each position in the sequence to the score and accepts the output of the recent versions of SignalP (5.0) and TargetP (2.0) as input data. Furthermore, we release a script to calculate custom scoring matrices that can be used for predictions in a simplified score cut-off mode. This allows for adjustments of the method to other groups of algae.

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来源期刊
The Plant Journal
The Plant Journal 生物-植物科学
CiteScore
13.10
自引率
4.20%
发文量
415
审稿时长
2.3 months
期刊介绍: Publishing the best original research papers in all key areas of modern plant biology from the world"s leading laboratories, The Plant Journal provides a dynamic forum for this ever growing international research community. Plant science research is now at the forefront of research in the biological sciences, with breakthroughs in our understanding of fundamental processes in plants matching those in other organisms. The impact of molecular genetics and the availability of model and crop species can be seen in all aspects of plant biology. For publication in The Plant Journal the research must provide a highly significant new contribution to our understanding of plants and be of general interest to the plant science community.
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