Algorithms for the Discovery and Geometric-Matching of Hierarchical 3-D Templates of Functional Sites in Protein Structures

蛋白质结构中功能位点分层 3D 模板的发现和几何匹配算法

• 批准号: 0318415
• 负责人: Olivier Lichtarge
• 金额: $ 89.65万
• 依托单位: Baylor College of Medicine
• 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: 2003-09-01 至 2007-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0318415&HistoricalAwards=false
• 关键词: Algorithms; Discovery; Geometric; Matching; Hierarchical

The long-term aims of this research are to develop automated algorithms to identify functional sites in protein structures and to characterize protein function on a proteomic scale. The approach is predicated on the Evolutionary Trace method (ET), which uses evolutionary data to identify key functional residues in proteins. These residues form spatial clusters that predict the location of active sites, or of binding sites, and they can be taken as three-dimensional (3-D) templates that may directly suggest functions when recognized in novel structures. This motivates our specific goals to automate and optimize functional site identification and the extraction of 3-D templates by ET (Aim 1); and then to refine algorithms that search for these templates in other structures, and score the quality of matches and the likelihood that they indicate functional similarity based on molecular mimicry (Aim 2). The broader impact of this proposal is manifold. It will strengthen biological research infrastructure, first, by revealing which regions of proteins are most biologically relevant, and hence logical targets for protein engineering and drug design. Second, it will develop a novel method for functional characterization of gene products by extending to three dimensions a functional annotation strategy traditionally bases on one-dimensional pattern matching on protein sequences. In so doing, this work addresses the fundamental problem in "post-genomic biology": that of linking massive and exponentially growing amounts of raw sequence and structure data to the molecular basis of biological function. The proposal will also lead to software that is robust, standardized, and easy to use, and technology transfer will benefit from the channels opened by the collaboration with the PDB. Another activity with broad impact will be the training of undergraduates and graduate students, and participation in programs that support female and minority applications at both Rice and Baylor College of Medicine.

这项研究的长期目标是开发自动化算法来识别蛋白质结构中的功能位点，并在蛋白质组学规模上表征蛋白质功能。该方法基于进化追踪方法（ET），该方法使用进化数据来识别蛋白质中的关键功能残基。这些残基形成预测活性位点或结合位点的位置的空间簇，并且它们可以被视为三维（3-D）模板，当在新结构中识别时可以直接暗示功能。这促使我们的具体目标是通过ET自动化和优化功能位点识别和3-D模板的提取（目标1）;然后改进在其他结构中搜索这些模板的算法，并对匹配的质量和它们基于分子模拟指示功能相似性的可能性进行评分（目标2）。这一提议的广泛影响是多方面的。它将加强生物学研究基础设施，首先，揭示蛋白质的哪些区域与生物学最相关，从而为蛋白质工程和药物设计提供逻辑目标。其次，它将开发一种新的方法，通过扩展到三维的功能注释策略，传统上基于一维模式匹配的蛋白质序列的基因产物的功能表征。这样做，这项工作解决了“后基因组生物学”的基本问题：将大量和指数增长的原始序列和结构数据与生物功能的分子基础联系起来。该提案还将产生强大、标准化和易于使用的软件，技术转让将受益于与PDB合作开辟的渠道。另一项具有广泛影响的活动将是本科生和研究生的培训，以及参与支持赖斯和贝勒医学院女性和少数民族申请的项目。