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2000
Volume 21, Issue 17
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

It has been reported that PKCθ plays an important role in the immune response by regulating the cellular activity of T cells and, thus, the production of immune factors such as IL-2.PKCθ protein is mainly expressed in T lymphocytes but not much in other cells and has a very good specificity. Therefore, it is very meaningful to use PKCθ protein as a novel target for immunosuppression. PKCθ is a valuable target that can be used to develop meaningful novel selective immunosuppressive agents.

Methods

In this study, we constructed a 3-characteristic pharmacophore(RHA)and used it to perform a virtual screening of the database. Then, we performed a molecular docking analysis of the compounds that scored high. The top five compounds with molecular docking scores were used as lead compounds, and structure-based ligand design fragment substitution was applied to them, resulting in 20077 new compounds. We performed molecular docking analysis, binding free energy calculations, molecular dynamics simulation, and ADMET prediction on these new compounds and finally identified two compounds as new PKCθ inhibitors.

Results and Discussion

Through the screening of pharmacophore, molecular design based on fragment substitution, molecular docking, we finally obtained two small molecules with higher scores than the positive control, in which the molecular docking score of P01 was -53.88 kcal/mol, and the molecular docking score of P02 was -51.20 kcal/mol, and then we performed the molecular dynamics simulation, free energy of binding calculations, and the prediction of ADMET properties for the compounds. The results showed that the ligands could form more stable complexes with the proteins, the binding free energy calculations of the ligand molecules were better than the positive control, all of them had good ADMET properties, and the compounds all had good drug similarity.

Conclusion

Our results provided 2 new ligands that could serve as lead compounds for new PKCθ inhibitors in the future.

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2024-08-12
2025-07-03
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References

