Template-Based Protein Structure Prediction Beyond Sequence Homology

超越序列同源性的基于模板的蛋白质结构预测

• 批准号: 0850009
• 负责人: Daisuke Kihara
• 金额: $ 71.6万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850009&HistoricalAwards=false
• 关键词: Template; Based; Protein; Structure; Prediction

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."Purdue University is awarded a grant to develop novel computational algorithms and software for building protein structure models, which can identify and utilize very distantly related template structures for the modeling. Considering the growing number of protein structures produced by structural genomics projects, it is important for protein structure prediction methods to take full advantage of those structures as templates in modeling. However, existing methods rely too heavily on sequence similarity in searching template structures and computing alignments with templates. The proposed project is aimed to push the limit of template searching and structure modeling by extensive use of structural information in computing target-template alignments and by introducing a refinement procedure, which uses an advanced ab initio folding. Key innovations of this project include (1) novel threading algorithms with a probabilistic handling of residue contacts in template proteins, (2) use of suboptimal alignments and multiple templates to improve accuracy of a model, and (3) use of an ab initio folding method for refining the model, which takes estimated errors at each region of the model into account. Improvement of recognition of a template for modeling a target protein will leverage the value of experimentally solved structures and will also lead to a significant reduction of the cost of structural genomics projects by reducing the number of template structures needed for modeling the structures of proteome of organisms.The proposed project is carried out in international collaboration with world experts in the protein structure prediction field. Graduate and undergraduate students in biological sciences and computer science will be trained in cross-listed courses among several departments. Several existing programs at Purdue for recruiting minority students and undergraduate students will contribute to broad participation in the project. Overall the proposed project leverages Purdue University?s efforts in interdisciplinary computational life science and engineering. Software tools developed under this funding may be accessed from the PI's lab website at http://dragon.bio.purdue.edu/.

普渡大学获得了一笔奖金，用于开发用于构建蛋白质结构模型的新型计算算法和软件，这些算法和软件可以识别和利用非常遥远的相关模板结构来进行建模。考虑到结构基因组计划产生的蛋白质结构的数量越来越多，蛋白质结构预测方法在建模时充分利用这些结构作为模板是很重要的。然而，现有的方法在搜索模板结构和计算与模板的比对时过于依赖序列相似性。该项目旨在通过在计算目标模板比对时广泛使用结构信息来突破模板搜索和结构建模的极限，并引入一种使用先进从头折叠的精化程序。该项目的主要创新包括(1)新颖的穿线算法，对模板蛋白质中的残基接触进行概率处理，(2)使用次优比对和多模板来提高模型的精度，以及(3)使用从头计算折叠方法来改进模型，该方法考虑了模型每个区域的估计误差。改进对用于建模目标蛋白质的模板的识别将利用实验解决的结构的价值，并通过减少对生物蛋白质组结构建模所需的模板结构的数量来显著降低结构基因组学项目的成本。生物科学和计算机科学的研究生和本科生将在几个系之间交叉列出的课程中接受培训。普渡大学现有的几个招收少数族裔学生和本科生的计划将有助于广泛参与该项目。总体而言，拟议的项目利用了普渡大学S在跨学科计算生命科学和工程方面的努力。在这笔资金下开发的软件工具可以从PI的实验室网站访问，网址是http://dragon.bio.purdue.edu/.