A novel protein motif finding algorithm for classification of the ligase subfamilies

The algorithm of extracting motifs from a family or subfamily is still a hot spot in bioinformatics. It not only contributes to understand functions of proteins and predicts the classification which a unknown protein sequence belongs to, but also helps to study the protein-protein interaction. In this paper, we present a novel algorithm to extract motifs of a subfamily, which is based on feature selection and position connection. Position connection is applied to generate motifs, which is the hybrid method with mechanism of vote decision-making to construct the classifier of the ligase subfamilies. Through testing in the database, more than 95.87% predictive accuracy is achieved. The result demonstrates that this novel method is practical. In addition, the method illuminates that motifs play an important role to classify proteins and research the characteristics of the subfamilies or families of protein database. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ;
Chetty, Madhu ;
Ahmad, Shandar ;
Ngom, Alioune ;
Teng, Shyh Wei ;
Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ;
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Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

从家族或亚家族中提取基序的算法在生物信息学领域仍是一个研究热点。这不仅有助于理解蛋白质的功能，预测未知蛋白质序列所属的分类，而且还有助于研究蛋白质之间的相互作用。在本文中，我们提出了一种基于特征选择和位置连接的亚家族基序提取的新算法，该算法通过投票决策机制的混合方法构建了连接酶亚家族的分类器。经过数据库测试，实现了超过95.87%的预测准确性。这一结果证明了该新方法的实用性。此外，该方法还揭示了基序在蛋白质分类和研究蛋白质数据库的亚家族或家族特征中发挥着至关重要的作用。可在PRIB 2008会议论文集中找到相关信息，链接为：http://dx.doi.org/10.1007/978-3-540-88436-1。