Volume 4 Supplement 1

7th German Conference on Chemoinformatics: 25 CIC-Workshop

Open Access

Molecular dynamics simulations and docking of non-nucleoside reverse transcriptase inhibitors (NNRTIs): a possible approach to personalized HIV treatment

  • Florian D Roessler1, 3,
  • Oliver Korb2,
  • Andreas Bender1,
  • Werner Maentele3 and
  • Peter J Bond1
Journal of Cheminformatics20124(Suppl 1):P32

https://doi.org/10.1186/1758-2946-4-S1-P32

Published: 1 May 2012

The human immunodeficiency virus (HIV) is currently ranked sixth in the worldwide causes of death [1]. One treatment approach is to inhibit reverse transcriptase (RT), an enzyme essential for reverse transcription of viral RNA into DNA before integration into the host genome [2]. By using non-nucleoside RT inhibitors (NNRTIs) [3], which target an allosteric binding site, major side effects can be evaded. Unfortunately, high genetic variability of HIV in combination with selection pressure introduced by drug treatment enables the virus to develop resistance against this drug class by developing point mutations. This situation necessitates treatment with alternative NNRTIs that target the particular RT mutants encountered in a patient.

Previously, proteochemometric approaches have demonstrated some success in predicting binding of particular NNRTIs to individual mutants; however a structurebased approach may help to further improve the predictive success of such models. Hence, our aim is to rationalize the experimental activity of known NNRTIs against a variety of RT mutants by combining molecular modeling, long-timescale atomistic molecular dynamics (MD) simulation sampling and ensemble docking. Initial control experiments on known inhibitor-RT mutant complexes using this protocol were successful, and the predictivity for further complexes is currently being evaluated. In addition to predictive power, MD simulations of multiple RT mutants are providing fundamental insight into the dynamics of the allosteric NNRTI binding site which is useful for the design of future inhibitors. Overall, work of this type is hoped to contribute to the development of predictive efficacy models for individual patients, and hence towards personalized HIV treatment options.

Authors’ Affiliations

(1)
Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge
(2)
The Cambridge Crystallographic Data Centre
(3)
Institute of Biophysics, Johann Wolfgang Goethe-University Frankfurt

References

  1. WHO: The top 10 causes of death.Google Scholar
  2. Chen LF, Hoy J, Lewin SR: Ten years of highly active antiretroviral therapy for HIV infection. Med J Australia. 2007, 186: 146-151.Google Scholar
  3. Turner BG, Summers MF: Structural biology of HIV. J Mol Biol. 1999, 285: 1-32. 10.1006/jmbi.1998.2354.View ArticleGoogle Scholar

Copyright

© Roessler et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.