Evolution of drug resistance in HIV protease
Abstract Background Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mut...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-020-03825-7 |
| _version_ | 1819299032217944064 |
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| author | Dhara Shah Christopher Freas Irene T. Weber Robert W. Harrison |
| author_facet | Dhara Shah Christopher Freas Irene T. Weber Robert W. Harrison |
| author_sort | Dhara Shah |
| collection | DOAJ |
| description | Abstract Background Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype–phenotype data. Results The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. Conclusions Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems. |
| first_indexed | 2024-12-24T05:39:20Z |
| format | Article |
| id | doaj.art-eb952b8310854621927508de276c57f8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-24T05:39:20Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-eb952b8310854621927508de276c57f82022-12-21T17:12:51ZengBMCBMC Bioinformatics1471-21052020-12-0121S1811510.1186/s12859-020-03825-7Evolution of drug resistance in HIV proteaseDhara Shah0Christopher Freas1Irene T. Weber2Robert W. Harrison3Department of Computer ScienceDepartment of Computer ScienceDepartment of BiologyDepartment of Computer ScienceAbstract Background Drug resistance is a critical problem limiting effective antiviral therapy for HIV/AIDS. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to identify protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Few studies, however, have assessed the evolution of resistance from genotype–phenotype data. Results The machine learning produced highly accurate and robust classification of resistance to HIV protease inhibitors. Genotype data were mapped to the enzyme structure and encoded using Delaunay triangulation. Estimates of evolutionary relationships, based on this encoding, and using Minimum Spanning Trees, showed clusters of mutations that closely resemble the wild type. These clusters appear to evolve uniquely to more resistant phenotypes. Conclusions Using the triangulation metric and spanning trees results in paths that are consistent with evolutionary theory. The majority of the paths show bifurcation, namely they switch once from non-resistant to resistant or from resistant to non-resistant. Paths that lose resistance almost uniformly have far lower levels of resistance than those which either gain resistance or are stable. This strongly suggests that selection for stability in the face of a rapid rate of mutation is as important as selection for resistance in retroviral systems.https://doi.org/10.1186/s12859-020-03825-7HIV proteaseDrug resistanceMachine learningEvolutionStructure-based |
| spellingShingle | Dhara Shah Christopher Freas Irene T. Weber Robert W. Harrison Evolution of drug resistance in HIV protease BMC Bioinformatics HIV protease Drug resistance Machine learning Evolution Structure-based |
| title | Evolution of drug resistance in HIV protease |
| title_full | Evolution of drug resistance in HIV protease |
| title_fullStr | Evolution of drug resistance in HIV protease |
| title_full_unstemmed | Evolution of drug resistance in HIV protease |
| title_short | Evolution of drug resistance in HIV protease |
| title_sort | evolution of drug resistance in hiv protease |
| topic | HIV protease Drug resistance Machine learning Evolution Structure-based |
| url | https://doi.org/10.1186/s12859-020-03825-7 |
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