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Construction of a support vector machine (SVM) for accurately detecting domain boundaries

构建支持向量机（SVM）以准确检测域边界

基本信息

批准号：
18500225
负责人：
KURODA Yutaka
金额：
$ 2.48万
依托单位：
Tokyo University of Agriculture and Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500225/
关键词：
Domain boundary prediction Support Vector Machine(SVM)Strutural domain Domain linkers Machine learning Large scale computer aided 機会学習

项目摘要

The prediction of structural domains in novel protein sequences is becoming of practical importance. One important area of application is the development of computer-aided techniques for identifying, at a low cost, novel protein domain targets for large-scale functional and structural proteomics. Traditional computer-aided methods rely on sequence similarity to domain databases such as Pfam, Prosite or SMART. However, methods that work independently from domain databases are required for detecting novel domains. Such methods need to predict domain regions solely from the information contained in the amino acid sequence of the protein of interest. In this project, we report a Support Vector Machine (SVM) that identifies domain linkers, which are loops separating two structural domains. We constructed a multi-domain protein database based on SCOP and CATH domain boundary definition. First, we selected domains that do not form inter-domain interactions, as defined by the presence of inter-domain VdW, Hbonds and SS-bonds, and are independently foldable. From this set, we further selected domain boundaries that form loops as defined by DSSP. Domain boundaries that fulfilled both conditions were called linkers and used for training and testing the SVM.We developed a domain linker prediction (DLP-SVM) based on SVMlight. The sensitivity and the specificity were, respectively, 46.8% and 57.1%. These values are over 5.1 and 6.8%, respectively, higher than previously reported methods. DLP-SVM is freely available at : http://www.tuat.ac.jp/~ domserv/cgi-bin/DLP-SVM.cgiA travel grant from the Protein Science Society of Japan was awarded to PhD student Teippei Ebina for presenting this research at the PRICIPS 2008 conference held in Cairns, Australia, June 2008.

新型蛋白质序列中结构域的预测变得具有实际重要性。一个重要的应用领域是开发计算机辅助技术，以低成本识别大规模功能和结构蛋白质组学的新型蛋白质结构域靶标。传统的计算机辅助方法依赖于 Pfam、Prosite 或 SMART 等领域数据库的序列相似性。然而，检测新域需要独立于域数据库工作的方法。此类方法需要仅根据目标蛋白质的氨基酸序列中包含的信息来预测结构域区域。在这个项目中，我们报告了一个识别域链接器的支持向量机（SVM），域链接器是分隔两个结构域的循环。我们构建了基于SCOP和CATH域边界定义的多域蛋白质数据库。首先，我们选择了不形成域间相互作用的域，如域间 VdW、Hbonds 和 SS-bonds 的存在所定义的，并且是独立可折叠的。从这个集合中，我们进一步选择了形成 DSSP 定义的循环的域边界。满足这两个条件的域边界称为链接器，用于训练和测试SVM。我们开发了基于SVMlight的域链接器预测（DLP-SVM）。敏感性和特异性分别为 46.8% 和 57.1%。这些值分别比之前报道的方法高出 5.1% 和 6.8% 以上。 DLP-SVM 可在以下网址免费获取：http://www.tuat.ac.jp/~ domserv/cgi-bin/DLP-SVM.cgi 博士生 Teippei Ebina 获得日本蛋白质科学学会的旅费资助，以表彰其在 2008 年 6 月于澳大利亚凯恩斯举行的 PRICIPS 2008 会议上展示这项研究成果。