  1. IgumenovaT.I. Dynamics and membrane interactions of protein kinase C.Biochemistry201554324953496810.1021/acs.biochem.5b00565 26214365
    [Google Scholar]
  2. BaierG. TelfordD. GiampaL. CoggeshallK.M. Baier-BitterlichG. IsakovN. AltmanA. Molecular cloning and characterization of PKC θ, a novel member of the protein kinase C (PKC) gene family expressed predominantly in hematopoietic cells.J. Biol. Chem.199326874997500410.1016/S0021‑9258(18)53494‑3 8444877
    [Google Scholar]
  3. SunZ. ArendtC.W. EllmeierW. SchaefferE.M. SunshineM.J. GandhiL. AnnesJ. PetrzilkaD. KupferA. SchwartzbergP.L. LittmanD.R. PKC-θ is required for TCR-induced NF-κB activation in mature but not immature T lymphocytes.Nature2000404677640240710.1038/35006090 10746729
    [Google Scholar]
  4. MajhiA. RahmanG.M. PanchalS. DasJ. Binding of curcumin and its long chain derivatives to the activator binding domain of novel protein kinase C.Bioorg. Med. Chem.20101841591159810.1016/j.bmc.2009.12.075 20100661
    [Google Scholar]
  5. MarslandB.J. KopfM. T-cell fate and function: PKC-θ and beyond.Trends Immunol.200829417918510.1016/j.it.2008.01.005 18328786
    [Google Scholar]
  6. ZhangE.Y. KongK.F. AltmanA. The yin and yang of protein kinase C-theta (PKCθ): A novel drug target for selective immunosuppression.Adv. Pharmacol.2013666626731210.1016/B978‑0‑12‑404717‑4.00006‑8 23433459
    [Google Scholar]
  7. WangD. YouY. CaseS.M. McAllister-LucasL.M. WangL. DiStefanoP.S. NuñezG. BertinJ. LinX. A requirement for CARMA1 in TCR-induced NF-κB activation.Nat. Immunol.20023983083510.1038/ni824 12154356
    [Google Scholar]
  8. LiY. HuJ. VitaR. SunB. TabataH. AltmanA. SPAK kinase is a substrate and target of PKCθ in T-cell receptor-induced AP-1 activation pathway.EMBO J.20042351112112210.1038/sj.emboj.7600125 14988727
    [Google Scholar]
  9. AltmanA. KaminskiS. BusuttilV. DroinN. HuJ. TadevosyanY. HipskindR.A. VillalbaM. Positive feedback regulation of PLCγ1/Ca2+ signaling by PKCθ in restimulated T cells via a Tec kinase‐dependent pathway.Eur. J. Immunol.20043472001201110.1002/eji.200324625 15214048
    [Google Scholar]
  10. KWON M.J.; WANG, R.; Sun, Z.M.; Pfeifhofer, C. PKCθ is a drug target for prevention of T cell—mediated autoimmunity and allograft rejection journal is.Endocr. Metab. Immune Disord. Drug Targets2010104367372
    [Google Scholar]
  11. PfeifhoferC. KoflerK. GruberT. Ghaffari TabriziN. LutzC. MalyK. LeitgesM. BaierG. Protein kinase C θ affects Ca2+ mobilization and NFAT cell activation in primary mouse T cells.J. Exp. Med.2003197111525153510.1084/jem.20020234 12782715
    [Google Scholar]
  12. MarslandB.J. SoosT.J. SpäthG. LittmanD.R. KopfM. Protein kinase C θ is critical for the development of in vivo T helper (Th)2 cell but not Th1 cell responses.J. Exp. Med.2004200218118910.1084/jem.20032229 15263025
    [Google Scholar]
  13. TanS.L. ZhaoJ. BiC. ChenX.C. HepburnD.L. WangJ. SedgwickJ.D. ChintalacharuvuS.R. NaS. Resistance to experimental autoimmune encephalomyelitis and impaired IL-17 production in protein kinase C theta-deficient mice.J. Immunol.200617652872287910.4049/jimmunol.176.5.2872 16493044
    [Google Scholar]
  14. GruberT. Hermann-KleiterN. Pfeifhofer-ObermairC. Lutz-NicoladoniC. ThuilleN. LetschkaT. BarsigJ. BaudlerM. LiJ. MetzlerB. Nüsslein-HildesheimB. WagnerJ. LeitgesM. BaierG. PKCθ cooperates with PKCα in alloimmune responses of T cells in vivo.Mol. Immunol.200946102071207910.1016/j.molimm.2009.02.030 19356803
    [Google Scholar]
  15. ValenzuelaJ.O. IclozanC. HossainM.S. PrlicM. HopewellE. BronkC.C. WangJ. CelisE. EngelmanR.W. BlazarB.R. BevanM.J. WallerE.K. YuX.Z. BegA.A. PKCθ is required for alloreactivity and GVHD but not for immune responses toward leukemia and infection in mice.J. Clin. Invest.2009119123774378610.1172/JCI39692 19907075
    [Google Scholar]
  16. RussG.R. Tedesco-SilvaH. KuypersD.R. CohneyS. LangerR.M. WitzkeO. ErisJ. SommererC. von Zur-MühlenB. WoodleE.S. GillJ. NgJ. KluppJ. ChodoffL. BuddeK. Efficacy of sotrastaurin plus tacrolimus after de novo kidney transplantation: Randomized, phase II trial results.Am. J. Transplant.20131371746175610.1111/ajt.12251 23668931
    [Google Scholar]
  17. PascherA. Simone DeP. PratschkeJ. SalaméE. PirenneJ. IsoneimiH. BijarniaM. KrishnanI. KluppJ. Protein kinase C inhibitor sotrastaurin in de novo liver transplant recipients: A randomized phase II trial.Am. J. Transplant.20151551283129210.1111/ajt.13175 25677074
    [Google Scholar]
  18. TrotterJ.F. LevyG. Sotrastaurin in liver transplantation: has it had a fair trial?Am. J. Transplant.20151551137113810.1111/ajt.13179 25677186
    [Google Scholar]
  19. YuX.Z. ValenzuelaJ.O. IclozanC.A. BlazarB.R. WallerE.K. BegA.A. Pkcθ is required for alloreactivity and gvhd but not for immune responses toward leukemia and infection in mice.Biol. Blood Marrow Transplant.2010162S313S31410.1016/j.bbmt.2009.12.469
    [Google Scholar]
  20. HuangH.J. YuH.W. ChenC.Y. HsuC.H. ChenH.Y. LeeK.J. TsaiF.J. ChenC.Y.C. Current developments of computer-aided drug design.J. Taiwan Inst. Chem. Eng.201041662363510.1016/j.jtice.2010.03.017
    [Google Scholar]
  21. TaleleT. KhedkarS. RigbyA. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic.Curr. Top. Med. Chem.201010112714110.2174/156802610790232251 19929824
    [Google Scholar]
  22. VonI.M. The war against influenza: Discovery and development of sialidase inhibitors.Nat. Biotechnol.200826Suppl. 1s47s54
    [Google Scholar]
