Prediction of HIV-1 Protease Cleavage Site from Octapeptide Sequence Information using Selected Classifiers and Hybrid Descriptors

Abstract Background: In most parts of the world, especially in underdeveloped countries, Acquired Immunodeficiency Syndrome (AIDS) still remains a major cause of death, disability and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of Human Immunodeficiency Virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset and thereafter used a "stratified" ten-fold cross-validation technique for training and testing of the models.Results: Findings from this study show that logistic regression (AUC 0.97, F1 score 0.934 and balanced accuracy 87.2 %), and multi-layer perceptron classifier (AUC 0.97, F1 score 0.907 and balanced accuracy 87.4 %) have close predictive performance to the state-of-the-art model, linear support vector machine (AUC 0.97, F1 score 0.915 and balanced accuracy 90.0 %). Linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier also have good predictive performances (AUC 0.95 - 0.96, F1 score 0.919 - 0.931 and balanced accuracy 82.0 % - 85.7 %).Conclusions: Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.