Finite Difference Model of Substrate Channeling in TCA Cycle Enzymes

Abstract

The Tricarboxylic Acid (TCA) cycle is a highly optimized metabolic pathway. Of particular interest in the TCA cycle is the malate dehydrogenase—citrate synthase (MDH-CS) complex, which accomplishes citrate production from malate with an oxaloacetate intermediate. This complex was studied experimentally by Bulutoglu et al, who showed that mutation of positively-charged residues on the complex surface effected the dynamic response of citrate production as measured by lag time experiments.1 More recently, we have created a Markov State model that was used to predict the transfer efficiency of each complex and determine the reaction pathways on the surface.2 Utilizing the kinetic parameters of both the recombinant and mutant complexes determined experimentally1and Markov state transition matrix produced by previous modeling2, we report a finite difference model to calculate time trajectories of the surface and bulk occupancies by OAA. Solution of a system of differential mass balance equations, based on the transient matrix, provide the occupancy probability at each node in the network. The lag time of MDH-CS was determined computationally to be comparable to experiment for both the recombinant and mutant complex with lag times of 0.44 and 2.1 seconds, respectively.1 Using the model, the dynamics of each of the four possible reaction pathways between the the two source (MDH) active sites and the two sink (CS) sites could be studied independently. This analysis provides a dynamic model for intermediate transport in an electrostatically channeled system, and can be used as a predictive tool to provide mechanistic insight into pathway dominance. We gratefully acknowledge support from the Army Research Office MURI (#W911NF1410263) via The University of Utah. References B. Bulutoglu, K. E. Garcia, F. Wu, S. D. Minteer, and S. Banta, ACS Chemical Biology, 11, 2847–53 (2016). doi:10.1021/acschembio.6b00523Y. Xie, S. D. Minteer, S. Banta, and S. Calabrese Barton, ACS Nanoscience Au, 2, 414-421 (2022). doi:10.1021/acsnanoscienceau.2c00011 Figure 1