Recovering biological electron transfer reaction parameters from multiple protein film voltammetric techniques informed by Bayesian inference

Deciphering the mechanism, kinetics and energetics of biological electron-transfer reactions requires a robust, rapid reproducible protein-film voltammetry information recovery process. Here we describe semi-automated computational approach for inferring chemical reaction parameters simple protein system, bacterial cytochrome domain from Cellvibrio japonicus that displays reversible one-electron Fe2+/3+ redox chemistry. Despite relative simplicity experimental developing robust data analysis to find global optimum in 13-dimensional parameter space is challenging task because Faradaic-to-background current ratio such experiments often low. We how multiple-technique approach, whereby three techniques (direct-current, pure sinusoidal Fourier transform alternating voltammetry) combined, ultimately enables automatic extraction both (i) quantitative “best-fit” point values are across multiple performed on different protein-electrode films, (ii) statistical description correlation relationships, along with uncertainty individual values, obtained using Bayesian inference. It latter achievement which particularly important as it represents method visualising possible limitations mathematical model system. Our multi-voltammetry powerful insight complementarity between content, simulation-speed sensitivity current–time generated by techniques, illustrating value adding purely bioelectrochemistry measurement toolkit.