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• W2022543307 endingPage "1982" @default.
• W2022543307 startingPage "1969" @default.
• W2022543307 abstract "It is now well established that most if not all enzymatic proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. A hypothesis is stated that the protein conformational transition networks, as just as higher-level biological networks, the protein interaction network, and the metabolic network, have evolved in the process of self-organized criticality. Here, the criticality means that all the three classes of networks are scale-free and, moreover, display a transition from the fractal organization on a small length-scale to the small-world organization on the large length-scale. Good mathematical models of such networks are stochastic critical branching trees extended by long-range shortcuts. Biological molecular machines are proteins that operate under isothermal conditions and hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating (input) reaction and the free energy-accepting (output) one. The far-from-equilibrium degree of coupling between the output and the input reaction fluxes have been studied both theoretically and by means of the Monte Carlo simulations on model networks. For single input and output gates the degree of coupling cannot exceed unity. Study simulations of random walks on model networks involving more extended gates indicate that the case of the degree of coupling value higher than one is realized on the mentioned above critical branching trees extended by long-range shortcuts." @default.
• W2022543307 created "2016-06-24" @default.
• W2022543307 creator A5058524818 @default.
• W2022543307 creator A5063513392 @default.
• W2022543307 date "2014-04-01" @default.
• W2022543307 modified "2023-09-24" @default.
• W2022543307 title "Stochastic Dynamics of Proteins and the Action of Biological Molecular Machines" @default.
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• W2022543307 doi "https://doi.org/10.3390/e16041969" @default.
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