Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Chinese Chemistry
  4. Volume 2, Issue 3
  5. Article
Volume 2, Issue 3
  • ISSN: 2666-0016
  • E-ISSN: 2666-0008

Abstract

ATAD2 is closely related to the occurrence and proliferation of many tumors. Thus, exploring ATAD2 inhibitors is greatly significant for the prevention and treatment of tumors. In this study, the quantitative structure–activity relationship (QSAR) analyses of 57 naphthyridone derivatives were conducted using hologram quantitative structure–activity relationship (HQSAR) and topomer comparative molecular field analysis (topomer CoMFA).

The 57 naphthyridone derivatives were divided into the training (44 derivatives) and testing (13 derivatives) sets. HQSAR and topomer CoMFA models were obtained by applying the SYBYL-X software and validated using various validation parameters. Contribution maps from the best HQSAR model and the contour maps from the best topomer CoMFA model were analyzed.

The most effective HQSAR model exhibited significant cross-validated (2 = 0.872) and non cross-validated (2 = 0.972) correlation coefficients, and the most effective topomer CoMFA model had 2 = 0.861 and 2 = 0.962. Several external validation parameters, such as ∆2, and, were used to calculate the correlation coefficients of the test set samples and validate both models. The result exhibited a powerful predictive capability. Graphical results from HQSAR and topomer CoMFA were validated by the binding mode in the crystal structure.

The models may be beneficial to enhance the understanding of the structure–activity relationships for this class of compounds and also provide useful clues for the design of potential ATAD2 bromodomain inhibitors.

© 2022 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ccchem/10.2174/2666001602666220516093452
2022-08-15
2025-03-17

