Abstract

Charge transfer through biological macromolecules is essential for many biological processes such as for instance photosynthesis and respiration. In these processes, protons or electrons are transferred between titratable residues or redox-active cofactors, respectively. Often their transfer is tightly coupled. Computational methods based on continuum electrostatics are widely used in theoretical biochemistry to analyze the function of even very complex biochemical systems. These methods allow one to consider the pH and the redox potential of the solution as well as explicitly considering membrane potentials in the calculations. Combining continuum electrostatic calculations with a statistical thermodynamic analysis, it is possible to calculate equilibrium parameters such as protonation or oxidation probabilities. Moreover, it is also possible to simulate reaction kinetics by using parameters calculated from continuum electrostatics. One needs to consider that the transfer rate between two sites depends on the current charge configuration of neighboring sites. We formulate the kinetics of charge transfer systems in a microstate formalism. A unique transfer rate constant can be assigned to the interconversion of microstates. Mutual interactions between sites participating in the transfer reactions are naturally taken into account. This formalism is applied to the kinetics of electron transfer in the tetraheme-subunit and the special pair of the reaction center of Blastochloris viridis. It is shown that continuum electrostatic calculations can be used in combination with an existing rate law to obtain electron transfer rate constants. The relaxation electron transfer kinetics after photo-oxidation of the special pair of photosynthetic reaction center is simulated by a microstate formalism and it is shown to be in good agreement with experimental data. A flux analysis is used to follow the individual electron transfer steps. This method of simulating the complex kinetics of biomolecules based on structural data is a first step on the way from structural biology to systems biology.

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