Cognitive Framework for HIV-1 Protease Cleavage Site Classification Using Evolutionary Algorithm

Abstract

Acquired immunodeficiency syndrome is the progression from human immunodeficiency virus (HIV) to symptomatic disease. The disease process starts by replicating the viral particles, and the HIV-1 protease is the causative agent behind the phenomenon. To control the replication, protease inhibitors are designed that bind to the active sites. Thus, knowledge about the HIV protease cleavage specificity is critical in the design of effective HIV inhibitors. Several classifiers and encoding techniques have been proposed for the identification of cleavage sites. A recent study shows a linear support vector machine with orthogonal encoding promulgated its sovereignty in the linearly separable peptide data. However, the performance of linear SVM degrades on nonlinear separable data. To cope with the considerable changes between linear and nonlinear separable data, we propose a cognitive framework that utilizes the dexterity of evolutionary algorithm on exploring out the best adaptive settings in the classifier and encoding techniques to improve the predictive capability of the model. Specifically, the proposed cognitive model selects the three decisive factors: physicochemical property for peptide encoding, SVM kernel selection for linear or nonlinear data and parameter tuning of the selected kernel. To validate the performance of the proposed model, the benchmark peptide data are exercised in the experimentation, and the obtained results are compared with the state-of-the-art techniques. Besides, we also analyzed the performance of the proposed model in out-of-sample test, and the extensive experimental results prove the proficiency of the proposed approach for the classification of the HIV-1 protease cleavage sites.