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）：人类基因组项目和相关的基因组项目激起了我们对疾病的理解和治疗的巨大希望。此过程的核心是对功能基序的自动检测以及将蛋白质序列分类为家族和/或亚家族。蛋白质序列分类的常规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功能的选择，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独立分类方法，该方法已成功用于分类医疗记录。每种蛋白质由多维矢量表示，其元素是指蛋白质最歧视的ETA-gram（ETA氨基酸序列）。 G蛋白耦合受体（GPCR）的初步研究表明，使用直接ETA-Gram特征的简单幼稚贝叶斯分类器 在其预处理中的选择可以胜过现有的分类器，包括先前研究的标准化GPCR序列数据子集（支持向量机）。用于蛋白质信息资源（PIR）蛋白序列数据库PSD和蛋白质家族的PFAM数据库（DB）的夹克刀测试表明，大约70％的蛋白质序列正确分类。 更重要的是，给定蛋白质家族中最歧视的ETA-Gram似乎具有功能性或结构性作用，如它们与已知的序列基序相比，在现有DBS中保守或活跃，并检查了该家族代表性成员的三维结构。在这些结果的鼓励下，我们建议追求以下具体目标： (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来自各种背景的研究人员。预期的可交付成果是无需对齐的分类方法和软件（CWA）；基于CWA的分类蛋白质的新数据库；以及在线服务器和GUI，将在用户友好的环境中向科学界传递数据库和数据挖掘工具。