Skip to main content

Advertisement

SpringerLink
Log in

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

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 ΔGbind 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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The data and materials are available from the corresponding authors on reasonable request.

References

  1. Fenton TR et al (2011) Functions and regulation of the 70 kDa ribosomal S6 kinases. Int J Biochem Cell B 43:47–59. https://doi.org/10.1016/j.biocel.2010.09.018

    Article  CAS  Google Scholar 

  2. Shamji AF et al (2003) Integration of growth factor and nutrient signaling: implications for cancer biology. Mol Cell 12:271–280. https://doi.org/10.1016/j.molcel.2003.08.016

    Article  CAS  PubMed  Google Scholar 

  3. Magnuson B et al (2012) Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks. Biochem J 441:1–21. https://doi.org/10.1042/BJ20110892

    Article  CAS  PubMed  Google Scholar 

  4. Couty S et al (2013) The discovery of potent ribosomal S6 kinase inhibitors by high-throughput screening and structure-guided drug design. Oncotarget 4:1647. https://doi.org/10.18632/oncotarget.1255

    Article  PubMed  PubMed Central  Google Scholar 

  5. Zaiets I et al. (2018) The p60-S6K1 isoform of ribosomal protein S6 kinase 1 is a product of alternative mRNA translation, UBJ 25–35, https://doi.org/10.15407/ubj90.04.025

  6. Yin Y et al (2020) Computer-aided discovery of phenylpyrazole based amides as potent S6K1 inhibitors. RSC Med Chem 11:583–590. https://doi.org/10.1039/C9MD00537D

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Dann SG et al (2007) mTOR Complex1–S6K1 signaling: at the crossroads of obesity, diabetes and cancer. Trends Mol Med 13:252–259. https://doi.org/10.1016/j.molmed.2007.04.002

    Article  CAS  PubMed  Google Scholar 

  8. Patra T et al (2021) Inhibition of p70 isoforms of S6K1 induces anoikis to prevent transformed human hepatocyte growth. Life Sci 265:118764. https://doi.org/10.1016/j.lfs.2020.118764

    Article  CAS  PubMed  Google Scholar 

  9. Bussenius J et al (2012) Design and evaluation of a series of pyrazolopyrimidines as p70S6K inhibitors. Bioorg Med Chem Lett 22:2283–2286. https://doi.org/10.1016/j.bmcl.2012.01.105

    Article  CAS  PubMed  Google Scholar 

  10. Dhar R et al (2008) Constitutive activation of p70 S6 kinase is associated with intrinsic resistance to cisplatin. Int J Mol Sci 32:1133–1137. https://doi.org/10.3892/ijo.32.5.1133

    Article  CAS  Google Scholar 

  11. Assad DX et al (2018) Additive cytotoxic effects of radiation and mTOR inhibitors in a cervical cancer cell line. Pathok Res Pract 214:259–262. https://doi.org/10.1016/j.prp.2017.10.019

    Article  CAS  Google Scholar 

  12. Xie G et al (2017) Dual blocking of PI3K and mTOR signaling by NVP-BEZ235 inhibits proliferation in cervical carcinoma cells and enhances therapeutic response. Cancer Lett 388:12–20. https://doi.org/10.1016/j.canlet.2016.11.024

    Article  CAS  PubMed  Google Scholar 

  13. Nam KH et al (2019) Identification of a novel S6K1 inhibitor, rosmarinic acid methyl ester, for treating cisplatin-resistant cervical cancer. BMC Cancer 19:1–13. https://doi.org/10.1186/s12885-019-5997-2

    Article  CAS  Google Scholar 

  14. Pearce LR, Alton GR et al (2010) Characterization of PF-4708671, a novel and highly specific inhibitor of p70 ribosomal S6 kinase (S6K1). Biochem J 431:245–255. https://doi.org/10.1042/BJ20101024

