Information processing in biological molecular machines (1707.07496v7)

Abstract: Biological molecular machines are bifunctional enzymes that catalyze two processes: one donating free energy and the other accepting it. Recent studies show that most protein enzymes have rich stochastic dynamics of transitions between the multitude of conformation substates that make up their native state. This dynamics often manifests itself in fluctuating rates of the catalyzed processes and the presence of short-term memory. For such stochastic dynamics, after dividing the free energy into operational and organizational energy, we proved the generalized fluctuation theorem, which leads to the extension of the second law of thermodynamics to include two competing functions of process: dissipation and information. Computer simulation of the course of catalyzed processes taking place on the model network of substates, expressed in jumps of unit values at random moments of time, indicates the possibility of negative dissipation of the organizational free energy at the expense of information temporarily stored in memory, i.e. the behavior like Maxwell's demon. Because similar courses can be registered in observation of real systems, all theses of the paper are open to experimental verification.