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-...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/8/7360 |
| _version_ | 1797605078784278528 |
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| author | Zixuan Cheng Mrinal Bhave Siaw San Hwang Taufiq Rahman Xavier Wezen Chee |
| author_facet | Zixuan Cheng Mrinal Bhave Siaw San Hwang Taufiq Rahman Xavier Wezen Chee |
| author_sort | Zixuan Cheng |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T04:56:04Z |
| format | Article |
| id | doaj.art-624cfb1db50147afa7ee4ab8974aa92e |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T04:56:04Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-624cfb1db50147afa7ee4ab8974aa92e2023-11-17T19:39:17ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-04-01248736010.3390/ijms24087360Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation StudiesZixuan Cheng0Mrinal Bhave1Siaw San Hwang2Taufiq Rahman3Xavier Wezen Chee4School of Engineering and Science, Swinburne University of Technology Sarawak, Kuching 93350, MalaysiaDepartment of Chemistry and Biotechnology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaSchool of Engineering and Science, Swinburne University of Technology Sarawak, Kuching 93350, MalaysiaDepartment of Pharmacology, University of Cambridge, Cambridge CB2 1PD, UKSchool of Engineering and Science, Swinburne University of Technology Sarawak, Kuching 93350, MalaysiaProtein 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.https://www.mdpi.com/1422-0067/24/8/7360p38γQSAR modellingvirtual screeningmolecular dynamic simulationsbinding interaction |
| spellingShingle | Zixuan Cheng Mrinal Bhave Siaw San Hwang Taufiq Rahman Xavier Wezen Chee Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics Simulation Studies International Journal of Molecular Sciences p38γ QSAR modelling virtual screening molecular dynamic simulations binding interaction |
| 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 | p38γ QSAR modelling virtual screening molecular dynamic simulations binding interaction |
| url | https://www.mdpi.com/1422-0067/24/8/7360 |
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