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Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors
Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology wi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517831/ https://www.ncbi.nlm.nih.gov/pubmed/33041806 http://dx.doi.org/10.3389/fphar.2020.566058 |
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author | Zhu, Jingyu Wu, Yuanqing Wang, Man Li, Kan Xu, Lei Chen, Yun Cai, Yanfei Jin, Jian |
author_facet | Zhu, Jingyu Wu, Yuanqing Wang, Man Li, Kan Xu, Lei Chen, Yun Cai, Yanfei Jin, Jian |
author_sort | Zhu, Jingyu |
collection | PubMed |
description | Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors. |
format | Online Article Text |
id | pubmed-7517831 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75178312020-10-09 Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors Zhu, Jingyu Wu, Yuanqing Wang, Man Li, Kan Xu, Lei Chen, Yun Cai, Yanfei Jin, Jian Front Pharmacol Pharmacology Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors. Frontiers Media S.A. 2020-09-11 /pmc/articles/PMC7517831/ /pubmed/33041806 http://dx.doi.org/10.3389/fphar.2020.566058 Text en Copyright © 2020 Zhu, Wu, Wang, Li, Xu, Chen, Cai and Jin http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Zhu, Jingyu Wu, Yuanqing Wang, Man Li, Kan Xu, Lei Chen, Yun Cai, Yanfei Jin, Jian Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title | Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title_full | Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title_fullStr | Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title_full_unstemmed | Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title_short | Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors |
title_sort | integrating machine learning-based virtual screening with multiple protein structures and bio-assay evaluation for discovery of novel gsk3β inhibitors |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517831/ https://www.ncbi.nlm.nih.gov/pubmed/33041806 http://dx.doi.org/10.3389/fphar.2020.566058 |
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