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Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

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Department of Chemistry, Columbia University, New York, New York 10027, United States
Schrödinger, Inc., 120 West 45th Street, New York, New York 10036, United States
Cite this: J. Chem. Theory Comput. 2016, 12, 6, 2990–2998
Publication Date (Web):May 4, 2016
https://doi.org/10.1021/acs.jctc.6b00201
Copyright © 2016 American Chemical Society

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    Abstract

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    Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein–ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand–receptor systems. We expect this approach to be of significant value in computationally driven drug design.

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    • Full set of initial poses generated by the IFD protocol used in the metadynamics part of our method (ZIP)

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