  • From This Site

    /content/journals/ccchem/10.2174/2666001602666220516093452
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. ChaikuadA. PetrosA.M. FedorovO. XuJ. KnappS. Structure-based approaches towards identification of fragments for the low-druggability ATAD2 bromodomain.MedChemComm20145121843184810.1039/C4MD00237G
     [Google Scholar]
  2. SniderJ. ThibaultG. HouryW.A. The AAA+ superfamily of functionally diverse proteins.Genome Biol.20089421610.1186/gb‑2008‑9‑4‑21618466635
     [Google Scholar]
  3. MorozumiY. BoussouarF. TanM. ChaikuadA. JamshidikiaM. ColakG. HeH. NieL. PetosaC. de DieuleveultM. CurtetS. VitteA.L. RabatelC. DebernardiA. CossetF.L. VerhoeyenE. EmadaliA. SchweiferN. GianniD. GutM. GuardiolaP. RousseauxS. GérardM. KnappS. ZhaoY. KhochbinS. Atad2 is a generalist facilitator of chromatin dynamics in embryonic stem cells.J. Mol. Cell Biol.20168434936210.1093/jmcb/mjv06026459632
     [Google Scholar]
  4. RevenkoA.S. KalashnikovaE.V. GemoA.T. ZouJ.X. ChenH.W. Chromatin loading of E2F-MLL complex by cancer-associated coregulator ANCCA via reading a specific histone mark.Mol. Cell. Biol.201030225260527210.1128/MCB.00484‑1020855524
     [Google Scholar]
  5. RaederM.B. BirkelandE. TrovikJ. KrakstadC. ShehataS. SchumacherS. ZackT.I. KrohnA. WernerH.M. MoodyS.E. WikE. StefanssonI.M. HolstF. OyanA.M. TamayoP. MesirovJ.P. KallandK.H. AkslenL.A. SimonR. BeroukhimR. SalvesenH.B. Integrated genomic analysis of the 8q24 amplification in endometrial cancers identifies ATAD2 as essential to MYC-dependent cancers.PLoS One201382e5487310.1371/journal.pone.005487323393560
     [Google Scholar]
  6. FouretR. LaffaireJ. HofmanP. Beau-FallerM. MazieresJ. ValidireP. GirardP. Camilleri-BröetS. VayletF. Leroy-LadurieF. SoriaJ.C. FouretP. A comparative and integrative approach identifies ATPase family, AAA domain containing 2 as a likely driver of cell proliferation in lung adenocarcinoma.Clin. Cancer Res.201218205606561610.1158/1078‑0432.CCR‑12‑050522914773
     [Google Scholar]
  7. TaghaviA. AkbariM.E. Hashemi-BahremaniM. NafissiN. KhalilnezhadA. PoorhosseiniS.M. Hashemi-GorjiF. YassaeeV.R. Gene expression profiling of the 8q22-24 position in human breast cancer: TSPYL5, MTDH, ATAD2 and CCNE2 genes are implicated in oncogenesis, while WISP1 and EXT1 genes may predict a risk of metastasis.Oncol. Lett.20161253845385510.3892/ol.2016.521827895739
     [Google Scholar]
  8. WanW.N. ZhangY.X. WangX.M. LiuY.J. ZhangY.Q. QueY.H. ZhaoW.J. ATAD2 is highly expressed in ovarian carcinomas and indicates poor prognosis.Asian Pac. J. Cancer Prev.20141562777278310.7314/APJCP.2014.15.6.277724761900
     [Google Scholar]
  9. WangH.L. ZhouP.Y. ZhangY. LiuP. Relationships between abnormal MMP2 expression and prognosis in gastric cancer: A meta-analysis of cohort studies.Cancer Biother. Radiopharm.201429416617210.1089/cbr.2014.160824784911
     [Google Scholar]
  10. CoutoP.P. Bastos-RodriguesL. SchayekH. MeloF.M. LisboaR.G.C. MirandaD.M. VilhenaA. BaleA.E. FriedmanE. De MarcoL. Spectrum of germline mutations in smokers and non-smokers in Brazilian Non-Small-Cell Lung Cancer (NSCLC) patients.Carcinogenesis201738111112111810.1093/carcin/bgx08928968711
     [Google Scholar]
  11. ZhengL. LiT. ZhangY. GuoY. YaoJ. DouL. GuoK. Oncogene ATAD2 promotes cell proliferation, invasion and migration in cervical cancer.Oncol. Rep.20153352337234410.3892/or.2015.386725813398
     [Google Scholar]
  12. WuG. LiuH. HeH. WangY. LuX. YuY. XiaS. MengX. LiuY. miR-372 down-regulates the oncogene ATAD2 to influence hepatocellular carcinoma proliferation and metastasis.BMC Cancer201414110710.1186/1471‑2407‑14‑10724552534
     [Google Scholar]
  13. ChungC. ToughD.F. Bromodomains: A new target class for small molecule drug discovery.Drug Discov. Today Ther. Strateg.201292-3e111e12010.1016/j.ddstr.2011.12.002
     [Google Scholar]
  14. HussainM. ZhouY. SongY. HameedH.M.A. JiangH. TuY. ZhangJ. ATAD2 in cancer: A pharmacologically challenging but tractable target.Expert Opin. Ther. Targets2018221859610.1080/14728222.2018.140692129148850
     [Google Scholar]
  15. HarnerM.J. ChauderB.A. PhanJ. FesikS.W. Fragment-based screening of the bromodomain of ATAD2.J. Med. Chem.201457229687969210.1021/jm501035j25314628
     [Google Scholar]
  16. BamboroughP. ChungC.W. FurzeR.C. GrandiP. MichonA.M. SheppardR.J. BarnettH. DialloH. DixonD.P. DouaultC. JonesE.J. KaramshiB. MitchellD.J. PrinjhaR.K. RauC. WatsonR.J. WernerT. DemontE.H. Structure-based optimization of naphthyridones into potent ATAD2 bromodomain inhibitors.J. Med. Chem.201558156151617810.1021/acs.jmedchem.5b0077326230603
     [Google Scholar]
