The first step for enzyme catalysis to happen is enzyme-substrate binding. Three important parameters to characterize and quantify catalysis are the association (kon) and dissociation (koff) rate constants of enzyme-substrate complexes, which characterize the binding kinetics, and the Michaelis constant (Km), which indicates enzyme-substrate affinity.
However, kon, koff and Km values are usually measured in vitro, using low concentrations of ligand and protein in the experiments. Such condition does not reflect the cell environment, which is crowded with macromolecules such as proteins and lipids. Macromolecular crowding may affect binding thermodynamics and kinetics of enzyme-substrate complexes, leading to in vivo values for Km, kon and koff which differ from the ones measured in vitro. A crowded environment could, for instance, reduce ligand diffusion, leading to a kon value lower than the one determined in vitro.
The aim of this project is to develop and apply computational methods to understand how enzyme-substrate binding is affected by crowded cell-like environments.
However, kon, koff and Km values are usually measured in vitro, using low concentrations of ligand and protein in the experiments. Such condition does not reflect the cell environment, which is crowded with macromolecules such as proteins and lipids. Macromolecular crowding may affect binding thermodynamics and kinetics of enzyme-substrate complexes, leading to in vivo values for Km, kon and koff which differ from the ones measured in vitro. A crowded environment could, for instance, reduce ligand diffusion, leading to a kon value lower than the one determined in vitro.
The aim of this project is to develop and apply computational methods to understand how enzyme-substrate binding is affected by crowded cell-like environments.
Example of crowded environment, with protein crowders in blue and small molecules in orange.