Similarity-Based Indexing and Integration of Protein Sequence and Structure Databases

基于相似性的蛋白质序列和结构数据库的索引和集成

• 批准号: 0750891
• 负责人: Hakan Ferhatosmanoglu
• 金额: $ 49.81万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0750891&HistoricalAwards=false
• 关键词: Similarity; Based; Indexing; Integration; Protein

The Ohio State University is awarded a grant to develop database indexing and similarity search technologies to manage, analyze, and integrate protein sequence and structure databases. Searching for similar sequences and structures in genomic and proteomic databases is a fundamental task in bioinformatics. As the size of the available data increases rapidly, it is essential to build indexing schemes so that integrated maintenance and querying of both sequence and structure data can be achieved effectively. To address this challenge, this project uses a unified theme for both types of data: extracting key features and mapping them into compact feature vectors spaces to facilitate construction of integrated index structures with sensitive, accurate, and efficient querying capabilities. For the sequence data, the project will develop novel feature extraction that involve physiochemical properties of the amino acids and detect low level of similarities. For the structural data, the project will develop methods to capture local structural motifs using contact maps and spatial motifs. In both cases, compact representation of features will be constructed, as well as efficient structure to index them. The approach incorporates biochemical proteins of molecules into feature extraction to discover functional sites of proteins and to return biologically relevant query results. Finally, based on the unified feature representation and indexing framework, the project will develop methods to integrate sequence and structure data effectively at various levels. A holistic approach combining sequence and structure data would help to overcome the limitations of each, and provide more accurate query results. The results of the project will benefit a wide range of application areas in natural and health sciences, including: comparative and functional genomics, protein modeling and design, drug development, and preventative and personalized medicine. Software developed in this project will facilitate large-scale genome-wide research projects which require iterative and interactive querying of available sequence and structure databases. The novel representations and sensitive motif extraction methods developed are also applicable to biological data visualization, classification, and multiple alignment problems. The software and the results of this project will be available at the website: http://bio.cse.ohio-state.edu.

俄亥俄州州立大学获得一笔赠款，用于开发数据库索引和相似性搜索技术，以管理、分析和整合蛋白质序列和结构数据库。在基因组和蛋白质组数据库中寻找相似的序列和结构是生物信息学的一项基本任务。随着可用数据量的迅速增加，建立索引方案以便有效地实现对序列和结构数据的集成维护和查询是非常必要的。为了应对这一挑战，该项目对这两种类型的数据使用统一的主题：提取关键特征并将其映射到紧凑的特征向量空间，以促进构建具有敏感，准确和高效查询功能的集成索引结构。对于序列数据，该项目将开发涉及氨基酸理化特性的新型特征提取并检测低水平的相似性。对于结构数据，该项目将开发使用接触图和空间图案捕捉局部结构图案的方法。在这两种情况下，将构造特征的紧凑表示，以及索引它们的有效结构。该方法将生物化学蛋白质的分子特征提取，发现蛋白质的功能位点，并返回生物相关的查询结果。最后，基于统一的特征表示和索引框架，该项目将开发在各个级别上有效集成序列和结构数据的方法。结合序列和结构数据的整体方法将有助于克服各自的局限性，并提供更准确的查询结果。该项目的成果将使自然科学和健康科学的广泛应用领域受益，包括：比较和功能基因组学，蛋白质建模和设计，药物开发以及预防和个性化医疗。在这个项目中开发的软件将促进大规模的全基因组研究项目，需要迭代和交互式查询可用的序列和结构数据库。开发的新的表示和敏感的基序提取方法也适用于生物数据可视化，分类和多个对齐问题。该软件和该项目的结果将在以下网站上提供：http://bio.cse.ohio-state.edu。