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Identification and neuroprotective evaluation of a potential c-Jun N-terminal kinase 3 inhibitor through structure-based virtual screening and in-vitro assay

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Abstract

The c-Jun N-terminal kinase 3 (JNK3) signaling cascade is activated during cerebral ischemia leading to neuronal damage. The present study was carried out to identify and evaluate novel JNK3 inhibitors using in-silico and in-vitro approach. A total of 380 JNK3 inhibitors belonging to different organic groups was collected from the previously reported literature. These molecules were used to generate a pharmacophore model. This model was used to screen a chemical database (SPECS) to identify newer molecules with similar chemical features. The top 1000 hits molecules were then docked against the JNK3 enzyme coordinate following GLIDE rigid receptor docking (RRD) protocol. Best posed molecules of RRD were used during induced-fit docking (IFD), allowing receptor flexibility. Other computational predictions such as binding free energy, electronic configuration and ADME/tox were also calculated. Inferences from the best pharmacophore model suggested that, in order to have specific JNK3 inhibitory activity, the molecules must possess one H-bond donor, two hydrophobic and two ring features. Docking studies suggested that the main interaction between lead molecules and JNK3 enzyme consisted of hydrogen bond interaction with methionine 149 of the hinge region. It was also observed that the molecule with better MM-GBSA dG binding free energy, had greater correlation with JNK3 inhibition. Lead molecule (AJ-292-42151532) with the highest binding free energy (dG = 106.8 Kcal/mol) showed better efficacy than the SP600125 (reference JNK3 inhibitor) during cell-free JNK3 kinase assay (IC50 = 58.17 nM) and cell-based neuroprotective assay (EC50 = 7.5 µM).

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References

  1. Pandian JD, Sudhan P (2013) Stroke epidemiology and stroke care services in India. J Stroke 15:128. https://doi.org/10.5853/jos.2013.15.3.128

    Article  PubMed  PubMed Central  Google Scholar 

  2. Feigin VL, Krishnamurthi RV, Parmar P et al (2015) Update on the global burden of ischemic and hemorrhagic stroke in 1990–2013: the GBD 2013 Study. Neuroepidemiology 45:161–176. https://doi.org/10.1159/000441085

    Article  PubMed  PubMed Central  Google Scholar 

  3. Shao S, Xu M, Zhou J et al (2017) Atorvastatin attenuates ischemia/reperfusion-induced hippocampal neurons injury via Akt-nNOS-JNK signaling pathway. Cell Mol Neurobiol 37:753–762. https://doi.org/10.1007/s10571-016-0412-x

    Article  CAS  PubMed  Google Scholar 

  4. Kuan C-Y, Whitmarsh AJ, Yang DD et al (2003) A critical role of neural-specific JNK3 for ischemic apoptosis. Proc Natl Acad Sci 100:15184–15189. https://doi.org/10.1073/pnas.2336254100

    Article  CAS  PubMed  Google Scholar 

  5. Messoussi A, Feneyrolles C, Bros A et al (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

    Article  CAS  PubMed  Google Scholar 

  6. Wu X-X, Dai D-S, Zhu X et al (2014) Molecular modeling studies of JNK3 inhibitors using QSAR and docking. Med Chem Res 23:2456–2475. https://doi.org/10.1007/s00044-013-0782-2

    Article  CAS  Google Scholar 

  7. Xu P, Yoshioka K, Yoshimura D et al (2003) In vitro development of mouse embryonic stem cells lacking JNK/stress-activated protein kinase-associated protein 1 (JSAP1) scaffold protein revealed its requirement during early embryonic neurogenesis. J Biol Chem 278:48422–48433. https://doi.org/10.1074/jbc.M307888200

    Article  CAS  PubMed  Google Scholar 

  8. Liu J-R, Zhao Y, Patzer A et al (2010) The c-Jun N-terminal kinase (JNK) inhibitor XG-102 enhances the neuroprotection of hyperbaric oxygen after cerebral ischaemia in adult rats. Neuropathol Appl Neurobiol 36:211–224. https://doi.org/10.1111/j.1365-2990.2009.01047.x

    Article  CAS  PubMed  Google Scholar 

  9. Jung H, Aman W, Hah J-M (2017) Novel scaffold evolution through combinatorial 3D-QSAR model studies of two types of JNK3 inhibitors. Bioorg Med Chem Lett 27:2139–2143. https://doi.org/10.1016/j.bmcl.2017.03.063

    Article  CAS  PubMed  Google Scholar 

  10. Zhang ZB, Li ZG (2012) Cathepsin B and Phospo-JNK in relation to ongoing apoptosis after transient focal cerebral ischemia in the rat. Neurochem Res 37:948–957. https://doi.org/10.1007/s11064-011-0687-8

    Article  CAS  PubMed  Google Scholar 

  11. Yamasaki T, Kawasaki H, Nishina H (2012) Diverse roles of JNK and MKK pathways in the brain. J Signal Transduct 2012:1–9. https://doi.org/10.1155/2012/459265

