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Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies

Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-...

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Detalles Bibliográficos
Autores principales: Cheng, Zixuan, Bhave, Mrinal, Hwang, Siaw San, Rahman, Taufiq, Chee, Xavier Wezen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139033/
https://www.ncbi.nlm.nih.gov/pubmed/37108523
http://dx.doi.org/10.3390/ijms24087360
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author Cheng, Zixuan
Bhave, Mrinal
Hwang, Siaw San
Rahman, Taufiq
Chee, Xavier Wezen
author_facet Cheng, Zixuan
Bhave, Mrinal
Hwang, Siaw San
Rahman, Taufiq
Chee, Xavier Wezen
author_sort Cheng, Zixuan
collection PubMed
description Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer.
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spelling pubmed-101390332023-04-28 Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies Cheng, Zixuan Bhave, Mrinal Hwang, Siaw San Rahman, Taufiq Chee, Xavier Wezen Int J Mol Sci Article Protein kinase p38γ is an attractive target against cancer because it plays a pivotal role in cancer cell proliferation by phosphorylating the retinoblastoma tumour suppressor protein. Therefore, inhibition of p38γ with active small molecules represents an attractive alternative for developing anti-cancer drugs. In this work, we present a rigorous and systematic virtual screening framework to identify potential p38γ inhibitors against cancer. We combined the use of machine learning-based quantitative structure activity relationship modelling with conventional computer-aided drug discovery techniques, namely molecular docking and ligand-based methods, to identify potential p38γ inhibitors. The hit compounds were filtered using negative design techniques and then assessed for their binding stability with p38γ through molecular dynamics simulations. To this end, we identified a promising compound that inhibits p38γ activity at nanomolar concentrations and hepatocellular carcinoma cell growth in vitro in the low micromolar range. This hit compound could serve as a potential scaffold for further development of a potent p38γ inhibitor against cancer. MDPI 2023-04-17 /pmc/articles/PMC10139033/ /pubmed/37108523 http://dx.doi.org/10.3390/ijms24087360 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cheng, Zixuan
Bhave, Mrinal
Hwang, Siaw San
Rahman, Taufiq
Chee, Xavier Wezen
Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title_full Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title_fullStr Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title_full_unstemmed Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title_short Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies
title_sort identification of potential p38γ inhibitors via in silico screening, in vitro bioassay and molecular dynamics simulation studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139033/
https://www.ncbi.nlm.nih.gov/pubmed/37108523
http://dx.doi.org/10.3390/ijms24087360
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