Journal of molecular modeling, 2023-04, Vol.29 (4), p.102-102, Article 102
2023
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- Autor(en) / Beteiligte
- Titel
- Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation
- Ist Teil von
- Journal of molecular modeling, 2023-04, Vol.29 (4), p.102-102, Article 102
- Ort / Verlag
- Berlin/Heidelberg: Springer Berlin Heidelberg
- Erscheinungsjahr
- 2023
- Quelle
- SpringerLink
- Beschreibungen/Notizen
- Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔG bind energies for S6K1-Hit1 and S6K1-Hit2 were − 111.47 ± 1.29 and − 54.29 ± 1.19 kJ mol −1 , respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment. Graphical abstract
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1610-2940eISSN: 0948-5023DOI: 10.1007/s00894-023-05504-9Titel-ID: cdi_proquest_miscellaneous_2788798680
- Format
- –
- Schlagworte
- Bayes Theorem, Biodiversity, Characterization and Evaluation of Materials, Chemistry, Chemistry and Materials Science, Computer Appl. in Life Sciences, Computer Applications in Chemistry, Humans, Kinases, Lead compounds, Metabolic disorders, Molecular docking, Molecular Docking Simulation, Molecular dynamics, Molecular Dynamics Simulation, Molecular Medicine, Original Paper, Pharmacology, Quantitative Structure-Activity Relationship, Residues, Ribosomal Protein S6 Kinases, 70-kDa - antagonists & inhibitors, Screening, Simulation, Theoretical and Computational Chemistry, Three dimensional models