Designing multi-site charge-bifurcation networks in de novo proteins: a kinetic, statistical, and machine-learning approach

Abstract

Electron bifurcation reactions separate electron pairs into high and low potential pools, and these reactions are central to the bioenergetics of living systems. Here, we used kinetic analysis and machine learning to analyze a diverse set of structural and electrochemical landscapes that may guide the design of molecular architectures that could serve as experimental targets that would function to bifurcate holes using light. We find that strong electrostatic repulsion between the holes enhances the quantum yield for bifurcation but reduces the energy efficiency of the process. We find that the quantum yield for hole bifurcation is enhanced by positioning the hot-hole pathway cofactor farther from the hole bifurcation site than its cold-hole pathway counterpart. This integrated design and optimization approach provides design strategies for de novo structures that could realize light-drive hole bifurcation, advancing the aim of employing bioinspired electron bifurcation for energy conversion, photocatalysis, and electrocatalysis. Beyond the specific light-driven hole-bifurcation architecture, our combined kinetic–statistical–machine-learning approach is transferable to other multi-particle, multi-site charge-transport network design challenges, opening paths for designing photochemical and catalytic networks, as well as for designing functional redox networks.