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.

在这项拟议的研究中，新颖和独特的是基于知识的预测系统的拟议设计，基于以前由字符串代表的蛋白质的最佳矢量空间表示。如果字符串的最佳表示可以通过数值序列找到，那么从纪律中找到了多种方法，例如优化，模式发现和机器学习，可以很容易地应用于对蛋白质第三级结构和功能的新理解，对于此，对于此，基于内核的非线性分类器以及基于内核的非线性分类和非线性尺寸的范围的范围的范围的范围是范围的范围，并且可以提供范围的范围，并且可以提供范围的范围，并将其范围用于范围的范围。与蛋白质结构发现有关的问题，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。在此提案中，P.I.S描述了他们打算如何实现这一目标，以便将其初步结果扩展到更一般的结构和蛋白质 - 蛋白质相互作用预测问题。为了促进进一步的研究活动，将获得所有获得的结果。使用现有的Web服务器，结果将可用于以合适的教程形式教授教师和研究生和本科生。这将使人们有兴趣定制用于研究生和本科研究和课堂项目的材料。作者还将将结果纳入当前和未来的课程材料中。两位女性PI将做出特殊的努力，以为女性毕业生提供机会参与拟议的研究和开发与此知识的软件，这超出了科学成果的长期社会影响。