Parameter effects on binding chemistry in crowded media using a two-dimensional stochastic off-lattice model.
The intracellular environment imposes a variety of constraints on biochemical reaction systems that can substantially change reaction rates and equilibria relative to an ideal solution-based environment. One of the most notable features of the intracellular environment is its dense macromolecular crowding, which, among many other effects, tends to strongly enhance binding and assembly reactions. Despite extensive study of biochemistry in crowded media, it remains extremely difficult to predict how crowding will quantitatively affect any given reaction system due to the dependence of the crowding effect on numerous assumptions about the reactants and crowding agents involved. We previously developed a two dimensional stochastic off-lattice model of binding reactions based on the Green's function reaction dynamics method in order to create a versatile simulation environment in which one can explore interactions among many parameters of a crowded assembly system. In the present work, we examine interactions among several critical parameters for a model dimerization system: the total concentration of reactants and inert particles, the binding probability upon a collision between two reactant monomers, the mean time of dissociation reactions, and the diffusion coefficient of the system. Applying regression models to equilibrium constants across parameter ranges shows that the effect of the total concentration is approximately captured by a low-order nonlinear polynomial model, while the other three parameter effects are each accurately captured by a linear model. Furthermore, validation on tests with multi-parameter variations reveals that the effects of these parameters are separable from one another over a broad range of variation in all four parameters. The simulation work suggests that predictive models of crowding effects can accommodate a wider variety of parameter variations than prior theoretical models have so far achieved.
Lee
Byoungkoo
B
654 Mellon Institute, Carnegie Mellon/University of Pittsburgh Joint Program in Computational Biology, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA.
LeDuc
Philip R
PR
Schwartz
Russell
R
D004058:Diffusion; D019281:Dimerization; D005110:Extracellular Space / Q000737:chemistry / Q000378:metabolism; D007700:Kinetics; D008956:Models, Chemical; D012044:Regression Analysis; D013269:Stochastic Processes; D013816:Thermodynamics