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Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while...

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Autores principales: Sheik Amamuddy, Olivier, Bishop, Nigel T., Tastan Bishop, Özlem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298995/
https://www.ncbi.nlm.nih.gov/pubmed/30560871
http://dx.doi.org/10.1038/s41598-018-36041-8
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author Sheik Amamuddy, Olivier
Bishop, Nigel T.
Tastan Bishop, Özlem
author_facet Sheik Amamuddy, Olivier
Bishop, Nigel T.
Tastan Bishop, Özlem
author_sort Sheik Amamuddy, Olivier
collection PubMed
description The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles.
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spelling pubmed-62989952018-12-26 Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics Sheik Amamuddy, Olivier Bishop, Nigel T. Tastan Bishop, Özlem Sci Rep Article The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles. Nature Publishing Group UK 2018-12-18 /pmc/articles/PMC6298995/ /pubmed/30560871 http://dx.doi.org/10.1038/s41598-018-36041-8 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Sheik Amamuddy, Olivier
Bishop, Nigel T.
Tastan Bishop, Özlem
Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title_full Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title_fullStr Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title_full_unstemmed Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title_short Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics
title_sort characterizing early drug resistance-related events using geometric ensembles from hiv protease dynamics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298995/
https://www.ncbi.nlm.nih.gov/pubmed/30560871
http://dx.doi.org/10.1038/s41598-018-36041-8
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