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

构建、分析和利用共磷酸化网络来表征细胞信号传导

• 批准号: 9978122
• 负责人: Mehmet Koyuturk
• 金额: $ 33.7万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9978122
• 关键词: Affinity; Alzheimer' s Disease; Alzheimer' s disease model; Antibodies; Biochemical Process; Biological; Biological Models; Biological Process; Cancer cell line; Cell model; Cellular biology; Communicable Diseases; Communities; Complex; Computational algorithm; Computing Methodologies; Data; Data Set; Dependence; Detection; Development; Genomics; Human; Light; Malignant Neoplasms; Malignant neoplasm of lung; Mass Spectrum Analysis; Metals; Methodology; Methods; Modeling; Mutation; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Phosphoric Monoester Hydrolases; Phosphorylation; Phosphotransferases; Post-Translational Protein Processing; Process; Proteins; Proteomics; Recurrence; Sampling; Science; Signal Transduction; Site; Source; Specificity; Substrate Interaction; Systems Biology; Technology; Tissue Sample; Validation; algorithmic methodologies; base; biomedical scientist; comparative; computational pipelines; diverse data; drug discovery; high throughput screening; in vivo Model; insight; knock-down; novel; phosphoproteomics; predictive modeling; protein protein interaction; protein structure; transcriptomics

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.

最近的研究表明，可能超过70%的人类蛋白质可以磷酸化，因此， 磷酸化程度和动力学的表征对于理解广泛的 生物和生物化学过程。为了满足磷酸化高通量筛选的需要， 基于质谱法的技术正在迅速发展，包括使用金属亲和富集， 以及磷酸化抗体富集以增强磷酸化位点的检测和 磷酸化水平。这些技术使得能够非靶向地定量磷酸化水平， 在给定的样品中有数千个磷酸盐。今天，许多实验室正在利用这些技术， 通过检查药物和敲除的扰动来比较表征信号传导景观 方法和评估癌症，传染病和正常发育的不同表型。 然而，与其他组学数据源（例如，基因组、转录组和相互作用组数据）， 磷酸化蛋白质组学数据的共享，以及它的二次和综合分析相对较少 普通目前由于这些原因，不同实验室产生的磷酸化蛋白质组数据， 在不同的生物过程的背景下，磷酸化动力学没有被充分利用， 潜力 这个项目的目的是开发计算方法，将使用磷酸化数据从不同的研究 和不同的实验室，以阐明相互作用和翻译后修饰的动力学， 相关蛋白质、磷酸酶、激酶和磷酸酶。为此，我们建议建立一个共同的， 通过评估磷酸化网络之间的相关性（或一般意义上的统计依赖性）， 在一系列生物状态的磷酸对，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 管道将向科学界介绍共磷酸化（co-P）网络的概念， 综合方法的建设，统计评估和功能评估，这些 网络，并验证其在预测任务中的使用。具体来说，为了实验验证，我们将使用激酶 在肺癌细胞系和来自阿尔茨海默病的体内模型的组织样品中的抑制研究。的 本项目的潜在影响远远超出本项目将产生的结果; 广泛的生物医学科学家的网络可能会产生对生物学的重要见解， 细胞信号传导和驱动药物发现，以增强生物医学科学。