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

Abstract

Background

WD repeat structural domain 5 (WDR5), which plays an important role in various biological functions through epigenetic regulation, is aberrantly expressed in human cancers, and is an effective target for the discovery of anticancer drugs.

Methods

In this paper, QSAR modeling analysis, including comparative molecular field (CoMFA) and comparative molecular similarity index analysis field (CoMSIA), was first performed using 41 pyrroloimidazole analogs. The results showed q2=0.667 and r2=0.981 in CoMFA and q2=0.662 and r2=0.983 in CoMSIA. Molecular docking and molecular dynamics simulations further confirmed the interaction and binding affinity of the inhibitors with key residues of the proteins, for example, PHE149, PHE133, and CYS261.

Results

Based on QSAR and docking studies, seven new compounds with high scores and qualified ADMET performance were designed.

Conclusion

In this study, new ideas have been provided for exploring new WDR5 inhibitors.

Loading

Article metrics loading...

/content/journals/lddd/10.2174/1570180820666230829105308
2023-09-11
2025-07-06
Loading full text...

Full text loading...

References

  1. AhoE.R. WeissmillerA.M. FesikS.W. TanseyW.P. Targeting WDR5: A winning anti-cancer strategy?Epigenet. Insights2019122516865719865282
    [Google Scholar]
  2. CarugoA. GenoveseG. SethS. NeziL. RoseJ.L. BossiD. CicaleseA. ShahP.K. VialeA. PettazzoniP.F. AkdemirK.C. BristowC.A. RobinsonF.S. TepperJ. SanchezN. GuptaS. EstecioM.R. GiulianiV. DellinoG.I. RivaL. YaoW. Di FrancescoM.E. GreenT. D’AlesioC. CortiD. KangY. JonesP. WangH. FlemingJ.B. MaitraA. PelicciP.G. ChinL. DePinhoR.A. LanfranconeL. HeffernanT.P. DraettaG.F. In vivo functional platform targeting patient-derived xenografts identifies WDR5-Myc association as a critical determinant of pancreatic cancer.Cell Rep.201616113314710.1016/j.celrep.2016.05.063 27320920
    [Google Scholar]
  3. LuK. TaoH. SiX. ChenQ. The histone H3 Lysine 4 presenter WDR5 as an oncogenic protein and novel epigenetic target in cancer.Front. Oncol.2018850210.3389/fonc.2018.00502 30488017
    [Google Scholar]
  4. LiQ. HuangY. XuJ. MaoF. ZhouB. SunL. BasinskiB.W. AksuM. LiuJ. DouY. RaoR.C. p53 inactivation unmasks histone methylation-independent WDR5 functions that drive self-renewal and differentiation of pluripotent stem cells.Stem Cell Reports202116112642265810.1016/j.stemcr.2021.10.002 34715053
    [Google Scholar]
  5. CosgroveM.S. PatelA. Mixed lineage leukemia: A structure-function perspective of the MLL1 protein.FEBS J.201027781832184210.1111/j.1742‑4658.2010.07609.x 20236310
    [Google Scholar]
  6. ThomasL.R. WangQ. GriebB.C. PhanJ. FoshageA.M. SunQ. OlejniczakE.T. ClarkT. DeyS. LoreyS. AlicieB. HowardG.C. CawthonB. EssK.C. EischenC.M. ZhaoZ. FesikS.W. TanseyW.P. Interaction with WDR5 promotes target gene recognition and tumorigenesis by MYC.Mol. Cell201558344045210.1016/j.molcel.2015.02.028 25818646
    [Google Scholar]