  23. DingT.T. LiuY.Y. ZhangL.M. Exploring dual agonists for PPARα/γ receptors using pharmacophore modeling, docking analysis and molecule dynamics simulation.Comb. Chem. High Throughput Screen.202124
    [Google Scholar]
  24. ShanJ. PanX. WangX. XiaoX. JiC. FragRep: A web server for structure-based drug design by fragment replacement.J. Chem. Inf. Model.202060125900590610.1021/acs.jcim.0c00767 33275427
    [Google Scholar]
  25. AbrahamM.J. MurtolaT. SchulzR. PállS. SmithJ.C. HessB. LindahlE. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  26. PállS. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS.J. Chem. Phys.20141531313411010.1063/5.0018516
    [Google Scholar]
  27. Páll Szilárd GROMACS 2020.6 Source code.Zenodo2020
    [Google Scholar]
  28. van EisM.J. PállJ. SchulerW. ZenkeG. VangrevelingheE. A web server for structure-based drug design by fragment replacement.J. Chem. Inf. Model.2020601259005906
    [Google Scholar]
  29. van EisM.J. EvenouJ. SchulerW. ZenkeG. VangrevelingheE. WagnerJ. von MattP. high performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151C10.1016/j.softx.2015.06.001
    [Google Scholar]
  30. SilakariO. ChandS. BahiaM.S. Structural basis of amino pyrimidine derivatives for inhibitory activity of PKC‐ θ: 3D‐QSAR and molecular docking studies.Mol. Inform.201231965966810.1002/minf.201100123 27477816
    [Google Scholar]
  31. SilakariO. ChandS. KaurM. VyasB. SilakariP. BahiaM. Receptor guided 3D-QSAR analysis of thieno[2,3-b]pyridine-5- carbonitrile inhibitors of protein kinase C theta (PKC-θ).Comb. Chem. High Throughput Screen.201316973173810.2174/13862073113169990059 24050689
    [Google Scholar]
  32. MengL. FengK. Molecular modelling studies of tricyclic triazinone analogues as potential PKC-theta inhibitors through combined QSAR, molecular docking and molecular dynamics simulations techniques.J. Taiwan Inst. Chem. Eng.2018121
    [Google Scholar]
  33. LiY. HaoM. RenH. ZhangS. WangX. MaM. LiG. YangL. Exploring the structure requirement for PKCθ inhibitory activity of pyridinecarbonitrile derivatives: An in silico analysis.J. Mol. Graph. Model.201234768810.1016/j.jmgm.2011.12.010 22306416
    [Google Scholar]
  34. KunikawaS. TanakaA. TakasunaY. TasakiM. ChidaN. A novel 2,4-diaminopyrimidine derivative as selective inhibitor of protein kinase C theta prevents allograft rejection in a rat heart transplant model.Bioorg. Med. Chem.201826205499550910.1016/j.bmc.2018.09.029 30274941
    [Google Scholar]
  35. KatohT. TakaiT. YukawaT. TsukamotoT. WatanabeE. MototaniH. AritaT. HayashiH. NakagawaH. KleinM.G. ZouH. SangB.C. SnellG. NakadaY. Discovery and optimization of 1,7-disubstituted-2,2-dimethyl-2,3-dihydroquinazolin-4(1H)-ones as potent and selective PKCθ inhibitors.Bioorg. Med. Chem.201624112466247510.1016/j.bmc.2016.04.008 27117263
    [Google Scholar]
  36. IqbalS. Anantha KrishnanD. GunasekaranK. Identification of potential PKC inhibitors through Pharmacophore designing, 3D-QSAR and molecular dynamics simulations targeting Alzheimer’s disease.J. Biomol. Struct. Dyn.2017138 29182053
    [Google Scholar]
  37. CywinC.L. DahmannG. ProkopowiczA.S.III YoungE.R.R. MagoldaR.L. CardozoM.G. CoganD.A. DiSalvoD. GinnJ.D. KashemM.A. WolakJ.P. HomonC.A. FarrellT.M. GrbicH. HuH. KaplitaP.V. LiuL.H. SperoD.M. JeanfavreD.D. O’SheaK.M. WhiteD.M. WoskaJ.R.Jr BrownM.L. Discovery of potent and selective PKC-θ inhibitors.Bioorg. Med. Chem. Lett.200717122523010.1016/j.bmcl.2006.09.056 17055721
    [Google Scholar]
  38. WangJ. JinW. ZhouX. LiJ. XuC. MaZ. WangJ. QinL. ZhouB. DingW. GaoT. YaoH. ChenZ. Identification, structure–activity relationships of marine-derived indolocarbazoles, and a dual PKCθ/δ inhibitor with potent antipancreatic cancer efficacy.J. Med. Chem.20206321129781299110.1021/acs.jmedchem.0c01271 33100009
    [Google Scholar]
  39. KumarA. KumarP. Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking.Struct. Chem.202132114916510.1007/s11224‑020‑01629‑2
    [Google Scholar]
  40. HatmalM.M. TahaM.O. Simulated annealing molecular dynamics and ligand-receptor contacts analysis for pharmacophore modeling.Future Med. Chem.20179111141115910.4155/fmc‑2017‑0061 28722471
    [Google Scholar]
  41. WuG. RobertsonD.H. BrooksC.L.3rd ViethM. Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm.Asian J. Res. Chem200314213513810.1002/jcc.10306
    [Google Scholar]
  42. RohaneJ.S.H. Drug designing in discovery studio.Asian J. Res. Chem2021142
    [Google Scholar]
  43. HartK. FoloppeN. BakerC.M. DenningE.J. NilssonL. MacKerellA.D.Jr Optimization of the CHARMM additive force field for DNA: Improved treatment of the BI/BII conformational equilibrium.J. Chem. Theory Comput.20128134836210.1021/ct200723y 22368531
    [Google Scholar]
  44. KumariR. DalalV. Identification of potential inhibitors for llm of staphylococcus aureus: Structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies.J. Biomol. Struct. Dyn.2021115 34096457
    [Google Scholar]
  45. KumariR. RathiR. PathakS.R. DalalV. Structural-based virtual screening and identification of novel potent antimicrobial compounds against YsxC of Staphylococcus aureus.J. Mol. Struct.2022125513247610.1016/j.molstruc.2022.132476
    [Google Scholar]
  46. DalalV. KumariR. Screening and identification of natural product-like compounds as potential antibacterial agents targeting femC of staphylococcus aureus: An in-silico approach.ChemistrySelect2022742e20220172810.1002/slct.202201728
    [Google Scholar]
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