Computational and Mathematical Study in Protein Interactions and Functions

蛋白质相互作用和功能的计算和数学研究

• 批准号: 0241102
• 负责人: Fengzhu Sun
• 金额: $ 103.6万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0241102&HistoricalAwards=false
• 关键词: Computational; Mathematical; Study; Protein; Interactions

0241102SunDetermining protein interactions and functions are central in most proteomics projects. This research focuses on the development of statistical and computational methods for the analysis of protein interaction data coming from high-throughput proteomic technologies such as yeast two-hybrid assays and mass spectrometry, and protein function data coming from databases of large-scale function annotations. The research involves the study of the following two important problems in biology: (1) identifying domain-domain interactions and protein-domain interactions from a large number of protein-protein interactions, and (2) assigning functions to unknown proteins from the knowledge of the functions of the annotated proteins, gene expression profiles, gene knockouts, protein sequence similarities, and protein-protein interactions. These results obtained for yeast proteins can help us predict interactions and functions of humanproteins. Training postdoctoral associates and graduate students from mathematics, statistics, computer science and molecular biology for proteomic research is an important part of the proposed research. Based on the existing excellent education program in the field of computational biology and bioinformatics within the Center for Computational and Experimental Genomics (CCEG) in USC, the principal investigators plan to train future researchers through rigorous course work, seminars, discussion groups, and participation in the research project.In recent years, an increasing number of genomes of model organisms have been sequenced. Using these genomic sequences, researchers have been able to make tremendous progress in the study of genomes. Beyond these successes is the far more challenging and rewarding task of understanding proteomes. In addition to genome sequences, many other databases, such as protein-protein physical interactions, genetic interactions, protein-DNA interactions, and gene expressions, have become available. An important problem is how to combine information from the variety of different databases to understand the biological processes and biological functions of proteins. The principal investigators will develop new statistical and computational methods for estimation and prediction of protein functions and for understanding important biological problems integrating several relevant large databases. They will also train graduate students and postdoctoral associates in the field of computational biology and bioinformatics through their participation in research activities related to the proposed project. The proposed project will generate a suite of computer algorithms related to protein-protein interactions and functional predictions. Both the algorithms and results will be disseminated through the web. The results from this study will be important for basic biological studies as well as for disease related studies from identifying protein functions.

0241102Sun 确定蛋白质相互作用和功能是大多数蛋白质组学项目的核心。本研究的重点是开发统计和计算方法，用于分析来自高通量蛋白质组学技术（例如酵母双杂交测定和质谱法）的蛋白质相互作用数据，以及来自大规模功能注释数据库的蛋白质功能数据。该研究涉及生物学中以下两个重要问题的研究：（1）从大量蛋白质-蛋白质相互作用中识别域-域相互作用和蛋白质-域相互作用，以及（2）根据注释蛋白质的功能、基因表达谱、基因敲除、蛋白质序列相似性和蛋白质-蛋白质相互作用的知识，为未知蛋白质分配功能。酵母蛋白的这些结果可以帮助我们预测人类蛋白的相互作用和功能。培训数学、统计学、计算机科学和分子生物学领域的博士后和研究生进行蛋白质组研究是拟议研究的重要组成部分。基于南加州大学计算和实验基因组学中心（CCEG）现有的计算生物学和生物信息学领域的优秀教育项目，主要研究人员计划通过严格的课程作业、研讨会、讨论小组和参与研究项目来培养未来的研究人员。 近年来，越来越多的模式生物基因组被测序。利用这些基因组序列，研究人员已经能够在基因组研究中取得巨大进展。除了这些成功之外，理解蛋白质组是更具挑战性和回报性的任务。除了基因组序列之外，许多其他数据库，例如蛋白质-蛋白质物理相互作用、遗传相互作用、蛋白质-DNA 相互作用和基因表达，也已变得可用。一个重要的问题是如何结合来自各种不同数据库的信息来了解蛋白质的生物过程和生物功能。主要研究人员将开发新的统计和计算方法，用于估计和预测蛋白质功能，并结合几个相关的大型数据库来理解重要的生物学问题。他们还将通过参与与拟议项目相关的研究活动来培训计算生物学和生物信息学领域的研究生和博士后。拟议的项目将生成一套与蛋白质-蛋白质相互作用和功能预测相关的计算机算法。算法和结果都将通过网络传播。这项研究的结果对于基础生物学研究以及通过识别蛋白质功能进行疾病相关研究非常重要。