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HIV-1 reverse transcriptase and antiviral drug resistance. Part 1.

ABSTRACT: HIV-1 reverse transcriptase (RT) contributes to the development of resistance to all anti-AIDS drugs by introducing mutations into the viral genome. At the molecular level, mutations in RT result in resistance to RT inhibitors. Eight nucleoside/nucleotide analogs (NRTIs) and five non-nucleoside inhibitors (NNRTIs) are approved HIV-1 drugs. Structures of RT have been determined in complexes with substrates and/or inhibitors, and the structures have illuminated different conformational and functional states of the enzyme. Understanding the molecular mechanisms of resistance to NRTIs and NNRTIs, and their complex relationships, may help in designing new drugs that are periodically required to overcome existing as well as emerging trends of drug resistance.

SUBMITTER: Das K

PROVIDER: S-EPMC4097814 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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HIV-1 reverse transcriptase and antiviral drug resistance. Part 1.

Das Kalyan K Arnold Eddy E

Current opinion in virology 20130419 2

HIV-1 reverse transcriptase (RT) contributes to the development of resistance to all anti-AIDS drugs by introducing mutations into the viral genome. At the molecular level, mutations in RT result in resistance to RT inhibitors. Eight nucleoside/nucleotide analogs (NRTIs) and five non-nucleoside inhibitors (NNRTIs) are approved HIV-1 drugs. Structures of RT have been determined in complexes with substrates and/or inhibitors, and the structures have illuminated different conformational and functio ...[more]

PMID: 23602471

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Project description:BackgroundSuccessful 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.ResultsHere 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.ConclusionOverall 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.

Dataset Information

HIV-1 reverse transcriptase and antiviral drug resistance. Part 1.

Publications

HIV-1 reverse transcriptase and antiviral drug resistance. Part 1.

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