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Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance

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dc.contributor.author Masso, Majid
dc.contributor.author Vaisman, Iosif I.
dc.date.accessioned 2015-10-29T16:12:04Z
dc.date.available 2015-10-29T16:12:04Z
dc.date.issued 2013-10-01
dc.identifier.citation Masso and Vaisman: Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance. BMC Genomics 2013 14(Suppl 4):S3. en_US
dc.identifier.uri https://hdl.handle.net/1920/9999
dc.description.abstract Background Successful management of chronic human immunodeficiency virus type 1 (HIV-1) infection with a cocktail of antiretroviral medications can be negatively affected by the presence of drug resistant mutations in the viral targets. These targets include the HIV-1 protease (PR) and reverse transcriptase (RT) proteins, for which a number of inhibitors are available on the market and routinely prescribed. Protein mutational patterns are associated with varying degrees of resistance to their respective inhibitors, with extremes that can range from continued susceptibility to cross-resistance across all drugs. Results Here we implement statistical learning algorithms to develop structure- and sequence-based models for systematically predicting the effects of mutations in the PR and RT proteins on resistance to each of eight and eleven inhibitors, respectively. Employing a four-body statistical potential, mutant proteins are represented as feature vectors whose components quantify relative environmental perturbations at amino acid residue positions in the respective target structures upon mutation. Two approaches are implemented in developing sequence-based models, based on use of either relative frequencies or counts of n-grams, to generate vectors for representing mutant proteins. To the best of our knowledge, this is the first reported study on structure- and sequence-based predictive models of HIV-1 PR and RT drug resistance developed by implementing a four-body statistical potential and n-grams, respectively, to generate mutant attribute vectors. Performance of the learning methods is evaluated on the basis of tenfold cross-validation, using previously assayed and publicly available in vitro data relating mutational patterns in the targets to quantified inhibitor susceptibility changes. Conclusion Overall performance results are competitive with those of a previously published study utilizing a sequence-based strategy, while our structure- and sequence-based models provide orthogonal and complementary prediction methodologies, respectively. In a novel application, we describe a technique for identifying every possible pair of RT inhibitors as either potentially effective together as part of a cocktail, or a combination that is to be avoided.
dc.description.sponsorship Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject HIV-1 en_US
dc.subject protease en_US
dc.subject reverse transcriptase en_US
dc.subject protein mutational patterns en_US
dc.subject sequence-based models en_US
dc.subject structure-based models en_US
dc.title Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance en_US
dc.type Article en_US
dc.identifier.doi http://dx.doi.org/10.1186/1471-2164-14-S4-S3


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