Particle-based simulation reveals macromolecular crowding effects on the Michaelis-Menten mechanism

Many computational models for analyzing and predicting cell physiology rely on in vitro data, collected in dilute and cleanly controlled buffer solutions. However, this can mislead models because about 40% of the intracellular volume is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interact with enzymes and their reactants and affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new type of kinetics that captures and quantifies the effect of volume exclusion and any other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of this type of kinetics using particle simulations. Our formulation, entitled GEneralized Elementary Kinetics (GEEK), can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli , which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V max and the effective Michaelis-Menten constants K M under physiologically relevant volume occupancies. Finally, the effects of crowding in the context of a linear pathway were explored, with the finding that crowding can have a redistributing effect, relative to ideal conditions, on the effective flux responses in the case of two-fold enzyme overexpression. We suggest that the presented framework in combination with detailed kinetics models will improve our understanding of enzyme reaction networks under non-ideal conditions.