Markov-State Transition Path Analysis of Electrostatic Channeling

Electrostatic channeling is a naturally occurring approach to control the flux of charged intermediates in catalytic cascades. Computational techniques have enabled quantitative understanding of such mechanisms, augmenting experimental approaches by modeling molecular interactions in atomic detail. In this work, we report the first utilization of a Markov-state model (MSM) to describe the surface diffusion of a reaction intermediate, glucose 6-phosphate, on an artificially modified cascade where hexokinase and glucose-6-phosphate dehydrogenase are covalently conjugated by a cationic oligopeptide bridge. Conformation space networks are used to represent intermediate transport on enzyme surfaces, along with committor probabilities that assess the desorption probability of the intermediate on each segment of the channeling pathway. For the region between the peptide bridge and downstream active site, the ionic strength dependence of desorption probability by MSM agreed well with that by transition state theory. A kinetic Monte Carlo model integrates parameters from different computational methods to evaluate the contribution of desorption during each step. The approach is validated by calculation of kinetic lag time, which agrees well with experimental results. These results further demonstrate the applicability of molecular simulations and advanced sampling techniques to the design of chemical networks.