Instantaneous generation of protein hydration properties from static structures

Ghanbarpour, Ahmadreza and Mahmoud, Amr H. and Lill, Markus A.. (2020) Instantaneous generation of protein hydration properties from static structures. Communications Chemistry, 3. p. 188.

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Official URL: https://edoc.unibas.ch/80333/

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Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.
Faculties and Departments:05 Faculty of Science > Departement Pharmazeutische Wissenschaften > Pharmazie > Computational Pharmacy (Lill)
UniBasel Contributors:Lill, Markus A.
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 10:04
Deposited On:14 Apr 2021 10:04

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