CAREER: On-line Service for Predicting Protein Phosphorylation Dynamics Under Unseen Perturbations

职业：预测看不见的扰动下蛋白质磷酸化动力学的在线服务

• 批准号: 1452656
• 负责人: Yuanfang Guan
• 金额: $ 93.97万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452656&HistoricalAwards=false
• 关键词: CAREER; line; Service; Predicting; Protein

Signal transduction through phosphorylation of proteins is a fundamental process involved in most of the biological pathways in all living species. It is also a dynamic process: the phosphorylation status of proteins constantly rewires in different organisms, tissues, cell lineages, and in the same cell but under different conditions. While experimental approaches such as phosphoproteomics have greatly advanced the study of such dynamics, we have only recently begun to construct computational models to estimate the phosphorylation status of proteins under unseen perturbations. The goal of this project is to develop an accurate and efficient method for predicting protein phosphorylation dynamics, and to establish an on-line service system for researchers to upload their own data and predict new dynamics under unseen perturbations. The research will implement an on-line tool, which will have wide applications from prokaryotes to higher, complex organisms, under a variety of conditions. Local high-school students will be recruited to participate in the project, which may spin off for them to compete in national and international science project competitions.Current models for reconstructing phosphorylation relationships between proteins using time-course data mainly rely on searching the large space of possible network structures, a process that is time-consuming and not directly applicable to predicting responses under unseen interventions. This research will investigate a fundamental solution, truncated singular value decomposition with graph partitioning, to estimate phosphorylation levels given new perturbations. This method will be orders of magnitude faster and more accurate than contemporary methods. These algorithms will be tested using in silico simulation as well as phosphoproteomics data. The research will implement an on-line tool, which will allow biology users from diverse research domains to upload their time-course phosphoproteomic data, retrieve the reconstructed phosphorylation relationships specific to their organism and cell type, and predict the phosphorylation levels under unseen perturbations. The results of the project can be found at http://guanlab.ccmb.med.umich.edu/research.

通过蛋白质的磷酸化进行的信号转导是所有生物物种中大多数生物途径中的一个基本过程。这也是一个动态的过程：蛋白质的磷酸化状态在不同的生物体、组织、细胞系中不断地重新连接，在同一细胞中但在不同的条件下。虽然实验方法，如磷蛋白质组学，极大地推动了这种动力学的研究，但我们最近才开始构建计算模型来估计未知扰动下蛋白质的磷酸化状态。本项目的目标是开发一种准确、高效的蛋白质磷酸化动力学预测方法，并建立一个在线服务系统，供研究人员上传自己的数据并预测未知扰动下的新动力学。这项研究将实现一个在线工具，它将在各种条件下从原核生物到更高级、更复杂的生物有广泛的应用。将招募当地的高中生参与该项目，这可能会为他们带来参加国内和国际科学项目竞争的机会。目前使用时间-过程数据重建蛋白质之间的磷酸化关系的模型主要依赖于搜索可能的网络结构的大空间，这一过程非常耗时，并且不直接适用于在未知干预下预测反应。这项研究将研究一个基本的解决方案，截断奇异值分解和图划分，以估计在新的扰动下的磷酸化水平。这种方法将比当代方法快几个数量级，更准确。这些算法将在电子计算机模拟和磷酸蛋白质组学数据中进行测试。这项研究将实施一个在线工具，该工具将允许来自不同研究领域的生物学用户上传他们的时间进程磷蛋白质组数据，检索特定于他们的生物体和细胞类型的重建的磷酸化关系，并预测在看不见的扰动下的磷酸化水平。该项目的结果可在http://guanlab.ccmb.med.umich.edu/research.上查阅。