Construction, Analysis, and Utilization of Co-Phosphorylation Networks to Characterize Cellular Signaling

最近的研究表明，可能超过70%的人类蛋白质可以被磷酸化--因此 对磷酸化程度和动力学的表征对于理解广泛的 生物和生化过程。为了满足高通量筛选磷酸化的需要， 基于质谱学的技术正在迅速发展，包括使用金属亲和富集法 以及磷化抗体浓缩，以加强对亚磷酸盐的检测和定量 磷酸化水平。这些技术可以无针对性地量化蛋白的磷酸化水平 在一个给定的样本中有数千个亚磷酸盐。今天，许多实验室正在利用这些技术来 通过检查药物和击倒的扰动来比较表征信号环境 方法和评估癌症、传染病和正常发育的不同表型。 然而，与其他组数据来源(例如，基因组、转录组和节间组数据)相比， 磷酸蛋白质组数据的共享及其二次和综合分析相对较少 目前很常见。出于这些原因，不同实验室产生的磷酸蛋白质组数据 不同生物过程背景下的磷酸化动力学没有得到充分利用 潜力。 该项目旨在开发使用来自不同研究的磷酸化数据的计算方法 和不同的实验室，以阐明相互作用和翻译后修饰的动力学 相关的蛋白质、磷酸盐、激酶和磷酸酶。为此，我们建议共同建造 通过评估以下各项之间的相关性(或一般的统计相关性)来构建磷酸化网络 各种生物状态的亚磷酸盐对，并开发算法和方法来分析和 利用这些网络来开发各种问题的系统生物学解决方案。建议的计算性 管道将向科学界介绍共磷酸化(co-P)网络的概念，提供 建设、统计评估和功能评估的综合方法 网络，并验证它们在预测性任务中的使用。具体地说，为了进行实验验证，我们将使用激酶 来自阿尔茨海默病体内模型的肺癌细胞系和组织样本的抑制研究。这个 该项目的潜在影响远远超出该项目将产生的结果；利用co-P 广泛的生物医学科学家的网络可能会对人类的生物学产生重大的洞察 细胞信号和推动药物发现，以加强生物医学科学。

Recent studies show that likely over 70% of human proteins can be phosphorylated – therefore characterization of phosphorylation extents and dynamics is critical to understanding a broad range of biological and biochemical processes. To meet the needs for high-throughput screening of phosphorylation, technologies based on mass spectrometry are advancing rapidly, including use of metal-affinity enrichment as well as phospho-antibody enrichment to enhance the detection of phosphosites and quantification of phosphorylation levels. These technologies enable untargeted quantification of the phosphorylation levels of thousands of phosphosites in a given sample. Today, many labs are utilizing these technologies to comparatively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches and assessing diverse phenotypes in cancer, infectious disease, and normal development. However, as compared to other sources of omic data (e.g., genomic, transcriptomic, and interactomic data), sharing of phospho-proteomic data, as well as its secondary and integrated analysis are relatively less common at present. For these reasons, phospho-proteomic data generated by different labs that capture phosphorylation dynamics in the context of diverse biological processes are not being utilized to their full potential. This project aims to develop computational methods that will use phosphorylation data from diverse studies and different labs to elucidate the dynamics of the interactions and post-translational modifications among relevant proteins, phosphosites, kinases, and phosphatases. For this purpose, we propose to construct co- phosphorylation networks by assessing the correlation (or statistical dependency in general terms) between pairs of phosphosites across a range of biological states, and develop algorithms and methods to analyze and utilize these networks to develop systems biology solutions to various problems. The proposed computational pipeline will introduce the notion of co-phosphorylation (co-P) networks to the scientific community, provide comprehensive methodologies for the construction, statistical assessment, and functional assessment of these networks, and validate their use in predictive tasks. Specifically, for experimental validation, we will use kinase inhibition studies in lung cancer cell lines and tissue samples from in vivo models of Alzheimer’s disease. The potential impact of this project is well beyond the results that will be generated by this project; utilization of co-P networks by a broad range of biomedical scientists will likely generate significant insights into the biology of cellular signaling and drive drug discovery for enhancing biomedical science.