    Article  CAS  Google Scholar 

  12. Yarza R, Vela S, Solas M, Ramirez MJ (2016) c-Jun N-terminal kinase (JNK) signaling as a therapeutic target for Alzheimer’s disease. Front Pharmacol.https://doi.org/10.3389/fphar.2015.00321

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mishra P, Günther S (2018) New insights into the structural dynamics of the kinase JNK3. Sci Rep 8:9435. https://doi.org/10.1038/s41598-018-27867-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Xie X, Gu Y, Fox T et al (1998) Crystal structure of JNK3: a kinase implicated in neuronal apoptosis. Structure 6:983–991. https://doi.org/10.1016/S0969-2126(98)00100-2

    Article  CAS  PubMed  Google Scholar 

  15. Assi K, Pillai R, Gomez-Munoz A et al (2006) The specific JNK inhibitor SP600125 targets tumour necrosis factor-alpha production and epithelial cell apoptosis in acute murine colitis. Immunology 118:112–121. https://doi.org/10.1111/j.1365-2567.2006.02349.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gross ND, Boyle JO, Du B et al (2007) Inhibition of Jun NH2-terminal kinases suppresses the growth of experimental head and neck squamous cell carcinoma. Clin Cancer Res 13:5910–5917. https://doi.org/10.1158/1078-0432.CCR-07-0352

    Article  CAS  PubMed  Google Scholar 

  17. Minutoli L, Altavilla D, Marini H et al (2004) Protective effects of SP600125 a new inhibitor of c-jun N-terminal kinase (JNK) and extracellular-regulated kinase (ERK1/2) in an experimental model of cerulein-induced pancreatitis. Life Sci 75:2853–2866. https://doi.org/10.1016/j.lfs.2004.03.040

    Article  CAS  PubMed  Google Scholar 

  18. Gaillard P, Jeanclaude-Etter I, Ardissone V et al (2005) Design and synthesis of the first generation of novel potent, selective, and in vivo active (Benzothiazol-2-yl)acetonitrile inhibitors of the c-Jun N-terminal kinase. J Med Chem 48:4596–4607. https://doi.org/10.1021/jm0310986

    Article  CAS  PubMed  Google Scholar 

  19. Rückle T, Biamonte M, Grippi-Vallotton T et al (2004) Design, synthesis, and biological activity of novel, potent, and selective (Benzoylaminomethyl)thiophene sulfonamide inhibitors of c-Jun-N-terminal kinase. J Med Chem 47:6921–6934. https://doi.org/10.1021/jm031112e

    Article  CAS  PubMed  Google Scholar 

  20. Swahn B-M, Huerta F, Kallin E et al (2005) Design and synthesis of 6-anilinoindazoles as selective inhibitors of c-Jun N-terminal kinase-3. Bioorg Med Chem Lett 15:5095–5099. https://doi.org/10.1016/j.bmcl.2005.06.083

    Article  CAS  PubMed  Google Scholar 

  21. Swahn B-M, Xue Y, Arzel E et al (2006) Design and synthesis of 2′-anilino-4,4′-bipyridines as selective inhibitors of c-Jun N-terminal kinase-3. Bioorg Med Chem Lett 16:1397–1401. https://doi.org/10.1016/j.bmcl.2005.11.039

    Article  CAS  PubMed  Google Scholar 

  22. Cao J, Gao H, Bemis G et al (2009) Structure-based design and parallel synthesis of N-benzyl isatin oximes as JNK3 MAP kinase inhibitors. Bioorg Med Chem Lett 19:2891–2895. https://doi.org/10.1016/j.bmcl.2009.03.043

    Article  CAS  PubMed  Google Scholar 

  23. Shin Y, Chen W, Habel J et al (2009) Synthesis and SAR of piperazine amides as novel c-jun N-terminal kinase (JNK) inhibitors. Bioorg Med Chem Lett 19:3344–3347. https://doi.org/10.1016/j.bmcl.2009.03.086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Song X, Chen W, Lin L et al (2011) Synthesis and SAR of 2-phenoxypyridines as novel c-Jun N-terminal kinase inhibitors. Bioorg Med Chem Lett 21:7072–7075. https://doi.org/10.1016/j.bmcl.2011.09.090

    Article  CAS  PubMed  Google Scholar 

  25. Zheng K, Iqbal S, Hernandez P et al (2014) Design and synthesis of highly potent and isoform selective JNK3 Inhibitors: SAR studies on aminopyrazole derivatives. J Med Chem 57:10013–10030. https://doi.org/10.1021/jm501256y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. He Y, Duckett D, Chen W et al (2014) Synthesis and SAR of novel isoxazoles as potent c-jun N-terminal kinase (JNK) inhibitors. Bioorg Med Chem Lett 24:161–164. https://doi.org/10.1016/j.bmcl.2013.11.052

