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Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning
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 sim...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814895/ https://www.ncbi.nlm.nih.gov/pubmed/36703428 http://dx.doi.org/10.1038/s42004-020-0261-x |
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author | Mahmoud, Amr H. Masters, Matthew R. Yang, Ying Lill, Markus A. |
author_facet | Mahmoud, Amr H. Masters, Matthew R. Yang, Ying Lill, Markus A. |
author_sort | Mahmoud, Amr H. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9814895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98148952023-01-10 Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning Mahmoud, Amr H. Masters, Matthew R. Yang, Ying Lill, Markus A. Commun Chem Article 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. Nature Publishing Group UK 2020-02-11 /pmc/articles/PMC9814895/ /pubmed/36703428 http://dx.doi.org/10.1038/s42004-020-0261-x Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mahmoud, Amr H. Masters, Matthew R. Yang, Ying Lill, Markus A. Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title | Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title_full | Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title_fullStr | Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title_full_unstemmed | Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title_short | Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
title_sort | elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814895/ https://www.ncbi.nlm.nih.gov/pubmed/36703428 http://dx.doi.org/10.1038/s42004-020-0261-x |
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