Multi-conformation Aproach of ENM-NMA Dynamic-Based Descriptors for HIV Drug Resistance Prediction

Drug resistance is a key factor in the failure of drug therapy, as the antiretroviral therapy against the human immunodeficiency virus (HIV). Due to the high costs of direct phenotypic assays, genotypic assays, based on sequencing of the viral genome or part of it, are commonly used to infer drug resistance via in silico predictions. In these approaches, the interpretation of the sequence information constitutes the biggest challenge. The large amount of data linking genotype and phenotype information provides a framework for predicting drug resistance from genotype, based on machine learning methods. Primarily, the sequence based information is used but largely fails to predict resistance in previously unobserved variants. The inclusion of structural and dynamic information is supposed to improve the predictions but has been limited by their computational cost of calculation. This study shows the feasibility of dynamic descriptors derived from normal mode analysis in elastic network models of HIV type 1 (HIV-1) protease in predicting drug resistance. We show that exploring the pre-configuration of dynamic information covering the intrinsic movement spectrum of proteinase in HIV-1 by multiple conformation approach descriptors improve the classification task.