Molecular binding events are a fundamental aspect of cellular processes and are therefore of particular interest for the development of novel therapeutics. Efficient prediction and ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (ΔG) properties can be valuable metrics for drug lead optimization, as these quantities are often indicators of in vivo efficacy. Simulation based approaches that can efficiently estimate these quantities are desirable because they can provide critical detail at the molecular level and offer opportunities for lead optimization. We present recent developments to our multiscale molecular dynamics, Brownian dynamics, and milestoning approach for estimating protein-ligand association and dissociation rates as well as binding thermodynamics, called “Simulation Enabled Estimation of Kinetic Rates" (SEEKR). This approach reduces the compute time required to simulate transition events, is embarrassingly parallel, and is agnostic to the simulation modality used. This allows us to use atomically detailed, yet computationally expensive, fully flexible MD simulations in milestones near the binding site where these interactions are critical for understanding the binding and unbinding, and BD simulations far from the binding site where rigid body dynamics provides a sufficient description at significantly reduced computational cost. We describe the effectiveness of the approach for rank-ordering ligands by dissociation rates and binding free energies for a model host-guest system, as well as the successful calculation of kinetics for protein-ligand systems. We also describe improvements focused on making such calculations feasible for larger systems of pharmaceutical relevance.

Computational predictions of drug binding kinetics with a multiscale molecular dynamics, Brownian dynamics, and milestoning approach

2019 Fall National Meeting

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