Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.7.49 (reverse transcriptase)
 31,746 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Long-term alterations to body metabolism have become apparent with the prolonged use of antiretroviral nucleoside analogue reverse transcriptase inhibitors (NRTIs). The NRTIs differ in the mechanisms, potency and probably also tissue specificity of mitochondrial toxicity. One group of NRTIs, the so-called "d-drugs" (zalcitabine> didanosine>stavudine) are relatively strong inhibitors of g-polymerase and thus cause a time- and dose-dependent decrease in the intracellular levels of mitochondrial DNA (mtDNA). The most important target organs of d-drugs are the liver, skeletal muscle, peripheral nerves and probably also the subcutaneous adipose tissue of lipoatrophic subjects. Hyperlactataemia may be observed. Zidovudine is an inhibitor of the mitochondrial adenine nucleotide translocator, binds to adenylate kinase and may also be converted into stavudine triphosphate in vivo. Persistent hyperlactataemia, mtDNA depletion and isolated cases of mitochondrial encephalomyopathies have been observed in babies under perinatal exposure with zidovudine. Nucleotide analogues such as tenofovir are avidly taken up into renal tubular epithelia. Isolated cases of renal failure and Fanconi syndrome require further investigation. Mitochondrial toxicity cannot yet be adequately monitored and predicted. Drugs with potential additive or synergistic toxicity, such as valproate, should be used with caution. Didanosine interacts with allopurinol, hydroxyurea and ribavirin. In established mitochondrial toxicity, cessation of the offending NRTI remains the most effective therapeutic intervention because vitamin cocktails and l-carnitine have, at best, only a marginal effect.
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PMID:Update on mitochondrial toxicity: where are we now? 1283 62

Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.
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PMID:ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution. 2749 96