In this talk, I will be presenting new methodologies based on MD simulations and related methods to investigate dynamically protein-ligand binding. In particular, I’ll be presenting two approaches based on two different enhanced sampling methods: i) electrostatic-driven steered MD, which provides reliable binding modes in a few tens of simulations; ii) a second approach, which is independent of any reaction coordinate and is based on scaled MD simulations confined by a cylinder built from the protein binding pocket to the bulk of the solvent. Machine learning-based analysis allows then to determine highly populated clusters along the binding pathway, and subsequent umbrella sampling simulations allow the construction of the minimum free energy binding path. From the minimum free energy path, we can eventually determine the thermodynamics and kinetics associated with the drug-target binding. The second part of the presentation will deal with smoothed potential MD simulations for dynamic undocking and residence time prediction, particularly focusing on real-life drug discovery projects.<br/><br/>

Dynamic docking to investigate thermodynamics and kinetics of drug-target binding

2019 Fall National Meeting

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