    Article  CAS  PubMed  Google Scholar 

  15. Wang J, Zhong C et al (2013) Crystal structures of S6K1 provide insights into the regulation mechanism of S6K1 by the hydrophobic motif. Biochem J 454:39–47. https://doi.org/10.1042/BJ20121863

    Article  CAS  PubMed  Google Scholar 

  16. Tolche A, Goldman J et al (2014) A phase I trial of LY2584702 tosylate, a p70 S6 kinase inhibitor, in patients with advanced solid tumours. Eur J Cancer 50:67–75. https://doi.org/10.1016/j.ejca.2013.11.039

    Article  CAS  Google Scholar 

  17. Hollebecque A, Houédé N et al (2014) A phase Ib trial of LY2584702 tosylate, a p70 S6 inhibitor, in combination with erlotinib or everolimus in patients with solid tumours. Eur J Cancer 50:76–84. https://doi.org/10.1016/j.ejca.2013.12.006

    Article  CAS  Google Scholar 

  18. Morreale A, Mallon B et al (1997) Ro31–8220 inhibits protein kinase C to block the phorbol ester-stimulated release of choline- and ethanolamine-metabolites from C6 glioma cells: p70 S6 kinase and MAPKAP kinase-1beta do not function downstream of PKC in activating PLD. FEBS Lett 417:38–42. https://doi.org/10.1016/s0014-5793(97)01252-0

    Article  CAS  PubMed  Google Scholar 

  19. Couty S, Westwood IM et al (2013) The discovery of potent ribosomal S6 kinase inhibitors by high-throughput screening and structure-guided drug design. Oncotarget 4:1647–1661. https://doi.org/10.18632/oncotarget.1255

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bae EJ, Yang YM et al (2007) Identification of a novel class of dithiolethiones that prevent hepatic insulin resistance via the adenosine monophosphate-activated protein kinase-p70 ribosomal S6 kinase-1 pathway. Hepatology 46:730–739. https://doi.org/10.1002/hep.21769

    Article  CAS  PubMed  Google Scholar 

  21. Ye P et al (2011) Potent and selective thiophene urea-templated inhibitors of S6K. Bioorg Med Chem Lett 21:849–852. https://doi.org/10.1016/j.bmcl.2010.11.069

    Article  CAS  PubMed  Google Scholar 

  22. Bussenius J et al (2012) Design and evaluation of a series of pyrazolopyrimidines as p70S6K inhibitors. Bioorg Med Chem Lett 22(6):2283–2286. https://doi.org/10.1016/j.bmcl.2012.01.105

    Article  CAS  PubMed  Google Scholar 

  23. Ip CKM et al (2012) Exploiting p70 S6 kinase as a target for ovarian cancer. Expert Opin Ther Targets 16(6):619–630. https://doi.org/10.1517/14728222.2012.684680

    Article  CAS  PubMed  Google Scholar 

  24. Chi OZ et al (2019) Inhibition of p70 ribosomal S6 kinase 1 (S6K1) by PF-4708671 decreased infarct size in early cerebral ischemia-reperfusion with decreased BBB permeability. Eur J Pharmacol 855:202–207. https://doi.org/10.1016/j.ejphar.2019.05.010

    Article  CAS  PubMed  Google Scholar 

  25. Estridge TB et al (2017) Identification of 4-aminopyrazolopyrimidine metabolite that may contribute to the hypolipidemic effects of LY2584702 in Long Evans diet–induced obese rats. J Pharmacol Exp Ther 362:108–118. https://doi.org/10.1124/jpet.117.240242

    Article  CAS  PubMed  Google Scholar 

  26. Zhang N et al (2020) Research progress of 70 kDa ribosomal protein S6 kinase (P70S6K) inhibitors as effective therapeutic tools for obesity, type II diabetes and cancer. Curr Med Chem 27:4699–4719. https://doi.org/10.2174/0929867327666200114113139

    Article  CAS  PubMed  Google Scholar 

  27. Baron R et al. (2012) Computational drug discovery and design, translated book computational drug discovery and design. https://doi.org/10.1007/978-1-61779-465-010.3390/molecules18010735

