Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines

Abstract

The Human Immunodeficiency Virus (HIV) encodes an enzyme, called HIV protease, which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides. Many efforts have been devoted to perform accurate predictions on the HIV-protease cleavability of peptides, in order to design efficient inhibitor drugs. Over the last decade, linear and nonlinear supervised learning methods have been extensively used to discriminate between protease-cleavable and non cleavable peptides. In this paper we consider four different proteins encoding schemes and we apply a discrete variant of linear support vector machines to predict their HIV protease-cleavable status. Empirical results indicate the effectiveness of the proposed method, that is able to classify with the highest accuracy the cleavable and non cleavable peptides contained in two publicly available benchmark datasets. Moreover, the optimal classification rules generated are characterized by a strong generalization capability, as shown by their accuracy in predicting the HIV protease cleavable status of peptides in out-of-sample datasets.