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

通过蛋白质磷酸化进行的信号转导是所有生物中大多数生物途径的基本过程。这也是一个动态过程：蛋白质的磷酸化状态在不同的生物体、组织、细胞系以及在不同条件下的同一细胞中不断地重新连接。虽然磷蛋白质组学等实验方法极大地推进了对这种动力学的研究，但我们直到最近才开始构建计算模型来估计蛋白质在看不见的扰动下的磷酸化状态。该项目的目标是开发一种准确有效的预测蛋白质磷酸化动力学的方法，并建立一个在线服务系统，供研究人员上传自己的数据，并在看不见的扰动下预测新的动力学。该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高级复杂生物的广泛应用。当地高中生将被招募参与该项目，该项目可能会让他们参加国家和国际科学项目竞赛。目前使用时间过程数据重建蛋白质之间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，这一过程既耗时又不能直接适用于预测未知干预下的反应。本研究将探讨一个基本的解决方案，截断奇异值分解与图划分，估计磷酸化水平给出新的扰动。这种方法将比现代方法快几个数量级，也更精确。这些算法将使用硅模拟以及磷蛋白质组学数据进行测试。该研究将实现一个在线工具，它将允许来自不同研究领域的生物学用户上传他们的时间过程磷蛋白质组学数据，检索特定于他们的生物体和细胞类型的重构磷酸化关系，并预测在看不见的扰动下的磷酸化水平。该项目的结果可以在http://guanlab.ccmb.med.umich.edu/research上找到。

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.