EF-G-dependent GTPase on the ribosome. conformational change and fusidic acid inhibition

Protein synthesis studies increasingly focus on delineating the nature of conformational changes occurring as the ribosome exerts its catalytic functions. Here, we use FRET to examine such changes during single-turnover EF-G-dependent GTPase on vacant ribosomes and to elucidate the mechanism by which fusidic acid (FA) inhibits multiple-turnover EF-G.GTPase. Our measurements focus on the distance between the G' region of EF-G and the N-terminal region of L11 (L11-NTD), located within the GTPase activation center of the ribosome. We demonstrate that single-turnover ribosome-dependent EF-G GTPase proceeds according to a kinetic scheme in which rapid G' to L11-NTD movement requires prior GTP hydrolysis and, via branching pathways, either precedes P(i) release (major pathway) or occurs simultaneously with it (minor pathway). Such movement retards P(i) release, with the result that P(i) release is essentially rate-determining in single-turnover GTPase. This is the most significant difference between the EF-G.GTPase activities of vacant and translocating ribosomes [Savelsbergh, A., Katunin, V. I., Mohr, D., Peske, F., Rodnina, M. V., and Wintermeyer, W. (2003) Mol. Cell 11, 1517-1523], which are otherwise quite similar. Both the G' to L11-NTD movement and P(i) release are strongly inhibited by thiostrepton but not by FA. Contrary to the standard view that FA permits only a single round of GTP hydrolysis [Bodley, J. W., Zieve, F. J., and Lin, L. (1970) J. Biol. Chem. 245, 5662-5667], we find that FA functions rather as a slow inhibitor of EF-G.GTPase, permitting a number of GTPase turnovers prior to complete inhibition while inducing a closer approach of EF-G to the GAC than is seen during normal turnover.     

Delineating Novel Therapeutic Drug and Vaccine Targets for Staphylococcus cornubiensis NW1T Through Computational Analysis

International Journal of Peptide Research and Therapeutics - Staphylococcal multidrug resistance is an emerging future threat. Among staphylococci species, Staphylococcus intermedius group (SIG)...     

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Background

Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO₂) from petroleum consumption. Limitations of the previous methods of predicting CO₂ emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO₂ emissions from petroleum consumption have motivated this research.

Methods/Findings

The OPEC CO₂ emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO₂ emissions. The proposed model predicts OPEC CO₂ emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.

Conclusion

An accurate prediction of OPEC CO₂ emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO₂ emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.