  17. SepehriB. RasouliZ. HassanzadehZ. GhavamiR. Molecular docking and QSAR analysis of naphthyridone derivatives as ATAD2 bromodomain inhibitors: Application of CoMFA, LS-SVM, and RBF neural network.Med. Chem. Res.201625122895290510.1007/s00044‑016‑1686‑8
     [Google Scholar]
  18. MoreU.A. PatelS. RahevarV. NoolviM.N. AminabhaviT.M. JoshiS.D. In silico ADME and QSAR studies on a set of coumarin derivatives as acetylcholinesterase inhibitors against Alzheimer’s disease: CoMFA, CoMSIA, topomer CoMFA, and HQSAR.Lett. Drug Des. Discov.202017668471210.2174/1570180816666190712095907
     [Google Scholar]
  19. Al-AttraqchiO.H.A. VenugopalaK.N. 2D- and 3D-QSAR Modeling of Imidazole-Based Glutaminyl Cyclase Inhibitors.Curr. Computeraided Drug Des.202016668269710.2174/157340991566619091815013631533602
     [Google Scholar]
  20. YuS. ZhouQ. ZhangX. JiaS. GanY. ZhangY. ShiJ. YuanJ. Hologram quantitative structure–activity relationship and topomer comparative molecular-field analysis to predict the affinities of azo dyes for cellulose fibers.Dyes Pigments2018153354310.1016/j.dyepig.2018.01.053
     [Google Scholar]
  21. DemontE.H. ChungC.W. FurzeR.C. GrandiP. MichonA.M. WellawayC. BarrettN. BridgesA.M. CraggsP.D. DialloH. DixonD.P. DouaultC. EmmonsA.J. JonesE.J. KaramshiB.V. LockeK. MitchellD.J. MouzonB.H. PrinjhaR.K. RobertsA.D. SheppardR.J. WatsonR.J. BamboroughP. Fragment-based discovery of low-micromolar ATAD2 bromodomain inhibitors.J. Med. Chem.201558145649567310.1021/acs.jmedchem.5b0077226155854
     [Google Scholar]
  22. YuS. YuanJ. ShiJ. RuanX. ZhangT. WangY. DuY. HQSAR and topomer CoMFA for predicting melanocortin-4 receptor binding affinities of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides.Chemom. Intell. Lab. Syst.2015146344110.1016/j.chemolab.2015.04.017
     [Google Scholar]
  23. KiraljR. FerreiraM.M.C. Basic validation procedures for regression models in QSAR and QSPR studies: theory and application.J. Braz. Chem. Soc.200920477078710.1590/S0103‑50532009000400021
     [Google Scholar]
  24. GolbraikhA. TropshaA. Beware of q2!J. Mol. Graph. Model.200220426927610.1016/S1093‑3263(01)00123‑111858635
     [Google Scholar]
  25. RoyR.K. DasR.N. AmbureP. AherR.B. Be aware of error measures. Further studies on validation of predictive QSAR models.Chemom. Intell. Lab. Syst.2016152183310.1016/j.chemolab.2016.01.008
     [Google Scholar]
  26. ShiL.M. FangH. TongW. WuJ. PerkinsR. BlairR.M. BranhamW.S. DialS.L. MolandC.L. SheehanD.M. QSAR models using a large diverse set of estrogens.J. Chem. Inf. Comput. Sci.200141118619510.1021/ci000066d11206373
     [Google Scholar]
  27. SchüürmannG. EbertR-U. ChenJ. WangB. KühneR. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean.J. Chem. Inf. Model.200848112140214510.1021/ci800253u18954136
     [Google Scholar]
  28. LinL.I. A concordance correlation coefficient to evaluate reproducibility.Biometrics198945125526810.2307/25320512720055
     [Google Scholar]
  29. LinL.I. Assay validation using the concordance correlation coefficient.Biometrics199248259960410.2307/2532314
     [Google Scholar]
  30. OjhaP.K. MitraI. Further exploring rm2 metrics for validation of QSPR models.Chemom. Intell. Lab. Syst.20111071194205
     [Google Scholar]
  31. RoyK. KarS. AmbureP. On a simple approach for determining applicability domain of QSAR models.Chemom. Intell. Lab. Syst.2015145222910.1016/j.chemolab.2015.04.013
     [Google Scholar]
  32. TongJ. ZhanP. BaiM. YaoT. Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three-dimensional quantitative structure-activity relationship, virtual screening, and docking simulations.J. Chemometr.201630952353610.1002/cem.2809
     [Google Scholar]
  33. YuanJ. PuY. YinL. Prediction of binding affinities of PCDDs, PCDFs and PCBs using docking-based comparative molecular similarity indices analysis.Environ. Toxicol. Pharmacol.20143811710.1016/j.etap.2014.04.01924858058
     [Google Scholar]
  34. ZhouY. HussainM. KuangG. ZhangJ. TuY. Mechanistic insights into peptide and ligand binding of the ATAD2-bromodomain via atomistic simulations disclosing a role of induced fit and conformational selection.Phys. Chem. Chem. Phys.20182036232222323210.1039/C8CP03860K30137066
     [Google Scholar]
/content/journals/ccchem/10.2174/2666001602666220516093452
Loading
Hologram QSAR and Topomer CoMFA Study on Naphthyridone Derivatives as ATAD2 Bromodomain Inhibitors
Curr Chin Chem 2, e160522204804 (2022); https://doi.org/10.2174/2666001602666220516093452
/content/journals/ccchem/10.2174/2666001602666220516093452
/content/journals/ccchem/10.2174/2666001602666220516093452
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keyword(s): ATAD2 bromodomain inhibitors; contour map; hologram QSAR; naphthyridone derivatives; proliferation; topomer CoMFA

Most Cited Most Cited RSS feed

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