Xiao Di, Chen Carissa, Yang Pengyi
Computational Systems Biology Group, Children's Medical Research Institute, The University of Sydney, Westmead, New South Wales, Australia.
Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
Proteomics. 2023 Feb;23(3-4):e2200068. doi: 10.1002/pmic.202200068. Epub 2022 May 31.
Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.
蛋白质磷酸化在调节细胞信号传导及其下游转录和翻译调控中起着至关重要的作用。直到最近，蛋白质磷酸化主要通过低通量生化分析方法进行研究。基于质谱（MS）的磷酸化蛋白质组学的发展改变了这一领域，通过能够测量全蛋白质组的磷酸化事件，在一个实验中通常可以鉴定和定量数万个磷酸化位点。这给大规模磷酸化蛋白质组学数据分析带来了重大挑战，使得计算方法和系统方法成为磷酸化蛋白质组学不可或缺的一部分。以前的工作主要集中在综述基于MS的磷酸化蛋白质组学中的实验技术，但该领域计算领域的系统综述仍然缺失。在这里，我们综述了为磷酸化蛋白质组学数据分析而开发的计算方法和工具以及系统方法。我们将它们分为四个方面，包括数据处理、功能分析、磷酸化蛋白质组注释及其与其他组学的整合，并且在每个方面，我们讨论了关键方法和实例研究。最后，我们强调了一些潜在的研究方向，未来的工作将在这些方向上对这个快速发展的领域做出重大贡献。我们希望这篇综述能提供计算系统磷酸化蛋白质组学领域的有用概述，并激发推动未来发展的新研究。
