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Prediction of protein functional sites by multivariate analysis of amino acid sequences

通过氨基酸序列的多变量分析预测蛋白质功能位点

基本信息

批准号：
63480514
负责人：
KANEHISA Minoru
金额：
$ 3.84万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1988
资助国家：
日本
起止时间：
1988 至 1989
项目状态：
已结题

项目摘要

In order to predict functional sites of proteins from their amino acid sequences, we have developed multivariate analysis and other methods and constructed databases for prediction. The starting point of our multivariate analysis method is to represent the amino acid sequence by a series of numerical values reflecting various biophysicochemical aspects of amino acid residues. For this purpose, we organized a database of amino acid indices by collecting published data for hydrophobicity and other properties. Since many of the reported indices were highly correlated, we performed a cluster analysis for grouping. Then, using discriminant analysis, we designed procedures to select important variables characterizing functional sites from a set of numerous variables defined from amino acid sequence data. The procedures were applied to the prediction of protein secondary structure segments and also to the prediction of glycosylation and phosphorylation sites. For the prediction of antigenicity determining sites, we organized a database with cross references of published peptide fragments and corresponding entries of the NBRF protein sequence database. However, our variable selection procedure did not produce satisfactory prediction. Because it was a severe limitation to represent sequence characteristics only by variables for multivariate analysis, we investigated more flexible methods. Thus, we applied an artificial intelligence method and developed an expert system. An expert system is more advantageous because it can incorporate various observations including results from multivariate analysis methods. We investigated the problem of predicting protein translocation sites in cells with this expert system approach. In summary, the multivariate analysis methods developed here are useful tools by themselves, but they can be more effective when combined with other approaches. Expert systems seem most suitable for practical applications.

为了从氨基酸序列中预测蛋白质的功能位点，我们开发了多变量分析等方法，并建立了预测数据库。我们的多元分析方法的出发点是用一系列反映氨基酸残基的各种生物物理化学方面的数值来表示氨基酸序列。为此，我们通过收集已发表的疏水性和其他性质的数据，建立了氨基酸指数数据库。由于许多报告的指数是高度相关的，我们进行了聚类分析进行分组。然后，使用判别分析，我们设计了程序，从氨基酸序列数据定义的一组众多变量中选择表征功能位点的重要变量。该程序应用于蛋白质二级结构段的预测，也用于糖基化和磷酸化位点的预测。为了预测抗原性决定位点，我们组织了一个数据库，交叉参考已发表的肽片段和NBRF蛋白序列数据库的相应条目。然而，我们的变量选择程序并没有产生令人满意的预测。由于在多变量分析中仅用变量表示序列特征存在严重的局限性，我们研究了更灵活的方法。因此，我们应用人工智能方法，开发了一个专家系统。专家系统更有优势，因为它可以结合各种观察结果，包括多变量分析方法的结果。我们研究了用这种专家系统方法预测细胞中蛋白质易位位点的问题。总之，这里开发的多变量分析方法本身是有用的工具，但当与其他方法结合使用时，它们可以更有效。专家系统似乎最适合实际应用。