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Acc Chem Res. 2020 Mar 17;53(3):654-661. doi: 10.1021/acs.accounts.9b00613. Epub 2020 Mar 5.

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations.

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Department of Chemistry and Institute of Structural and Molecular Biology, University College London, London WC1E 0AJ, United Kingdom.
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States.
EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh EH9 9FJ, United Kingdom.
Pharmaceutical Sciences, University of Geneva, Geneva 1211, Switzerland.


ConspectusThis Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult ("undruggable") targets. We also discuss the major challenges in the field and their possible solutions.

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