  7. SunY. BellJ.L. CarterD. GherardiS. PoulosR.C. MilazzoG. WongJ.W.H. Al-AwarR. TeeA.E. LiuP.Y. LiuB. AtmadibrataB. WongM. TrahairT. ZhaoQ. ShohetJ.M. HauptY. SchulteJ.H. BrownP.J. ArrowsmithC.H. VedadiM. MacKenzieK.L. HüttelmaierS. PeriniG. MarshallG.M. BraithwaiteA. LiuT. WDR5 supports an N-Myc transcriptional complex that drives a protumorigenic gene expression signature in neuroblastoma.Cancer Res.201575235143515410.1158/0008‑5472.CAN‑15‑0423 26471359
    [Google Scholar]
  8. GeZ. SongE.J. KawasawaY.I. LiJ. DovatS. SongC. WDR5 high expression and its effect on tumorigenesis in leukemia.Oncotarget2016725377403775410.18632/oncotarget.9312 27192115
    [Google Scholar]
  9. ChenX. XieW. GuP. CaiQ. WangB. XieY. DongW. HeW. ZhongG. LinT. HuangJ. Upregulated WDR5 promotes proliferation, self-renewal and chemoresistance in bladder cancer via mediating H3K4 trimethylation.Sci. Rep.201551829310.1038/srep08293 25656485
    [Google Scholar]
  10. CuiZ. LiH. LiangF. MuC. MuY. ZhangX. LiuJ. Effect of high WDR5 expression on the hepatocellular carcinoma prognosis.Oncol. Lett.20181557864787010.3892/ol.2018.8298 29731905
    [Google Scholar]
  11. DaiX. GuoW. ZhanC. LiuX. BaiZ. YangY. WDR5 expression is prognostic of breast cancer outcome.PLoS One2015109e012496410.1371/journal.pone.0124964 26355959
    [Google Scholar]
  12. GrebienF. VedadiM. GetlikM. GiambrunoR. GroverA. AvellinoR. SkuchaA. VittoriS. KuznetsovaE. SmilD. Barsyte-LovejoyD. LiF. PodaG. SchapiraM. WuH. DongA. SenisterraG. StukalovA. HuberK.V.M. SchöneggerA. MarcellusR. BilbanM. BockC. BrownP.J. ZuberJ. BennettK.L. Al-awarR. DelwelR. NerlovC. ArrowsmithC.H. Superti-FurgaG. Pharmacological targeting of the Wdr5-MLL interaction in C/EBPα N-terminal leukemia.Nat. Chem. Biol.201511857157810.1038/nchembio.1859 26167872
    [Google Scholar]
  13. GrebienF. VedadiM. GetlikM. GiambrunoR. GroverA. AvellinoR. SkuchaA. VittoriS. KuznetsovaE. SmilD. Barsyte-LovejoyD. LiF. PodaG. SchapiraM. WuH. DongA. SenisterraG. StukalovA. HuberK.V.M. SchöneggerA. MarcellusR. BilbanM. BockC. BrownP.J. ZuberJ. BennettK.L. Al-awarR. DelwelR. NerlovC. ArrowsmithC.H. Superti-FurgaG. Erratum: Pharmacological targeting of the Wdr5-MLL interaction in C/EBPα N-terminal leukemia.Nat. Chem. Biol.2015111081510.1038/nchembio1015‑815b 26379026
    [Google Scholar]
  14. WangF. JeonK.O. SalovichJ.M. MacdonaldJ.D. AlvaradoJ. GogliottiR.D. PhanJ. OlejniczakE.T. SunQ. WangS. CamperD. YuhJ.P. ShawJ.G. SaiJ. RossaneseO.W. TanseyW.P. StaufferS.R. FesikS.W. Discovery of potent 2-Aryl-6,7-dihydro-5 H -pyrrolo[1,2- a]imidazoles as WDR5-WIN-Site inhibitors using fragment-based methods and structure-based design.J. Med. Chem.201861135623564210.1021/acs.jmedchem.8b00375 29889518
    [Google Scholar]
  15. ClarkM. CramerR.D.III Van OpdenboschN. Validation of the general purpose tripos 5.2 force field.J. Comput. Chem.19891089821012
    [Google Scholar]
  16. FuL. ChenY. XuC. WuT. GuoH. LinZ. WangR. ShuM. 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.20202961012102910.1007/s00044‑020‑02542‑3
    [Google Scholar]
  17. CramerR.D. SoltanshahiF. JilekR. CampbellB. AllChem: Generating and searching 1020 synthetically accessible structures.J. Comput. Aided Mol. Des.200721634135010.1007/s10822‑006‑9093‑8 17253118
    [Google Scholar]
