Dybowski, Jan Nikolaj; Riemenschneider, Mona; Hauke, Sascha; Pyka, Martin; Verheyen, Jens; Hoffmann, Daniel; Heider, Dominik:
Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers
In: BioData Mining, Vol. 4 (2011), No. 1, p. 26
2011article/chapter in journalOA Gold
BiologyMedicineComputer ScienceScientific institutes » Center of Medical Biotechnology (ZMB) Faculty of Biology » Bioinformatics and Computational Biophysics
Related: 1 publication(s)
Title in English:
Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers
Author:
Dybowski, Jan Nikolaj;Riemenschneider, Mona;Hauke, Sascha;Pyka, Martin;Verheyen, JensUDE
LSF ID
55009
Other
connected with university
;
Hoffmann, DanielUDE
GND
1214304125
LSF ID
16263
ORCID
0000-0003-2973-7869ORCID iD
Other
connected with university
;
Heider, DominikUDE
LSF ID
50610
Other
connected with university
Year of publication:
2011
Open Access?:
OA Gold
DuEPublico 1 ID
Note:
OA Förderung 2012
Language of text:
English

Abstract in English:

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.