Automated Annotation of Function in Protein Structures from Evolutionary-based 3D-Templates

根据基于进化的 3D 模板自动注释蛋白质结构中的功能

• 批准号: 0547695
• 负责人: Olivier Lichtarge
• 金额: --
• 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: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0547695&HistoricalAwards=false
• 关键词: Automated; Annotation; Function; Protein; Structures

PI Olivier Lichtarge, Baylor College of MedicineCo-PI Lydia Kavraki, Rice UniveristyThe goals of this project are to develop automated algorithms to identify functional sites in protein structures and to characterize protein function on a genome scale. The approach is predicated on the Evolutionary Trace method (ET) to locate functional sites in structures. Past NSF support enabled to automate functional site analysis with ET; the extraction of 3D-templates of residues; the search in other structures for geometric matches to these templates; and the statistical likelihood that matched structures perform the function associated with the template. Taken together these steps create a complete, automated functional annotation pipeline that motivates the new goals: to optimize the extraction of 3-D templates that capture the necessary and sufficient determinants of function (Aim 1); to add new template information into our matching algorithms to better evaluate whether molecular mimicry underlies functional similarity (Aim 2); and the development of novel strategies that use multiple templates to identify function (Aim 3). The broader impact of this proposal is manifold. It will strengthen biological research infrastructure 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 based on one-dimensional pattern matching in protein sequences. The proposal will also lead to software that is robust, standardized, and easy to use. High-school, undergraduates and graduate students will participate in the development of the system. The project will broaden participation using campus programs that attract female and minority applications and are being offered at both institutions.

PI Olivier Lichtarge，贝勒医学院 Co-PI Lydia Kavraki，莱斯大学 该项目的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基因组规模上表征蛋白质功能。该方法基于进化追踪方法（ET）来定位结构中的功能位点。过去 NSF 支持使用 ET 自动进行功能位点分析；残基 3D 模板的提取；在其他结构中搜索与这些模板的几何匹配；以及匹配结构执行与模板相关的功能的统计可能性。将这些步骤结合起来，创建一个完整的、自动化的功能注释管道，激发新的目标：优化 3D 模板的提取，捕获必要且充分的功能决定因素（目标 1）；将新的模板信息添加到我们的匹配算法中，以更好地评估分子拟态是否是功能相似性的基础（目标 2）；以及开发使用多个模板来识别功能的新颖策略（目标 3）。该提案的更广泛影响是多方面的。它将通过揭示蛋白质的哪些区域最具生物学相关性以及蛋白质工程和药物设计的逻辑目标来加强生物研究基础设施。其次，它将开发一种新的基因产物功能表征方法，将传统上基于蛋白质序列中一维模式匹配的功能注释策略扩展到三维。该提案还将带来强大、标准化且易于使用的软件。高中生、本科生和研究生都将参与该系统的开发。该项目将利用吸引女性和少数族裔申请的校园项目来扩大参与范围，并在两个机构中提供。