Computational prediction of human immunodeficiency resistance to reverse transcriptase inhibitors

Abstract

Human immunodeficiency virus (HIV) causes acquired immunodeficiency syndrome (AIDS) and leads to over one million of deaths annually. Highly active antiretroviral treatment (HAART) is a gold standard in the HIV/AIDS therapy. Nucleoside and non-nucleoside inhibitors of HIV reverse transcriptase (RT) are important component of HAART, but their effect depends on the HIV susceptibility/resistance. HIV resistance mainly occurs due to mutations leading to conformational changes in the three-dimensional structure of HIV RT. The aim of our work was to develop and test a computational method for prediction of HIV resistance associated with the mutations in HIV RT. Earlier we have developed a method for prediction of HIV type 1 (HIV-1) resistance; it is based on the usage of position-specific descriptors. These descriptors are generated using the particular amino acid residue and its position; the position of certain residue is determined in a multiple alignment. The training set consisted of more than 1900 sequences of HIV RT from the Stanford HIV Drug Resistance database; for these HIV RT variants experimental data on their resistance to ten inhibitors are presented. Balanced accuracy of prediction varies from 80% to 99% depending on the method of classification (support vector machine, Naive Bayes, random forest, convolutional neural networks) and the drug, resistance to which is obtained. Maximal balanced accuracy was obtained for prediction of resistance to zidovudine, stavudine, didanosine and efavirenz by the random forest classifier. Average accuracy of prediction is 89%.