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Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

preprint
submitted on 15.02.2019 and posted on 15.02.2019 by Markus Lill, Ying Yang, Amr Mahmoud, Matthew Masters
Hydration is a key player in protein-ligand association. No computational method for modeling hydration has so far consistently improved the scoring performance of docking approaches. Using molecular dynamics on thousands of proteins in conjunction with modern deep learning approaches allowed the successful modeling of hydration during scoring of protein-ligand binding poses. This on-the-fly inclusion of hydration information resulted in unprecedented accuracy in binding pose prediction.
Big-data analytics based on relevance deduced from the trained neural network
revealed that the correct prediction of binding poses depends on three essential pillars of hydration, i.e. water-mediated interactions, desolvation, and enthalpically stable water layers around the bound ligand. The latter form of hydration may open new avenues for optimizing ligands for diverse protein targets.

Funding

Purdue Research Foundation

History

Email Address of Submitting Author

mlill@purdue.edu

Institution

Purdue University

Country

United States of America

ORCID For Submitting Author

0000-0003-3023-5188

Declaration of Conflict of Interest

no conflict of interest

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