SGER: Algorithms for predicting protein function using interaction maps

SGER：使用相互作用图预测蛋白质功能的算法

• 批准号: 0542187
• 负责人: Mona Singh
• 金额: --
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0542187&HistoricalAwards=false
• 关键词: SGER; Algorithms; predicting; protein; function

Intellectual MeritThe goal of this project is to develop algorithms for analyzing protein interaction maps, inorder to make novel predictions about a protein's biological process. The goal is to providea framework for moving from individual pairwise linkages to exploiting entire interaction networks,where each interaction may arise from either experimental and/or other computational methods. Methods for analyzing interaction networks are in their infancy, and most current approaches predict the function of a protein by considering only the annotations of its direct interactions. In contrast, the proposed methods will use the global connectivity of interaction networks, the relationships between functions, and several high-throughput data sources in making predictions. The hope is that by developing novel network-based algorithms, we will obtain functional predictions for many, as yet, uncharacterized proteins.Broader ImpactBoth PIs teach cross-disciplinary courses in computational biology, and the research outlined here will further enhance their educational efforts. The co-PI, Chazelle, has designed and is teaching a new undergraduate course-an integrated, quantitative introduction to the natural sciences. Together with biologists, physicists, and chemists the PI, Singh, has designed a graduate course Introduction to computational molecular biology and genomics; she has co-taught it with a molecular biologist for the past four years.The proposed work will develop methods using interaction maps for baker's yeast and fruit fly. Humans share many proteins and pathways with these model organisms. Thus, network analysis methods may allowtransfer of information from these organisms to human, potentially revealing critical information about proteins and pathways implicated in human disease. Predictions and software will be made available on the web (www.cs.princeton.edu/mona/software.html).

智力价值该项目的目标是开发分析蛋白质相互作用图谱的算法，以便对蛋白质的生物过程做出新的预测。我们的目标是提供一个框架，从个别成对的联系，以利用整个相互作用网络，其中每个相互作用可能会出现从实验和/或其他计算方法。分析相互作用网络的方法还处于起步阶段，目前大多数方法仅通过考虑其直接相互作用的注释来预测蛋白质的功能。相比之下，所提出的方法将使用交互网络的全局连通性，功能之间的关系，以及几个高吞吐量的数据源进行预测。希望是通过开发新的基于网络的算法，我们将获得许多功能的预测，到目前为止，uncharacterized蛋白质。更广泛的影响两个PI教授计算生物学的跨学科课程，这里概述的研究将进一步加强他们的教育工作。合作PI，Chazelle，已经设计并正在教授一门新的本科课程-一门综合的，定量的自然科学介绍。PI Singh与生物学家、物理学家和化学家一起设计了一门研究生课程《计算分子生物学和基因组学导论》;在过去的四年里，她与一位分子生物学家共同教授这门课程。拟议的工作将开发使用面包酵母和果蝇相互作用图谱的方法。人类与这些模式生物共享许多蛋白质和途径。因此，网络分析方法可能允许从这些生物体向人类转移信息，潜在地揭示与人类疾病有关的蛋白质和途径的关键信息。预测和软件将在网上提供（www.cs.princeton.edu/mona/software.html）。