Dataset Information

Crystal engineering of HIV-1 reverse transcriptase for structure-based drug design.

ABSTRACT: HIV-1 reverse transcriptase (RT) is a primary target for anti-AIDS drugs. Structures of HIV-1 RT, usually determined at approximately 2.5-3.0 A resolution, are important for understanding enzyme function and mechanisms of drug resistance in addition to being helpful in the design of RT inhibitors. Despite hundreds of attempts, it was not possible to obtain the structure of a complex of HIV-1 RT with TMC278, a nonnucleoside RT inhibitor (NNRTI) in advanced clinical trials. A systematic and iterative protein crystal engineering approach was developed to optimize RT for obtaining crystals in complexes with TMC278 and other NNRTIs that diffract X-rays to 1.8 A resolution. Another form of engineered RT was optimized to produce a high-resolution apo-RT crystal form, reported here at 1.85 A resolution, with a distinct RT conformation. Engineered RTs were mutagenized using a new, flexible and cost effective method called methylated overlap-extension ligation independent cloning. Our analysis suggests that reducing the solvent content, increasing lattice contacts, and stabilizing the internal low-energy conformations of RT are critical for the growth of crystals that diffract to high resolution. The new RTs enable rapid crystallization and yield high-resolution structures that are useful in designing/developing new anti-AIDS drugs.

SUBMITTER: Bauman JD

PROVIDER: S-EPMC2528191 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Crystal engineering of HIV-1 reverse transcriptase for structure-based drug design.

Bauman Joseph D JD Das Kalyan K Ho William C WC Baweja Mukta M Himmel Daniel M DM Clark Arthur D AD Oren Deena A DA Boyer Paul L PL Hughes Stephen H SH Shatkin Aaron J AJ Arnold Eddy E

Nucleic acids research 20080801 15

HIV-1 reverse transcriptase (RT) is a primary target for anti-AIDS drugs. Structures of HIV-1 RT, usually determined at approximately 2.5-3.0 A resolution, are important for understanding enzyme function and mechanisms of drug resistance in addition to being helpful in the design of RT inhibitors. Despite hundreds of attempts, it was not possible to obtain the structure of a complex of HIV-1 RT with TMC278, a nonnucleoside RT inhibitor (NNRTI) in advanced clinical trials. A systematic and iterat ...[more]

PMID: 18676450

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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

Crystal engineering of HIV-1 reverse transcriptase for structure-based drug design.

Publications

Crystal engineering of HIV-1 reverse transcriptase for structure-based drug design.

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