Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Mahmoud, Amr H. and Masters, Matthew R. and Yang, Ying and Lill, Markus A.. (2020) Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning. Communications Chemistry, 3. p. 19.

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Official URL: https://edoc.unibas.ch/80334/

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Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design.
Faculties and Departments:05 Faculty of Science > Departement Pharmazeutische Wissenschaften > Pharmazie > Computational Pharmacy (Lill)
UniBasel Contributors:Lill, Markus A. and Abdallah, Amr
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature Publishing Group
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Apr 2021 10:02
Deposited On:14 Apr 2021 10:02

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