Abstract Zhi Qun Tang¹, Hong Huang Lin¹, Hai Lei Zhang¹, Lian Yi Han¹, Xin Chen² and Yu Zong Chen^1,3

¹Department of Pharmacy and Department of Computational Science, National University of Singapore, Republic of Singapore, 117543. ²Department of Biotechnology, Zhejiang University, Hang Zhou, Zhejiang Province, P. R. China, 310029. ³Shanghai Center for Bioinformatics Technology, Shanghai, P. R. China, 201203.

Abstract: Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clusteringbased, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying diffi culties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

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