Skip to main content
Log in

A selectivity study of polysubstituted pyridinylimidazoles as dual inhibitors of JNK3 and p38α MAPK based on 3D-QSAR, molecular docking, and molecular dynamics simulation

  • Original Research
  • Published:
Structural Chemistry Aims and scope Submit manuscript

Abstract

Within the mitogen-activated protein kinase (MAPK) 1 family, the two kinases of c-Jun N-terminal kinase 3 (JNK3) and p38α MAPK have emerged in the last decades as particularly attractive therapeutic targets due to their implication in several neurodegenerative pathologic conditions. In this study, the structure and activity relationship of 60 dual JNK3/p38α MAPK inhibitors was explored; three-dimensional quantitative structure-activity relationship (3D-QSAR), including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis fields (CoMSIA), was performed. From the data we got, the 3D-QSAR model (CoMFAJNK3 with q2 is 0.642, r2 is 0.958; CoMSIAJNK3 with q2 is 0.660, r2 is 0.963; CoMFAp38α with q2 is 0.605, r2 is 0.980; CoMSIAp38α with q2 is 0.608, r2 is 0.970) had good predictability. Molecular docking further revealed the binding mode of inhibitors to JNK3/p38α MAPK. The results of 3D-QSAR, molecular docking, and molecular dynamics simulation also provided guidance for the discovery of new dual inhibitors of JNK3 and p38α MAPK. Finally, 10 novel compounds with good potential activity and ADME/T profile were designed. Molecular dynamics simulation results validated that Met149/Lys 93/Gln 155 (JNK3) and Met109/Lys53 (p38α) located in the active site play a key role for novel dual inhibitors.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Fricker M, LoGrasso P, Ellis S, Wilkie N, Hunt P, Pollack SJ (2005) Substituting c-Jun N-terminal kinase-3 (JNK3) ATP-binding site amino acid residues with their p38 counterparts affects binding of JNK- and p38-selective inhibitors. Arch Biochem Biophys 438:195–205. https://doi.org/10.1016/j.abb.2005.04.013

    Article  CAS  PubMed  Google Scholar 

  2. Resnick L, Fennell M (2004) Targeting JNK3 for the treatment of neurodegenerative disorders. Drug Discov Today 9:932–939. https://doi.org/10.1016/S1359-6446(04)03251-9

    Article  CAS  PubMed  Google Scholar 

  3. Messoussi A, Feneyrolles C, Bros A, Deroide A, Daydé-Cazals B, Chevé G (2014) Recent progress in the design, study, and development of c-Jun N-terminal kinase inhibitors as anticancer agents. Chem Biol 21:1433-1443. https://doi.org/10.1016/j.chembiol.2014.09.007

  4. Mielke K, Herdegen T (2000) JNK and p38 stresskinases-degenerative effectors of signal-transduction-cascades in the nervous system. Prog Neurobiol 61:45–60. https://doi.org/10.1016/S0301-0082(99)00042-8

    Article  CAS  PubMed  Google Scholar 

  5. Ansideri F, Macedo JT, Eitel M, El-Gokha A, Zinad DS, Scarpellini C, et al (2018) Structural optimization of a pyridinylimidazole scaffold: shifting the selectivity from p38α mitogen-activated protein kinase to c-Jun N-terminal kinase 3. ACS Omega 3:7809-7831. https://doi.org/10.1021/acsomega.8b00668

  6. Koch P, Jahns H, Schattel V, Goettert M, Laufer S (2010) Pyridinylquinoxalines and pyridinylpyridopyrazines as lead compounds for novel p38α mitogen-activated protein kinase inhibitors. J Med Chem 53:1128–1137. https://doi.org/10.1021/jm901392x

    Article  CAS  PubMed  Google Scholar 

  7. Muth F, El-Gokha A, Ansideri F, Eitel M, Döring E, Sievers-Engler A et al (2017) Tri- and tetrasubstituted pyridinylimidazoles as covalent inhibitors of c-Jun N-terminal kinase 3. J Med Chem 60:594–607. https://doi.org/10.1021/acs.jmedchem.6b01180

    Article  CAS  PubMed  Google Scholar 

  8. Muth F, Günther M, Bauer SM, Döring E, Fischer S, Maier J et al (2015) Tetra-substituted pyridinylimidazoles as dual inhibitors of p38α mitogen-activated protein kinase and c-Jun N-terminal kinase 3 for potential treatment of neurodegenerative diseases. J Med Chem 58:443–456. https://doi.org/10.1021/jm501557a

    Article  CAS  PubMed  Google Scholar 

  9. Ansideri F, Lange A, El-Gokha A, Boeckler FM, Koch P (2016) Fluorescence polarization-based assays for detecting compounds binding to inactive c-Jun N-terminal kinase 3 and p38α mitogen-activated protein kinase. Anal Biochem 503:28–40. https://doi.org/10.1016/j.ab.2016.02.018

    Article  CAS  PubMed  Google Scholar 

  10. Fu L, Chen Y, C-m X, Wu T, H-m G, Lin Z-h et al (2020) 3D-QSAR, HQSAR, molecular docking, and new compound design study of 1,3,6-trisubstituted 1,4-diazepan-7-ones as human KLK7 inhibitors. Med Chem Res 29:1012–1029. https://doi.org/10.1007/s00044-020-02542-3