  28. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797. https://doi.org/10.1016/j.chembiol.2003.09.002

    Article  CAS  PubMed  Google Scholar 

  29. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865. https://doi.org/10.1038/nature03197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Singh N et al (2021) Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 22:1790–1818. https://doi.org/10.1093/bib/bbaa034

    Article  PubMed  Google Scholar 

  31. Lin X et al (2020) A review on applications of computational methods in drug screening and design. Molecules 25:1375. https://doi.org/10.3390/molecules25061375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. D’Souza S et al (2020) Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discov Today 25:748–756. https://doi.org/10.1016/j.drudis.2020.03.003

    Article  CAS  PubMed  Google Scholar 

  33. Zhu J et al (2021) A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ. Mol Divers 25:1271–1282. https://doi.org/10.1007/s11030-021-10243-1

    Article  CAS  PubMed  Google Scholar 

  34. Pinzi L et al (2019) Molecular docking: shifting paradigms in drug discovery. Int J Mol Sci 20:4331. https://doi.org/10.3390/ijms20184331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Niwa H et al (2014) Crystal structures of the S6K1 kinase domain in complexes with inhibitors. J Struct Funct Genomics 15:153–164. https://doi.org/10.1007/s10969-014-9188-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Adewumi AT et al (2020) Thompson loop: opportunities for antitubercular drug design by targeting the weak spot in demethylmenaquinone methyltransferase protein. RSC Adv 10:23466–23483. https://doi.org/10.1039/D0RA03206A

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Batool M, Ahmad B, Choi S (2019) A structure-based drug discovery paradigm. Int J Mol Sci 20:2783. https://doi.org/10.3390/ijms20112783

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhang H et al (2022) Discovery of novel microtubule stabilizers targeting taxane binding site by applying molecular docking, molecular dynamics simulation, and anticancer activity testing. Bioorg Chem 122:105722

    Article  CAS  PubMed  Google Scholar 

  39. Zhao S et al (2021) Ligand-based pharmacophore modeling, virtual screening and biological evaluation to identify novel TGR5 agonists. RSC Adv 11(16):9403–9409. https://doi.org/10.1039/d0ra10168k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gangwal RP et al (2014) Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening. J Mol Graph Model 49:18–24. https://doi.org/10.1016/j.jmgm.2014.01.002

    Article  CAS  PubMed  Google Scholar 

  41. Huey R et al (2012) Using AutoDock 4 and AutoDock vina with AutoDockTools: a tutorial. Trends Pharmacol Sci 10550:92037

    Google Scholar 

  42. Rao SN et al (2007) Validation studies of the site-directed docking program LibDock. J Chem Inf Model 47:2159–2171. https://doi.org/10.1021/ci6004299

    Article  CAS  PubMed  Google Scholar 

  43. Wu G et al (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER—A CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562. https://doi.org/10.1002/jcc.10306

    Article  CAS  PubMed  Google Scholar 

  44. Bissantz C et al (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem 43:4759–4767. https://doi.org/10.1021/jm001044l

    Article  CAS  PubMed  Google Scholar 

  45. Liu N et al (2019) Using LeDock as a docking tool for computational drug design. Journal 218:012–143. https://doi.org/10.1088/1755-1315/218/1/012143

    Article  Google Scholar 

  46. Friedman N et al (1997) Bayesian network classifiers. Mach Learn 29:131–163. https://doi.org/10.1023/A:1007465528199

    Article  Google Scholar 

  47. Sugahara S et al (2021) Exact learning augmented naive Bayes classifier. Entropy 23:1703. https://doi.org/10.3390/e23121703

    Article  PubMed  PubMed Central  Google Scholar 

  48. Van Der Spoel D et al (2005) GROMACS: fast, flexible, and free. J Comput chem 26:1701–1718. https://doi.org/10.1002/jcc.20291

