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Improved Bevirimat Resistance Prediction by Combination of Structural and Sequence-Based Classifiers

Improved Bevirimat Resistance Prediction by Combination of Structural and Sequence-Based Classifiers

J Nikolaj Dybowski, Mona Riemenschneider, Sascha Hauke, Martin Pyka, Jens Verheyen, Daniel Hoffmann, Dominik Heider

BioData Min. 2011 Nov 14;4:26.

PMID: 22082002

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.

Chemicals Related in the Paper:

Catalog Number	Product Name	Structure	CAS Number	Price
AP174022425	Bevirimat		174022-42-5	Price

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