Abstract
BackgroundThe human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable.ResultsIn this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance.ConclusionsExperiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1337-6) contains supplementary material, which is available to authorized users.
Highlights
The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS)
We propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms
The extracted biological features from the four benchmark datasets (i.e., 746, 1625, Schilling, and Impens) are used as input features to three machine learning algorithms (i.e., artificial neural network (ANN), decision trees (DT), and logistic regression (LR)), and predictive performance are optimized by area under the ROC curve (AUC) based on the validation set instead of the test set to avoid overfitting
Summary
The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, antagonize AIDS. Experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Introduction In early 1980’s, human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) transition began in perishing modus with a leading cause of death. States in June 1981 by Center for Disease Control (CDC) [2]. It has been 35 years and still HIV is one of the major global public health issues. After the confrontation with AIDS epidemic, unprecedented endeavors have been coordinated towards the advancement of antiretroviral treatments of AIDS that assault and repress the action of HIV-1 protease (HIV-1 PR)
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