We describe a general microscopic model for the calculation of diffusion rates ofsmall ligands (e.g. gas molecules) in proteins^1. In our model, the diffusive hopping of theligand within the protein is coarse grained using a master equation approach with transitionrates between the coarse states (= protein cavities) estimated from equilibrium andnon-equilibrium pulling MD simulations. The master equation is propagated to obtain theligand population of the active site as a function of time. A fit of the time-dependentpopulations to a phenomenological rate law gives the phenomenological diffusion rate constants that can be compared to experimental measurements. We report the application of this methodology to a number of enzymes that have recently attracted much interest for biotechnological applications such as H2 oxidation and production in biofuel cells ([NiFe]-hydrogenase)^1,2 as well as CO2 reduction and production of biomass (Carbon-monoxide dehydrogenase/Acetyl co-enzyme A synthase (CODH/ACS))^3. We find that experimentally determined diffusion rates of inhibitors in wild-type^1 and mutant^2 hydrogenases could be well reproduced with our method. Moreover, our simulations could explain how mutations impact on inhibitor diffusion^2. We will show in our presentation that the method developed can give unprecedented insight into the structural features that are built into enzymes to guide the directed flow of ligands into and between active sites. We will also demonstrate that incorporation of protein dynamical effects is crucial for a more complete understanding of small ligand transport within and between proteins.[1] P. Wang, R. B. Best, J. Blumberger, J. Am. Chem. Soc. 133, 3548 (2011).[2] P. Wang, J. Blumberger, Proc. Natl. Acad. Sci. USA , 109, 6399 (2012).[3] P. Wang et al., manuscript in preparation.

Multiscale computational model for small ligand diffusion within proteins with application to hydrogenase and carbon monoxide dehyrogenase

245th National Meeting (2013)

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