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Predicting Bevirimat resistance of HIV-1 from genotype
BACKGROUND: Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224585/ https://www.ncbi.nlm.nih.gov/pubmed/20089140 http://dx.doi.org/10.1186/1471-2105-11-37 |
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author | Heider, Dominik Verheyen, Jens Hoffmann, Daniel |
author_facet | Heider, Dominik Verheyen, Jens Hoffmann, Daniel |
author_sort | Heider, Dominik |
collection | PubMed |
description | BACKGROUND: Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1. RESULTS: We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 ± 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the α-helix in p2, which hints at a possible resistance mechanism. CONCLUSIONS: We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease. |
format | Online Article Text |
id | pubmed-3224585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32245852011-11-27 Predicting Bevirimat resistance of HIV-1 from genotype Heider, Dominik Verheyen, Jens Hoffmann, Daniel BMC Bioinformatics Research Article BACKGROUND: Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1. RESULTS: We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 ± 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the α-helix in p2, which hints at a possible resistance mechanism. CONCLUSIONS: We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease. BioMed Central 2010-01-20 /pmc/articles/PMC3224585/ /pubmed/20089140 http://dx.doi.org/10.1186/1471-2105-11-37 Text en Copyright ©2010 Heider et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Heider, Dominik Verheyen, Jens Hoffmann, Daniel Predicting Bevirimat resistance of HIV-1 from genotype |
title | Predicting Bevirimat resistance of HIV-1 from genotype |
title_full | Predicting Bevirimat resistance of HIV-1 from genotype |
title_fullStr | Predicting Bevirimat resistance of HIV-1 from genotype |
title_full_unstemmed | Predicting Bevirimat resistance of HIV-1 from genotype |
title_short | Predicting Bevirimat resistance of HIV-1 from genotype |
title_sort | predicting bevirimat resistance of hiv-1 from genotype |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224585/ https://www.ncbi.nlm.nih.gov/pubmed/20089140 http://dx.doi.org/10.1186/1471-2105-11-37 |
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