%0 Journal Article
%T Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers
%A J Nikolaj Dybowski
%A Mona Riemenschneider
%A Sascha Hauke
%A Martin Pyka
%A Jens Verheyen
%A Daniel Hoffmann
%A Dominik Heider
%J BioData Mining
%D 2011
%I BioMed Central
%R 10.1186/1756-0381-4-26
%X We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.Bevirimat (BVM) belongs to a new class of antiretroviral drugs inhibiting the maturation of HIV-1 particles to infectious virions. BVM prevents the final cleavage of precursor protein p25 to p24 and p2. In electron microscopy, these immature particles failed to build a capsid composed of a nucleocapsid (p7) and RNA surrounded by a cone-shaped core assembled from p24 proteins [1]. In selection experiments with BVM mutations at Gag cleavage site p24/p2 BVM resistance emerged and was conferred in phenotypic resistance tests. In contrast, especially natural polymorphisms in the QVT-motif of p2 hampered the effective suppression of viral replication in clinical phase II trails, which also increased the measured resistance factors in cell culture experiments. It was recently shown by Keller et al. that BVM stabilizes the immature Gag lattice and thus, prevents cleavage [2].Machine learning techniques are widely used to predict drug resistance in HIV-1. For instance, Beerenwinkel et al. used support vector machines [3] and decision trees [4] to predict drug resistance of HIV-1 against several protease and reverse transcriptase inhibitors. Other groups also employed artificial neural networks [5,6], rule-based systems [7] and random forests [8,9].In our recen
%U http://www.biodatamining.org/content/4/1/26