Varshney Neha, Mishra Abhinava K
Division of Biological Sciences, Department of Cellular and Molecular Medicine, University of California, San Diego, CA 93093, USA.
Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA.
Proteomes. 2023 May 2;11(2):16. doi: 10.3390/proteomes11020016.
Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.
蛋白质磷酸化是一种关键的翻译后修饰（PTM），是许多细胞信号通路的核心调控机制。几种蛋白激酶和磷酸酶精确地控制着这一生物化学过程。这些蛋白质功能的缺陷与包括癌症在内的许多疾病有关。基于质谱（MS）的生物样品分析可对磷酸化蛋白质组进行深入覆盖。公共数据库中可用的大量MS数据揭示了磷酸化蛋白质组学领域的大数据。为了应对处理大数据以及增强磷酸化位点预测可信度方面的挑战，近年来许多计算算法和基于机器学习的方法得到了快速发展。高分辨率和高灵敏度的实验方法与数据挖掘算法的出现，共同为定量蛋白质组学提供了强大的分析平台。在这篇综述中，我们汇编了用于预测磷酸化位点的生物信息学资源的综合集合，以及它们在癌症背景下的潜在治疗应用。
