Theoretical Investigation of the role of water in hydrophobic key-lock association kinetics

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The association of small molecules (ligands) with hydrophobic binding pockets plays an integral role in biochemical molecular recognition and function, as well as in various self-assembly processes in the physical chemistry of aqueous solutions. The binding process is typically governed by the so-called key-lock principle, in which the shape-complementary pocket and ligand associate due to intrinsic and water- mediated interactions. While the investigation of water contributions to the binding free energy (affinity) in equilibrium has attracted a great deal of attention in the last decade, almost nothing is known about the role of water in determining the rates of binding and kinetic mechanisms. For instance, what are the nanoscale hydrodynamic effects on ligand diffusion close to the hydrophobic docking site, and how are they controlled or even steered by the chemical composition of the pocket? We have recently shown by molecular simulations and diffusional descriptions of a simple prototypical pocket-ligand model that hydration fluctuations within the binding pocket can massively couple to the ligand dynamics and influence its binding rate. Since the hydration fluctuations in turn can be modified by the pocket geometry and polarity, the possibility opens up to create well-controlled hydrodynamic fluctuations which accelerate or decelerate approaching ligands to obtain a desired binding rate. In this proposal we want to explore such an appealing notion theoretically by using explicit-water molecular simulations and extended diffusion models on generic key-lock systems in aqueous solvent. The influence of the physicochemical and geometric properties of the pocket as well as the nature of the solvent on local diffusivities and binding rates will be studied. Anisotropic ligands will be investigated to study translational-rotational dynamical coupling. Non-Markovian (memory) effects shall be identified and cast into an implicit description by coupled stochastic diffusion models, in order to generate efficient coarse-grained models for rate prediction. The results of this project will add not only to our fundamental understanding of (bio)molecular binding processes but will be helpful for the design of functional host-guest systems as well as optimized drugs in biomedical applications.