Intelligent Data Analysis for Identifying Protein Disorder

用于识别蛋白质紊乱的智能数据分析

• 批准号: 9711532
• 负责人: Zoran Obradovic
• 金额: $ 21.99万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-01 至 2001-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9711532&HistoricalAwards=false
• 关键词: Intelligent; Data; Analysis; Identifying; Protein

Despite reported examples of proteins with natively disordered regions, their commonness and function in nature is unknown. Solutions to these problems cannot be found by searching existing protein structure databases since they are likely to be strongly biased against natively disordered proteins. The approach proposed in this project consists of disorder estimation from primary sequence information through an intelligent data analysis. Preliminary results indicate that disordered regions can have crucial function and that such regions are evidently very common in nature. If confirmed by an in depth study, this represents a major shift in the world view of protein structure and function. The aims of the current study are to further investigate this problem by: a) enlarging and improving a preliminary data set of proteins with disordered regions; b) improving the preliminary protein disorder prediction system accuracy, interpretability, and accessibility; c) testing the validity of the obtained knowledge. The techniques for analyzing massive volumes of data are likely to serve as stepping-stones towards the development of sophisticated prediction systems for some important problems in biochemistry. Possible biological applications include studies aimed at understanding protein structure and function, as well as improving protein crystallization process efficiency.

尽管报道了具有天然无序区域的蛋白质的例子，但它们在自然界中的共性和功能是未知的。这些问题的解决方案无法通过搜索现有的蛋白质结构数据库来找到，因为它们可能对天然无序蛋白质有强烈的偏见。在这个项目中提出的方法包括通过智能数据分析从一级序列信息进行无序估计。初步结果表明，无序区域可能具有重要的功能，而且这种区域在自然界中显然非常常见。如果得到深入研究的证实，这代表着世界对蛋白质结构和功能的看法发生了重大转变。本研究的目的是进一步研究这个问题，通过：a）扩大和改进的初步数据集的蛋白质与无序区域; B）提高初步的蛋白质紊乱预测系统的准确性，可解释性和可访问性; c）测试所获得的知识的有效性。分析大量数据的技术很可能成为开发复杂预测系统的垫脚石，用于解决生物化学中的一些重要问题。 可能的生物学应用包括旨在了解蛋白质结构和功能以及提高蛋白质结晶过程效率的研究。