ALGORITHMS: Collaborative Research:Development of Vector Space based Methods for Protein Structure Prediction

算法：协作研究：基于向量空间的蛋白质结构预测方法的开发

• 批准号: 0305567
• 负责人: Weili Wu
• 金额: --
• 依托单位: University of Texas at Dallas
• 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: 2003-07-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0305567&HistoricalAwards=false
• 关键词: ALGORITHMS; Collaborative; Research; Development; Vector

What is novel and unique in this proposed research is the proposed design of knowledge-based prediction systems based on optimal vector space representations of proteins that have previously been represented by character strings. If an optimal representation of a character string can be found by a numerical sequence, then a great variety of methodologies from disciplines such as optimization, pattern discovery, and machine learning can be readily applied to new understanding of protein tertiary structure and function.For this, kernel based nonlinear classifiers and nonlinear dimension reduction as well as visualization methods will be developed to provide scalable and elective prediction systems.The prediction systems will be specially tailored for several problems related to protein structure discovery such as protein secondary structure, relative solvent accessibility and disulfide connectivity, as well as prediction of protein-protein interaction. In this proposal the P.I.s describe how they intend to accomplish this, so that their preliminary results can be extended to the more general structure and protein-protein interaction prediction problem. All results obtained will be made available to the research community in order to facilitate further research activity. Using existing web servers, the results will be made available to teaching faculty and graduate and undergraduate students in a suitable tutorial form. This will allow those interested to tailor the material for use in graduate and undergraduate research and class projects. The authors will incorporate the results into current and future course material as well.Special efforts will be made by the two women PIs to provide opportunities for womengraduate students to participate in the proposed research and for development of therelated software, which has long range social impact beyond the scientific results.

这项研究的新颖性和独特之处在于基于知识的预测系统的提议设计，该系统基于先前由字符串表示的蛋白质的最佳向量空间表示。如果可以通过数字序列找到字符串的最佳表示，那么优化、模式发现和机器学习等学科的多种方法可以很容易地应用于对蛋白质三级结构和功能的新理解。为此，将开发基于内核的非线性分类器和非线性降维以及可视化方法，以提供可扩展和选择性的预测系统。预测系统将专门针对多个 与蛋白质结构发现相关的问题，例如蛋白质二级结构、相对溶剂可及性和二硫键连接性，以及蛋白质-蛋白质相互作用的预测。在这个提案中，PI 描述了他们打算如何实现这一目标，以便他们的初步结果可以扩展到更一般的结构和蛋白质-蛋白质相互作用预测问题。获得的所有结果将提供给研究界，以促进进一步的研究活动。使用现有的网络服务器，结果将以合适的教程形式提供给教师以及研究生和本科生。这将使那些有兴趣的人能够定制材料以用于研究生和本科生的研究和课堂项目。作者也将把研究结果纳入当前和未来的课程材料中。两位女性 PI 将做出特别努力，为女研究生提供参与拟议研究和开发相关软件的机会，这些软件具有超越科学成果的长期社会影响。