Model Systems for the Elucidation of Parameters Governing Electron Bifurcation

Yale University, New Haven CT

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Abstract

Project Summary/Abstract Electron bifurcation (EB) describes a process wherein an endergonic redox reaction is driven by the negative free energy change of a coupled exergonic redox reaction, limiting the free energy lost as heat. EB is emerging as a fundamental mechanism of biological energy conservation and is operative in the mitochondrial Q-cycle as well as in metabolic transformations in many anaerobic microorganisms including those in the human colon. Despite its importance in metabolism, fundamental mechanistic understanding of EB is still very limited. The aim of this proposal is to develop the first two classes of synthetic models that show EB, and to examine these models to gain insight into the kinetic and thermodynamic requirements for efficient bifurcation. First, we propose a simplified 3-component model system where electron transfer occurs independently of other chemical changes such as protonation. We describe how time-resolved spectroscopic monitoring will be used to observe EB, how relevant parameters of the bifurcating donor and two acceptors will be rationally tuned, and how rates of electron transfer will be modeled using Marcus Theory to understand how driving force and molecular structure affect EB. Results from this model system will inform the study of a second, more involved model system in which electron transfer is accompanied by proton transfer. This model will permit study of the role of proton-coupled electron transfer (PCET) in the EB process and may help to answer questions relating to biological gating of electron transfer in electron bifurcating enzymes. The results from this proposed work will contribute useful experimental evidence to help build a more complete understanding of the mechanistic underpinnings of EB. This research has potential future implications for the development of therapies to treat mitochondrial diseases and may be relevant in determining the distribution of microorganisms that comprises the human microbiome.