Protein Structure-Based Prediction of Functional Information

基于蛋白质结构的功能信息预测

• 批准号: 0517292
• 负责人: Mary Jo Ondrechen
• 金额: --
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0517292&HistoricalAwards=false
• 关键词: Protein; Structure; Based; Prediction; Functional; Protein; Structure; Based; Prediction; Functional

One of the most important tasks in genome research is to discover the function of the many gene products whose sequences are now known. Structural genomics efforts are rapidly increasing the number of known three-dimensional structures of these gene products, but the determination of function from the structure has proved to be difficult. The goals of this project are to develop and implement a methodology for the prediction of functional sites in proteins from their three-dimensional structures alone. This method will be fast, so that it can analyze structures on a high-throughput basis. The method will also be independent of any sequence or structure alignments or comparisons, so that it is applicable to proteins with few or no homologues, to novel folds, and to engineered structures. The method will begin with the titration curve analyses that have recently been developed by this group and tested specifically for functional site identification. Now these analyses will be coupled with other structure-based calculations, to identify catalytic and binding sites with high degree of accuracy and precision. In addition, hypotheses will be tested in order to understand the basis for the success of the method. The most significant element of intellectual merit of the proposed study is the development of a unique method to aid in the analysis and interpretation of structural genomics data. The ability to predict the function of gene products has broad impact in areas beyond molecular biology, including plant science, agriculture, and counter-bioterrorism efforts. The broader impact of this project includes the training of students, including students from underrepresented groups, at the interface of the biological, physical, and computational sciences. The project will be incorporated into the PI's outreach activities, which include ongoing efforts to promote careers in the sciences to minority youth, including undergraduates and K-12 students.

基因组研究中最重要的任务之一是发现许多序列现在已知的基因产物的功能。 结构性基因组学努力正在迅速增加这些基因产物的已知三维结构的数量，但是从结构中确定功能很困难。该项目的目标是开发和实施一种方法，以仅仅从其三维结构中预测蛋白质中的功能位点。该方法将很快，因此可以高通量基础分析结构。该方法还将独立于任何序列，结构比对或比较，因此它适用于很少或没有同源物的蛋白质，用于新颖的折叠和工程结构。该方法将从该组最近开发的滴定曲线分析开始，并专门测试了功能位点识别。现在，这些分析将与其他基于结构的计算相结合，以识别具有高度准确性和精确度的催化和结合位点。此外，将检验假设，以了解该方法成功的基础。 拟议研究的智力优点的最重要要素是开发一种独特的方法来帮助分析和解释结构基因组学数据。预测基因产物功能的能力对分子生物学以外的领域具有广泛的影响，包括植物科学，农业和反抗恐怖主义努力。 该项目的更广泛影响包括对生物，物理和计算科学的界面的学生进行培训，包括来自代表性不足的群体的学生。该项目将纳入PI的外展活动，其中包括为促进科学职业的持续努力，包括本科生和K-12学生在内。