Advances in Exploration of Machine Learning Methods for Predicting Functional Class and Interaction Profiles of Proteins and Peptides Irrespective of Sequence Homology

Juan      Cui; Lianyi      Han; Honghuang      Lin; Zhiqun      Tang; Zhiliang      Ji; Zhiwei      Cao; Yixue      Li; Yuzong      Chen

doi:10.2174/157489307780618222

Abstract

Various computational methods have been used for predicting protein function from clues contained in protein sequence. A particular challenge is the functional prediction of proteins that show low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from a variety of sequence-derived structural and physicochemical properties independent of sequence similarity, which showed promising potential for a broad spectrum of proteins including those that show low and no similarity to other proteins. These methods can thus be explored as potential tools to complement similarity-based, clustering-based and structure-based methods for predicting protein function. This article reviews the strategies, algorithms, current progresses, available software and web-servers, and underlying difficulties in using machine learning methods for predicting the functional class of proteins and peptides, and protein-protein interactions. The reported prediction performances in the application of these methods are also presented.

Keywords: Machine learning method, neural network, support vector machine, protein function prediction, protein-protein interaction, peptide prediction

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