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

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, [2000-

Antineoplastic Agents*/pharmacology ; Molecular Dynamics Simulation*; Biological Assay ; Drug Discovery ; Ligands ; Molecular Docking Simulation ; Quantitative Structure-Activity Relationship ; Mitogen-Activated Protein Kinase 12/metabolism

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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Keywords: QSAR modelling; binding interaction; molecular dynamic simulations; p38γ; virtual screening

0 (Antineoplastic Agents)
0 (Ligands)
EC 2.7.1.- (Mitogen-Activated Protein Kinase 12)

Date Created: 20230428 Date Completed: 20230502 Latest Revision: 20230502

20250114

PMC10139033

10.3390/ijms24087360

37108523