Open Access Research

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

J Nikolaj Dybowski1, Mona Riemenschneider2, Sascha Hauke3, Martin Pyka4, Jens Verheyen5, Daniel Hoffmann1 and Dominik Heider1*

Author Affiliations

1 Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany

2 Leibniz Institute for Arteriosclerosis Research, University of Münster, Domagkstr. 3, 48149 Münster, Germany

3 CASED, Technische Universität Darmstadt, Mornewegstr. 32, 64293 Darmstadt, Germany

4 Department of Psychiatry, University of Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany

5 Institute of Virology, University of Cologne, Fuerst-Pueckler-Str. 56, 50935 Cologne, Germany

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BioData Mining 2011, 4:26  doi:10.1186/1756-0381-4-26

Published: 14 November 2011

Abstract

Background

Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.

Results

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.

Conclusions

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.