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KINAID:一种用于磷酸蛋白质组学的基于直系同源的激酶-底物预测与分析工具。

KINAID: an orthology-based kinase-substrate prediction and analysis tool for phosphoproteomics.

作者信息

Aman Javed M, Zhu Audrey W, Wühr Martin, Shvartsman Stanislav Y, Singh Mona

机构信息

Computer Science Department, Princeton University, Princeton, NJ, 08544, United States.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, United States.

出版信息

Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf300.

Abstract

SUMMARY

Proteome-wide datasets of phosphorylated peptides, either measured in a condition of interest or in response to perturbations, are increasingly becoming available for model organisms across the evolutionary spectrum. We introduce KINAID (KINase Activity and Inference Dashboard), an interactive and extensible tool written in Dash/Plotly, that predicts kinase-substrate interactions, uncovers and displays kinases whose substrates are enriched amongst phosphorylated peptides, interactively illustrates kinase-substrate interactions, and clusters phosphopeptides targeted by similar kinases. KINAID is the first tool of its kind that can analyze data from not only Homo sapiens but also 10 additional model organisms (including Mus musculus, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae). We demonstrate KINAID's utility by applying it to recently published S. cerevisiae phosphoproteomics data.

AVAILABILITY AND IMPLEMENTATION

Webserver is available at https://kinaid.princeton.edu; open-source python library is available at https://github.com/Singh-Lab/kinaid; archive is available at https://doi.org/10.24433/CO.8460107.v1.

摘要

摘要

在整个进化谱系的模式生物中,无论是在感兴趣的条件下测量还是响应扰动而测量的磷酸化肽的全蛋白质组数据集越来越多。我们引入了KINAID(激酶活性和推理仪表板),这是一个用Dash/Plotly编写的交互式可扩展工具,它可以预测激酶-底物相互作用,发现并显示其底物在磷酸化肽中富集的激酶,以交互方式说明激酶-底物相互作用,并对相似激酶靶向的磷酸肽进行聚类。KINAID是同类工具中的第一个,它不仅可以分析来自智人的数据,还可以分析另外10种模式生物(包括小家鼠、斑马鱼、黑腹果蝇、秀丽隐杆线虫和酿酒酵母)的数据。我们通过将KINAID应用于最近发表的酿酒酵母磷酸蛋白质组学数据来证明其效用。

可用性和实现方式

网络服务器可在https://kinaid.princeton.edu获得;开源Python库可在https://github.com/Singh-Lab/kinaid获得;存档可在https://doi.org/10.24433/CO.8460107.v1获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ab/12122078/ebaa8d9b8648/btaf300f1.jpg

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