    Article  CAS  PubMed  Google Scholar 

  27. Zheng K, Park CM, Iqbal S et al (2015) Pyridopyrimidinone derivatives as potent and selective c-Jun N-terminal kinase (JNK) inhibitors. ACS Med Chem Lett 6:413–418. https://doi.org/10.1021/ml500474d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Darshit BS, Balaji B, Rani P, Ramanathan M (2014) Identification and in vitro evaluation of new leads as selective and competitive glycogen synthase kinase-3β inhibitors through ligand and structure based drug design. J Mol Graph Model 53:31–47. https://doi.org/10.1016/j.jmgm.2014.06.013

    Article  CAS  PubMed  Google Scholar 

  29. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671. https://doi.org/10.1007/s10822-006-9087-6

    Article  CAS  PubMed  Google Scholar 

  30. Muralikumar S, Vetrivel U, Narayanasamy A, Das N U (2017) Probing the intermolecular interactions of PPARγ-LBD with polyunsaturated fatty acids and their anti-inflammatory metabolites to infer most potential binding moieties. Lipids Health Dis 16:17. https://doi.org/10.1186/s12944-016-0404-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hevener KE, Mehboob S, Su P-C et al (2012) Discovery of a novel and potent class of F. tularensis Enoyl-Reductase (FabI) inhibitors by molecular shape and electrostatic matching. J Med Chem 55:268–279. https://doi.org/10.1021/jm201168g

    Article  CAS  PubMed  Google Scholar 

  32. Jana S, Singh SK (2019) Identification of selective MMP-9 inhibitors through multiple e-pharmacophore, ligand-based pharmacophore, molecular docking, and density functional theory approaches. J Biomol Struct Dyn 37:944–965. https://doi.org/10.1080/07391102.2018.1444510

    Article  CAS  PubMed  Google Scholar 

  33. Sivashanmugam M, S KN (2019) Virtual screening of natural inhibitors targeting ornithine decarboxylase with pharmacophore scaffolding of DFMO and validation by molecular dynamics simulation studies. J Biomol Struct Dyn 37:766–780. https://doi.org/10.1080/07391102.2018.1439772

    Article  CAS  PubMed  Google Scholar 

  34. Zhang G, Musgrave CB (2007) Comparison of DFT methods for molecular orbital eigenvalue calculations. J Phys Chem A 111:1554–1561. https://doi.org/10.1021/jp061633o

    Article  CAS  PubMed  Google Scholar 

  35. Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58:4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang H, Lou C, Sun L et al (2019) admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35:1067–1069. https://doi.org/10.1093/bioinformatics/bty707

    Article  CAS  PubMed  Google Scholar 

  37. Darshit BS, Ramanathan M (2016) Activation of AKT1/GSK-3β/β-catenin–TRIM11/survivin pathway by novel GSK-3β inhibitor promotes neuron cell survival: study in differentiated SH-SY5Y cells in OGD model. Mol Neurobiol 53:6716–6729. https://doi.org/10.1007/s12035-015-9598-z

    Article  CAS  PubMed  Google Scholar 

  38. Wu X (2015) Dual AO/EB staining to detect apoptosis in osteosarcoma cells compared with flow cytometry. Med Sci Monit Basic Res 21:15–20. https://doi.org/10.12659/MSMBR.893327

    Article  PubMed  PubMed Central  Google Scholar 

  39. Banavath HN, Sharma OP, Kumar MS, Baskaran R (2015) Identification of novel tyrosine kinase inhibitors for drug resistant T315I mutant BCR-ABL: a virtual screening and molecular dynamics simulations study. Sci Rep 4:6948. https://doi.org/10.1038/srep06948

    Article  Google Scholar 

  40. Manolopoulos DE, May C, Down SE (1991) Theoretical studies of the fullerenes: CJ4 to CT0. Chem Phys Lett 181:105–111

    Article  CAS  Google Scholar 

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Acknowledgements

We thank Shelvia Malik, Jr. Application scientist, Dr. Sudharsan Pandiyan, Senior Application Scientist, Schrodinger, and Dr. Pritesh Bhat, Senior Application Scientist, Schrodinger, for providing their valuable time and expertise and technical support during the study. We thank the National Centre for Cell Science (NCCS) Pune for providing cell lines at a reasonable price and in a timely manner. We also thank, Dr. Sivaram Hariharan for syntax and grammar revision. We thank PSG Sons & Charity and Tamilnadu Dr. MGR medical university for providing us with a better facility and environment for the learning.

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  1. Department of Pharmacology, PSG College of Pharmacy, Coimbatore, Tamilnadu, India

    Ravi Kumar Rajan & M. Ramanathan

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Rajan, R.K., Ramanathan, M. Identification and neuroprotective evaluation of a potential c-Jun N-terminal kinase 3 inhibitor through structure-based virtual screening and in-vitro assay. J Comput Aided Mol Des 34, 671–682 (2020). https://doi.org/10.1007/s10822-020-00297-y

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