Ifremer, IRSI-SeBiMER, Plouzané, France.
Ifremer, DYNECO-LEBCO, Plouzané, France.
BMC Bioinformatics. 2024 Oct 25;25(1):338. doi: 10.1186/s12859-024-05953-w.
Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes.
KiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest.
The KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable.
蛋白激酶是一个广泛存在于生命之树中的蛋白质超家族，它们通常参与信号转导，使生物体能够感知和响应生物或非生物环境因素。它们在生物体生理学中具有重要作用，包括发育、繁殖、适应环境压力，而它们的失调会导致疾病，包括多种形式的癌症。鉴定任何生物体的蛋白激酶（激酶组）对于理解其生理功能、限制和对环境压力的适应是有用的。越来越多的基因组可用性使得现在可以跨广泛的生物体多样性来检查和比较激酶组。在这里，我们提出了一个遵循 FAIR 原则（可发现、可访问、可互操作和可重用）的管道，该管道有助于从预测的蛋白质组中搜索和鉴定蛋白激酶，并根据真核生物中存在的丝氨酸/苏氨酸/酪氨酸蛋白激酶组对其进行分类。
KiNext 是一个 Nextflow 管道，它重新组合了许多现有的生物信息学工具，以可重复的方式从一组氨基酸序列中搜索和鉴定生物体的蛋白激酶。传统的真核蛋白激酶（ePKs）和非典型蛋白激酶（aPKs）是通过使用从激酶的催化结构域生成的隐马尔可夫模型（HMMs）来识别的。此外，KiNext 通过使用针对每个组专门定制的隐马尔可夫模型（HMMs），将 ePKs 分类为八个激酶组。KiNext 管道的性能通过与已经为两种海洋物种（太平洋牡蛎 Crassostrea gigas 和单细胞绿藻 Ostreoccocus tauri）发表的其他工具获得的先前鉴定的激酶组进行了验证。KiNext 通过发现以前未识别的激酶，并将以前未分类的激酶的很大一部分分配给两个物种中的一个组，从而优于以前的结果。这些结果表明在激酶鉴定和分类方面都有了改进，同时在 FAIR 管道中提供了结果的可追溯性和可重复性。KiNext 提供的默认 HMM 模型最适合真核生物，但可以轻松修改管道以包括其他感兴趣的分类群的 HMM 模型。
KiNext 管道能够基于预测的氨基酸序列（即蛋白质组）高效且可重复地鉴定激酶组。KiNext 旨在易于使用、自动化、便携和可扩展。