    Article  CAS  Google Scholar 

  11. Clark M, Cramer RD, Jones DM, Patterson DE, Simeroth PE (1990) Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases. Tetrahedron Comput Methodol 3:47–59. https://doi.org/10.1016/0898-5529(90)90120-W

    Article  CAS  Google Scholar 

  12. Klebe G, Abraham UJJoC-AMD (1999) Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J Comput Aided Mol Des 13:1–10. https://doi.org/10.1023/a:1008047919606

    Article  CAS  PubMed  Google Scholar 

  13. Bush B, Nachbar RJJCAMD (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des 7:587–619. https://doi.org/10.1007/bf00124364

    Article  CAS  PubMed  Google Scholar 

  14. Wendt B, Cramer RJJCAMD (2014) Challenging the gold standard for 3D-QSAR: template CoMFA versus X-ray alignment. J Comput Aided Mol Des 28:803–824. https://doi.org/10.1007/s10822-014-9761-z

    Article  CAS  PubMed  Google Scholar 

  15. Golbraikh A, Tropsha A (2002a) Beware of q2! J Mol Graph Model 20:269–276. https://doi.org/10.1016/s1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  16. Golbraikh A, Tropsha AJJCAMD (2002b) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des 16:357–369. https://doi.org/10.1023/a:1021372108686

    Article  CAS  PubMed  Google Scholar 

  17. Mitra I, Roy PP, Kar S, Ojha PK, Roy KJJC (2010) On further application of r2m as a metric for validation of QSAR models. J Chemom 24:22–33. https://doi.org/10.1002/cem.1268

    Article  CAS  Google Scholar 

  18. Pratim Roy P, Paul S, Mitra I, Roy KJM (2009) On two novel parameters for validation of predictive QSAR models. Molecules. 14:1660–1701. https://doi.org/10.3390/molecules14051660

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Götz AW, Williamson MJ, Xu D, Poole D, Le Grand S, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized Born. J Chem Theory Comput 8:1542–1555. https://doi.org/10.1021/ct200909j

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9:3878–3888. https://doi.org/10.1021/ct400314y

    Article  CAS  PubMed  Google Scholar 

  21. Sprenger KG, Jaeger VW, Pfaendtner J (2015) The General AMBER Force Field (GAFF) Can accurately predict thermodynamic and transport properties of many ionic liquids. J Phys Chem B 119:5882–5895. https://doi.org/10.1021/acs.jpcb.5b00689

    Article  CAS  PubMed  Google Scholar 

  22. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Struct Funct Bioinformatics 78:1950–1958. https://doi.org/10.1002/prot.22711

    Article  CAS  Google Scholar 

  23. Sun H, Duan L, Chen F, Liu H, Wang Z, Pan P et al (2018) Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches. Phys Chem Chem Phys 20:14450–14460. https://doi.org/10.1039/c7cp07623a

    Article  CAS  PubMed  Google Scholar 

  24. Huang K, Luo S, Cong Y, Zhong S, Zhang JZH, Duan L (2020) An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity. Nanoscale. 12:10737–10750. https://doi.org/10.1039/c9nr10638c

    Article  CAS  PubMed  Google Scholar 

  25. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics 1:8. https://doi.org/10.1186/1758-2946-1-8

    Article  CAS  Google Scholar 

  26. Abdizadeh T, Ghodsi R, Hadizadeh F (2017) 3D-QSAR (CoMFA, CoMSIA) and molecular docking studies on histone deacetylase 1 selective inhibitors. Recent Pat Anti-Cancer Drug Discov 12:365–383. https://doi.org/10.2174/1574892812666170508125927

    Article  CAS  Google Scholar 

  27. Astolfi A, Kudolo M, Brea J, Manni G, Manfroni G, Palazzotti D et al (2019) Discovery of potent p38α MAPK inhibitors through a funnel like workflow combining in silico screening and in vitro validation. Eur J Med Chem 182:111624. https://doi.org/10.1016/j.ejmech.2019.111624

    Article  CAS  PubMed  Google Scholar 

  28. Laufer SA, Hauser DRJ, Domeyer DM, Kinkel K, Liedtke AJ (2008) Design, synthesis, and biological evaluation of novel tri- and tetrasubstituted imidazoles as highly potent and specific ATP-mimetic inhibitors of p38 MAP kinase: focus on optimized interactions with the enzyme’s surface-exposed front region. J Med Chem 51:4122–4149. https://doi.org/10.1021/jm701529q

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

The authors are grateful to the Natural Science Foundation of China (81171508), the Key Project of Chongqing Natural Science Foundation (cstc2015jcyjBX0080), and the Scientific Research Startup Fund of Chongqing University of Technology (2017ZD42).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mao Shu or Zhi-hua Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Supplementary Information

ESM 1

(DOCX 2356 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, L., Chen, Y., Guo, Hm. et al. A selectivity study of polysubstituted pyridinylimidazoles as dual inhibitors of JNK3 and p38α MAPK based on 3D-QSAR, molecular docking, and molecular dynamics simulation. Struct Chem 32, 819–834 (2021). https://doi.org/10.1007/s11224-020-01668-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11224-020-01668-9

Keywords

Navigation