  18. ManouchehrizadehE. MostoufiA. TahanpesarE. FereidoonnezhadM. Alignment-independent 3D-QSAR and molecular docking studies of tacrine−4-oxo-4H-Chromene hybrids as anti-Alzheimer’s agents.Comput. Biol. Chem.20198046347110.1016/j.compbiolchem.2019.05.010 31170562
    [Google Scholar]
  19. ZhouW. ChenZ. WangY. LiX. LuA. SunX. LiuZ. Systems pharmacology-based method to assess the mechanism of action of weight-loss herbal intervention therapy for obesity.Front. Pharmacol.201910116510.3389/fphar.2019.01165 31680953
    [Google Scholar]
  20. ChenY. TianY. GaoY. WuF. LuoX. JuX. LiuG. In silico Design of novel HIV-1 NNRTIs based on combined modeling studies of dihydrofuro[3,4-d]pyrimidines.Front Chem.2020816410.3389/fchem.2020.00164 32266208
    [Google Scholar]
  21. LiuJ. WangF. MaZ. WangX. WangY. Structural determination of three different series of compounds as Hsp90 inhibitors using 3D-QSAR modeling, molecular docking and molecular dynamics methods.Int. J. Mol. Sci.201112294697010.3390/ijms12020946 21541036
    [Google Scholar]
  22. Ul-HaqZ. AshrafS. Al-MajidA. BarakatA. 3D-QSAR Studies on barbituric acid derivatives as urease inhibitors and the effect of charges on the quality of a model.Int. J. Mol. Sci.201617565710.3390/ijms17050657 27144563
    [Google Scholar]
  23. BushB.L. NachbarR.B. Jr Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA.J. Comput. Aided Mol. Des.19937558761910.1007/BF00124364 8294948
    [Google Scholar]
  24. JinX. ZhaoY. RenZ. WangP. LiY. Bio-enhanced degradation strategies for fluoroquinolones in the sewage sludge composting stage: molecular modification and resistance gene regulation.Int. J. Environ. Res. Public Health20221913776610.3390/ijerph19137766 35805422
    [Google Scholar]
  25. ZhangL. TsaiK.C. DuL. FangH. LiM. XuW. How to generate reliable and predictive CoMFA models.Curr. Med. Chem.201118692393010.2174/092986711794927702 21182474
    [Google Scholar]
  26. KarS. RoyK. LeszczynskiJ. Applicability domain: A step toward confident predictions and decidability for QSAR Modeling.Methods Mol. Biol.2018180014116910.1007/978‑1‑4939‑7899‑1_6 29934891
    [Google Scholar]
  27. GolbraikhA. TropshaA. Beware of q2!J. Mol. Graph. Model.200220426927610.1016/S1093‑3263(01)00123‑1 11858635
    [Google Scholar]
  28. GolbraikhA. TropshaA. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.J. Comput. Aided Mol. Des.2002165/635736910.1023/A:1020869118689 12489684
    [Google Scholar]
  29. GaoJ. LiangL. ZhuY. QiuS. WangT. ZhangL. Ligand and structure-based approaches for the identification of peptide deformylase inhibitors as antibacterial drugs.Int. J. Mol. Sci.2016177114110.3390/ijms17071141 27428963
    [Google Scholar]
  30. CrossJ.B. ThompsonD.C. RaiB.K. BaberJ.C. FanK.Y. HuY. HumbletC. Comparison of several molecular docking programs: Pose prediction and virtual screening accuracy.J. Chem. Inf. Model.20094961455147410.1021/ci900056c 19476350
    [Google Scholar]
  31. KrügerA. ZimbresF. KronenbergerT. WrengerC. Molecular modeling applied to nucleic acid-based molecule development.Biomolecules2018838310.3390/biom8030083 30150587
    [Google Scholar]
