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Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR
Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding mo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092126/ https://www.ncbi.nlm.nih.gov/pubmed/25010341 http://dx.doi.org/10.1371/journal.pone.0102212 |
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author | Quesada-Romero, Luisa Mena-Ulecia, Karel Tiznado, William Caballero, Julio |
author_facet | Quesada-Romero, Luisa Mena-Ulecia, Karel Tiznado, William Caballero, Julio |
author_sort | Quesada-Romero, Luisa |
collection | PubMed |
description | Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. |
format | Online Article Text |
id | pubmed-4092126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-40921262014-07-18 Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR Quesada-Romero, Luisa Mena-Ulecia, Karel Tiznado, William Caballero, Julio PLoS One Research Article Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q(2) value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors. Public Library of Science 2014-07-10 /pmc/articles/PMC4092126/ /pubmed/25010341 http://dx.doi.org/10.1371/journal.pone.0102212 Text en © 2014 Quesada-Romero et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Quesada-Romero, Luisa Mena-Ulecia, Karel Tiznado, William Caballero, Julio Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title | Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title_full | Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title_fullStr | Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title_full_unstemmed | Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title_short | Insights into the Interactions between Maleimide Derivates and GSK3β Combining Molecular Docking and QSAR |
title_sort | insights into the interactions between maleimide derivates and gsk3β combining molecular docking and qsar |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4092126/ https://www.ncbi.nlm.nih.gov/pubmed/25010341 http://dx.doi.org/10.1371/journal.pone.0102212 |
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