Efficacy of different protein descriptors in predicting protein functional families using support vector machine

Abstract

Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families; there is thus a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance; in particular, three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets. This study suggests that currently used descriptor-sets are generally useful for classifying proteins and that prediction performance may be enhanced by exploring combinations of descriptors.