    Article  CAS  PubMed  Google Scholar 

  49. Tian C et al (2019) ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J Chem Theory Comput 16:528–552. https://doi.org/10.1021/acs.jctc.9b00591

    Article  CAS  PubMed  Google Scholar 

  50. Maier JA et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713. https://doi.org/10.1021/acs.jctc.5b00255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Case DA et al (2021) Amber 2021. University of California, San Francisco

  52. Hiscocks J et al (2009) Gaussian 09: IOps reference. In: Caricato M, Frisch MJ (eds). Gaussian

  53. Sousa da Silva AW et al (2012) ACPYPE-Antechamber python parser interface. BMC Res Notes 5:1–8. https://doi.org/10.1186/1756-0500-5-367

    Article  Google Scholar 

  54. McGibbon RT et al (2015) MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109:1528–1532. https://doi.org/10.1016/j.bpj.2015.08.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Grant BJ et al (2021) The Bio3D packages for structural bioinformatics. Protein Sci 30:20–30. https://doi.org/10.1002/pro.3923

    Article  CAS  PubMed  Google Scholar 

  56. Kumari R et al (2014) g_mmpbsa A GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962. https://doi.org/10.1021/ci500020m

    Article  CAS  PubMed  Google Scholar 

  57. Lu S-H et al (2011) The discovery of potential acetylcholinesterase inhibitors: A combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci 18:8. https://doi.org/10.1186/1423-0127-18-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Lin H-Y et al (2019) Structure-based pharmacophore modeling to discover novel CCR5 inhibitors for HIV-1/cancers therapy. J Biomed Sci 12:10–30. https://doi.org/10.4236/jbise.2019.121002

    Article  CAS  Google Scholar 

  59. Weng C-W et al (2022) Hybrid pharmacophore- and structure-based virtual screeningpipeline to identify novel EGFR inhibitors that suppressnon-small cell lung cancer cell growth. Int J Mol Sci 23:34873. https://doi.org/10.3390/ijms23073487

    Article  CAS  Google Scholar 

  60. Kohlbacher SM et al (2021) QPHAR: quantitative pharmacophore activity relationship: method and validation. J Cheminformatics 13:57. https://doi.org/10.1186/s13321-021-00537-9

    Article  Google Scholar 

  61. Rogers D et al (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. https://doi.org/10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  62. Karnati KR et al (2018) Understanding the co-loading and releasing of doxorubicin and paclitaxel using chitosan functionalized single-walled carbon nanotubes by molecular dynamics simulations. Phys Chem Chem Phys 20:9389–9400. https://doi.org/10.1039/C8CP00124C

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank State Key Laboratory of Biotherapy and Cancer Center of West China Hospital for providing the Accelrys DS 3.5 program package to perform this research.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 82260693 and 81903543) and Science and Technology Program Project of Gansu Province (20JR5RA534).

Author information

Author notes

  1. Hui Zhang and Hong-Rui Zhang contributed equally to this work.

Authors and Affiliations

  1. College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, People’s Republic of China

    Hui Zhang, Hong-Rui Zhang, Jian Zhang, Mei-Ling Hu, Li Ren, Qing-Qing Luo & Hua-Zhao Qi

Authors
  1. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong-Rui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mei-Ling Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qing-Qing Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hua-Zhao Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Hui Zhang: conceptualization, methodology, project administration, investigation, supervision, writing—review and editing; Hong-Rui Zhang: methodology, investigation, writing—original draft, investigation, validation; Jian Zhang: validation, resources, formal analysis; Mei-Ling Hu: validation, data curation; Li Ren: data curation, formal analysis; Qing-Qing Luo: validation, data curation; Hua-Zhao Qi: resources, formal analysis.

Corresponding author

Correspondence to Hui Zhang.

Ethics declarations

Ethics approval

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Zhang, HR., Zhang, J. et al. Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation. J Mol Model 29, 102 (2023). https://doi.org/10.1007/s00894-023-05504-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-023-05504-9

Keywords

Search

Navigation