Published online 7 December 2004
Nucleic Acids Research, Vol. 32 No. 21 © Oxford University Press 2004; all rights reserved
Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach
1 Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Blk SOC1, level 7, 3 Science Drive 2, Singapore 117543, 2 The Key Laboratory for Chemical Biology of FuJian Province, School of Life Sciences, Xiamen University, Xiamen 361005, People's Republic of China and 3 ShangHai Center for Bioinformatics Technology, 100 QinZhou Road, Level 12, ShangHai 200235, Peoples Republic of China
* To whom correspondence should be addressed. Tel: +65 6874 6877; Fax: +65 6774 6756; Email: csccyz{at}nus.edu.sg
Received September 8, 2004; Revised October 23, 2004; Accepted November 17, 2004
The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
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