  32. KrajkaV. VulinovicF. GenovaM. TanzerK. JijumonA.S. BodakuntlaS. TennstedtS. Mueller-FielitzH. MeierB. JankeC. KleinC. RakovicA. H-ABC– and dystonia-causing TUBB4A mutations show distinct pathogenic effects.Sci. Adv.2022810eabj922910.1126/sciadv.abj9229 35275727
    [Google Scholar]
  33. GötzA.W. WilliamsonM.J. XuD. PooleD. Le GrandS. WalkerR.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born.J. Chem. Theory Comput.2012851542155510.1021/ct200909j 22582031
    [Google Scholar]
  34. Salomon-FerrerR. GötzA.W. PooleD. Le GrandS. WalkerR.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. explicit solvent particle mesh ewald.J. Chem. Theory Comput.2013993878388810.1021/ct400314y 26592383
    [Google Scholar]
  35. SprengerK.G. JaegerV.W. PfaendtnerJ. The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids.J. Phys. Chem. B2015119185882589510.1021/acs.jpcb.5b00689 25853313
    [Google Scholar]
  36. Lindorff-LarsenK. PianaS. PalmoK. MaragakisP. KlepeisJ.L. DrorR.O. ShawD.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field.Proteins20107881950195810.1002/prot.22711 20408171
    [Google Scholar]
  37. LoncharichR.J. BrooksB.R. PastorR.W. Langevin dynamics of peptides: The frictional dependence of isomerization rates ofN-acetylalanyl-N?-methylamide.Biopolymers199232552353510.1002/bip.360320508 1515543
    [Google Scholar]
  38. LariniL. MannellaR. LeporiniD. Langevin stabilization of molecular-dynamics simulations of polymers by means of quasisymplectic algorithms.J. Chem. Phys.20071261010410110.1063/1.2464095 17362055
    [Google Scholar]
  39. MuraM. WangJ. ZhouY. PinnaM. ZvelindovskyA.V. DennisonS.R. PhoenixD.A. The effect of amidation on the behaviour of antimicrobial peptides.Eur. Biophys. J.201645319520710.1007/s00249‑015‑1094‑x 26745958
    [Google Scholar]
  40. VirtanenS.I. NiinivehmasS.P. PentikäinenO.T. Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening.J. Mol. Graph. Model.20156230331810.1016/j.jmgm.2015.10.012 26550792
    [Google Scholar]
  41. WengG. WangE. ChenF. SunH. WangZ. HouT. Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein–peptide complexes.Phys. Chem. Chem. Phys.20192119101351014510.1039/C9CP01674K 31062799
    [Google Scholar]
  42. HuangK. LuoS. CongY. ZhongS. ZhangJ.Z.H. DuanL. An accurate free energy estimator: Based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity.Nanoscale20201219107371075010.1039/C9NR10638C 32388542
    [Google Scholar]
  43. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b00104 25860834
    [Google Scholar]
  44. DainaA. MichielinO. ZoeteV. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules.Sci. Rep.2017714271710.1038/srep42717 28256516
    [Google Scholar]
  45. T, A.; R, G.; F, H. 3D-QSAR (CoMFA, CoMSIA) and molecular docking studies on histone deacetylase 1 selective inhibitors.Recent Patents Anticancer Drug Discov.2017124365383
    [Google Scholar]
/content/journals/lddd/10.2174/1570180820666230829105308
Loading
/content/journals/lddd/10.2174/1570180820666230829105308
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keyword(s): 3D-QSAR; ADMET; molecular docking; molecular dynamics simulation; WDR5; WDR5 inhibitors
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test