Alignment-independent Classification of Proteins

与比对无关的蛋白质分类

• 批准号: 7050072
• 负责人: Ivet Bahar
• 金额: $ 27.21万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7050072
• 关键词: Internet; classification; computer assisted sequence analysis; computer human interaction; computer program /software; computer system design /evaluation; molecular biology information system; molecular probes; protein localization; protein protein interaction; protein purification; protein sequence; protein structure function; technology /technique development

DESCRIPTION (provided by applicant): The Human Genome project and related genome projects have stirred great hopes for improving our understanding and treatment of diseases. Central to this process is the automated detection of functional motifs and classification of protein sequences into families and/or subfamilies. Conventional approaches for protein sequence classification usually employ sequence alignment methods; other methods depend on the choice of the features included in the training sets, and on accuracy and availability of data. We propose an alignment-independent classification approach based on a search engine technology that had been successfully used in classifying medical records. Each protein is represented by a multidimensional vector, the elements of which refer to the protein's most discriminative eta-grams (sequences of eta amino acids). Preliminary studies on G protein coupled receptors (GPCRs) showed that a simple Naive Bayes classifier using straightforward eta-gram feature selection in its preprocessing, can outperform existing classifiers including support vector machines on previously investigated, standardized GPCR sequence data subsets. Jackknife tests applied to the Protein Information Resource (PIR) Protein Sequence Database PSD and to the Pfam database (DB) of protein families showed that approximately 70% of the protein sequences are classified correctly. More significantly, the most discriminative eta-grams in a given protein family appear to have a functional or structural role, as suggested by their comparison with the sequence motifs known to be conserved or active in existing DBs and by the examination of the three-dimensional structure of representative members of the family. Encouraged by these results, we propose to pursue the following specific aims: (1) develop a new computational tool for protein sequence analysis and protein classification based on eta-gram distributions, (2) build a comprehensive DB of protein families based on eta-gram distributions and investigate the relationships between this DB and the leading protein classification DBs, (3) determine the functional significance of the top-ranking n-grams, and (4) develop a Java based toolkit that will provide easy-to-use, yet flexible, web interface to researchers from various background. The expected deliverables are the methodology and software for classification without alignment (CWA); a new database of classified proteins, based on CWA; and an on-line server and GUI that will deliver the database and data mining tools to the scientific community in a user-friendly environment.

描述（由申请人提供）：人类基因组计划和相关基因组计划为提高我们对疾病的理解和治疗带来了巨大的希望。该过程的核心是自动检测功能基序并将蛋白质序列分类为家族和/或亚家族。传统的蛋白质序列分类方法通常采用序列比对方法；其他方法取决于训练集中包含的特征的选择以及数据的准确性和可用性。我们提出了一种基于搜索引擎技术的与对齐无关的分类方法，该技术已成功用于医疗记录分类。每个蛋白质都由一个多维向量表示，其元素指的是蛋白质最具辨别力的eta-gram（eta氨基酸序列）。对 G 蛋白偶联受体 (GPCR) 的初步研究表明，使用简单的 eta-gram 特征的简单朴素贝叶斯分类器 预处理中的选择可以优于现有的分类器，包括先前研究的标准化 GPCR 序列数据子集上的支持向量机。应用于蛋白质信息资源 (PIR) 蛋白质序列数据库 PSD 和蛋白质家族 Pfam 数据库 (DB) 的 Jackknife 测试表明，大约 70% 的蛋白质序列被正确分类。 更重要的是，给定蛋白质家族中最具辨别力的 eta-gram 似乎具有功能或结构作用，正如它们与现有 DB 中已知保守或活跃的序列基序的比较以及通过检查三维模型所表明的那样。家庭代表性成员的结构。受到这些结果的鼓舞，我们建议实现以下具体目标： (1) 开发一种新的计算工具，用于基于 eta-gram 分布的蛋白质序列分析和蛋白质分类，(2) 建立基于 eta-gram 分布的蛋白质家族综合数据库，并研究该数据库与主要蛋白质分类之间的关系DB，(3) 确定排名靠前的 n 元语法的功能意义，(4) 开发基于 Java 的工具包，为来自不同背景的研究人员提供易于使用且灵活的 Web 界面。预期交付成果是无需对齐的分类方法和软件（CWA）；基于 CWA 的新的分类蛋白质数据库；以及一个在线服务器和 GUI，它将在用户友好的环境中向科学界提供数据库和数据挖掘工具。