Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:5.99.1.2 (topoisomerase)
 9,166 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

In the structure of bovine F1-ATPase determined at 1.95-A resolution with crystals grown in the presence of ADP, 5'-adenylyl-imidodiphosphate, and azide, the azide anion interacts with the beta-phosphate of ADP and with residues in the ADP-binding catalytic subunit, betaDP. It occupies a position between the catalytically essential amino acids, beta-Lys-162 in the P loop and the "arginine finger" residue, alpha-Arg-373, similar to the site occupied by the gamma-phosphate in the ATP-binding subunit, betaTP. Its presence in the betaDP-subunit tightens the binding of the side chains to the nucleotide, enhancing its affinity and thereby stabilizing the state with bound ADP. This mechanism of inhibition appears to be common to many other ATPases, including ABC transporters, SecA, and DNA topoisomerase IIalpha. It also explains the stimulatory effect of azide on ATP-sensitive potassium channels by enhancing the binding of ADP.
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PMID:How azide inhibits ATP hydrolysis by the F-ATPases. 1672 6

Processive molecular motors, such as kinesin, myosin, or dynein, convert chemical energy into mechanical energy by hydrolyzing ATP. The mechanical energy is used for moving in discrete steps along the cytoskeleton and carrying a molecular load. Single-molecule recordings of motor position along a substrate polymer appear as a stochastic staircase. Recordings of other single molecules, such as F1-ATPase, RNA polymerase, or topoisomerase, have the same appearance. We present a maximum likelihood algorithm that extracts the dwell time sequence from noisy data, and estimates state transition probabilities and the distribution of the motor step size. The algorithm can handle models with uniform or alternating step sizes, and reversible or irreversible kinetics. A periodic Markov model describes the repetitive chemistry of the motor, and a Kalman filter allows one to include models with variable step size and to correct for baseline drift. The data are optimized recursively and globally over single or multiple data sets, making the results objective over the full scale of the data. Local binary algorithms, such as the t-test, do not represent the behavior of the whole data set. Our method is model-based, and allows rapid testing of different models by comparing the likelihood scores. From data obtained with current technology, steps as small as 8 nm can be resolved and analyzed with our method. The kinetic consequences of the extracted dwell sequence can be further analyzed in detail. We show results from analyzing simulated and experimental kinesin and myosin motor data. The algorithm is implemented in the free QuB software.
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PMID:Extracting dwell time sequences from processive molecular motor data. 1690 7

Extensive research within the last decade has revealed that most chronic illnesses such as cancer, cardiovascular and pulmonary diseases, neurological diseases, diabetes, and autoimmune diseases exhibit dysregulation of multiple cell signaling pathways that have been linked to inflammation. Thus mono-targeted therapies developed for the last two decades for these diseases have proven to be unsafe, ineffective and expensive. Although fruits and vegetables are regarded to have therapeutic potential against chronic illnesses, neither their active component nor the mechanism of action is well understood. Resveratrol (trans-3, 5, 4'-trihydroxystilbene), a component of grapes, berries, peanuts and other traditional medicines, is one such polyphenol that has been shown to mediate its effects through modulation of many different pathways. This stilbene has been shown to bind to numerous cell-signaling molecules such as multi drug resistance protein, topoisomerase II, aromatase, DNA polymerase, estrogen receptors, tubulin and F1-ATPase. Resveratrol has also been shown to activate various transcription factor (e.g; NFkappaB, STAT3, HIF-1alpha, beta-catenin and PPAR-gamma), suppress the expression of antiapoptotic gene products (e.g; Bcl-2, Bcl-X(L), XIAP and survivin), inhibit protein kinases (e.g; src, PI3K, JNK, and AKT), induce antioxidant enzymes (e,g; catalase, superoxide dismutase and hemoxygenase-1), suppress the expression of inflammatory biomarkers (e.g., TNF, COX-2, iNOS, and CRP), inhibit the expression of angiogenic and metastatic gene products (e.g., MMPs, VEGF, cathepsin D, and ICAM-1), and modulate cell cycle regulatory genes (e.g., p53, Rb, PTEN, cyclins and CDKs). Numerous animal studies have demonstrated that this polyphenol holds promise against numerous age-associated diseases including cancer, diabetes, Alzheimer, cardiovascular and pulmonary diseases. In view of these studies, resveratrol's prospects for use in the clinics are rapidly accelerating. Efforts are also underway to improve its activity in vivo through structural modification and reformulation. Our review describes various targets of resveratrol and their therapeutic potential.
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PMID:Resveratrol: a multitargeted agent for age-associated chronic diseases. 1841 53