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2000
Volume 21, Issue 3
  • ISSN: 1573-4099
  • E-ISSN: 1875-6697

Abstract

Background

Translationally controlled tumour protein (TCTP) is associated with tumor diseases, such as breast cancer, and its inhibitor can reduce the growth of tumor cells. Unfortunately, there is currently no effective medication available for treating TCTP-related breast cancer.

Objectives

The objective of this study was to explore the inhibitor candidates among natural compounds for the treatment of breast cancer related to TCTP protein.

Methods

To explore the potential inhibitors of TCTP, we first screened out four potential inhibitors in the Traditional Chinese Medicine (TCM) for cancer based on AI virtual screening using the docking method, and then revealed the interaction mechanism of TCTP and four candidate inhibitors from TCM with molecular docking and molecular dynamics (MD) methods.

Results

Based on the conformational characteristics and the MD properties of the four leading compounds, we designed the new skeleton molecules with the AI method using MolAICal software. Our MD simulations have revealed that different small molecules bind to different sites of TCTP, but the flexible regions and the signaling pathways are almost the same, and the VDW and hydrophobic interactions are crucial in the interactions between TCTP and ligands.

Conclusion

We have proposed the candidate inhibitor of TCTP. Our study has provided a potential new method for exploring inhibitors from Traditional Chinese Medicine (TCM).

© 2025 Bentham Science Publishers
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References

  1. KantarjianH.M. PratF. SteensmaD.P. KurzrockR. StewartD.J. SekeresM.A. LevequeJ. Cancer research in the United States: A critical review of current status and proposal for alternative models.Cancer2018124142881288910.1002/cncr.31522 29757456
     [Google Scholar]
  2. ZhongF. XingJ. LiX. LiuX. FuZ. XiongZ. LuD. WuX. ZhaoJ. TanX. LiF. LuoX. LiZ. ChenK. ZhengM. JiangH. Artificial intelligence in drug design.Sci. China Life Sci.201861101191120410.1007/s11427‑018‑9342‑2 30054833
     [Google Scholar]
  3. BredaA. BassoL. SantosD. de AzevedoW.Jr Virtual screening of drugs: Score functions, docking, and drug design.Curr. Computeraided Drug Des.20084426527210.2174/157340908786786047
     [Google Scholar]
  4. YangX. WangY. ByrneR. SchneiderG. YangS. Concepts of artificial intelligence for computer-assisted drug discovery.Chem. Rev.201911918105201059410.1021/acs.chemrev.8b00728 31294972
     [Google Scholar]
  5. BatoolM. AhmadB. ChoiS. A structure-based drug discovery paradigm.Int. J. Mol. Sci.20192011278310.3390/ijms20112783 31174387
     [Google Scholar]
  6. SmalleyE. AI-powered drug discovery captures pharma interest.Nat. Biotechnol.201735760460510.1038/nbt0717‑604 28700560
     [Google Scholar]
  7. MakK.K. PichikaM.R. Artificial intelligence in drug development: Present status and future prospects.Drug Discov. Today201924377378010.1016/j.drudis.2018.11.014 30472429
     [Google Scholar]
  8. SunY. JiaoY. ShiC. ZhangY. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Comput. Struct. Biotechnol. J.2022205014502710.1016/j.csbj.2022.09.002 36091720
     [Google Scholar]
  9. Jiménez-LunaJ. GrisoniF. WeskampN. SchneiderG. Artificial intelligence in drug discovery: Recent advances and future perspectives.Expert Opin. Drug Discov.202116994995910.1080/17460441.2021.1909567 33779453
     [Google Scholar]
  10. PungpoP. PunkvangA. SaparpakornP. WolschannP. HannongbuaS. Recent advances in NNRTI Design: Computer-aided molecular design approaches.Curr. Computeraided Drug Des.20095317419910.2174/157340909789054685
     [Google Scholar]
  11. HongR. XuB. Breast cancer: An up‐to‐date review and future perspectives.Cancer Commun.2022421091393610.1002/cac2.12358 36074908
     [Google Scholar]
  12. GiaquintoA.N. SungH. MillerK.D. KramerJ.L. NewmanL.A. MinihanA. JemalA. SiegelR.L. Breast cancer statistics, 2022.CA Cancer J. Clin.202272652454110.3322/caac.21754 36190501
     [Google Scholar]
  13. WaksA.G. WinerE.P. Breast cancer treatment.JAMA2019321328830010.1001/jama.2018.19323 30667505
     [Google Scholar]
  14. McPhersonL. CochraneS. ZhuX. Current usage of traditional chinese medicine in the management of breast cancer.Integr. Cancer Ther.201615333534210.1177/1534735415607656 26420777
     [Google Scholar]
  15. YangZ. ZhangQ. YuL. ZhuJ. CaoY. GaoX. The signaling pathways and targets of traditional Chinese medicine and natural medicine in triple-negative breast cancer.J. Ethnopharmacol.202126411324910.1016/j.jep.2020.113249 32810619
     [Google Scholar]
  16. LeeY.W. ChenT.L. ShihY.R.V. TsaiC.L. ChangC.C. LiangH.H. TsengS.H. ChienS.C. WangC.C. Adjunctive traditional Chinese medicine therapy improves survival in patients with advanced breast cancer: A population‐based study.Cancer201412091338134410.1002/cncr.28579 24496917
     [Google Scholar]
  17. WangS. LinH. CongW. Chinese medicines improve perimenopausal symptoms induced by surgery, chemoradiotherapy, or endocrine treatment for breast cancer.Front. Pharmacol.20191017410.3389/fphar.2019.00174 30930771
     [Google Scholar]
  18. AmsonR. PeceS. LespagnolA. VyasR. MazzarolG. TosoniD. ColalucaI. VialeG. Rodrigues-FerreiraS. WynendaeleJ. ChaloinO. HoebekeJ. MarineJ.C. Di FioreP.P. TelermanA. Reciprocal repression between P53 and TCTP.Nat. Med.2012181919910.1038/nm.2546 22157679
     [Google Scholar]
  19. MiaoX. ChenY.B. XuS.L. ZhaoT. LiuJ.Y. LiY.R. WangJ. ZhangJ. GuoG.Z. TCTP overexpression is associated with the development and progression of glioma.Tumour Biol.20133463357336110.1007/s13277‑013‑0906‑9 23749504
     [Google Scholar]
  20. ZhangF. LiuB. WangZ. YuX.J. NiQ.X. YangW.T. MukaidaN. LiY.Y. A novel regulatory mechanism of Pim-3 kinase stability and its involvement in pancreatic cancer progression.Mol. Cancer Res.201311121508152010.1158/1541‑7786.MCR‑13‑0389 24165482
     [Google Scholar]
  21. LucibelloM. GambacurtaA. ZonfrilloM. PierimarchiP. SerafinoA. RasiG. RubartelliA. GaraciE. TCTP is a critical survival factor that protects cancer cells from oxidative stress-induced cell-death.Exp. Cell Res.2011317172479248910.1016/j.yexcr.2011.07.012 21801721
     [Google Scholar]
  22. FujitaT. FelixK. PinkaewD. Hutadilok-TowatanaN. LiuZ. FujiseK. Human fortilin is a molecular target of dihydroartemisinin.FEBS Lett.200858271055106010.1016/j.febslet.2008.02.055 18325342
     [Google Scholar]
  23. ChanT.H.M. ChenL. GuanX.Y. Role of translationally controlled tumor protein in cancer progression.Biochem. Res. Int.201220121510.1155/2012/369384 22570787
     [Google Scholar]
  24. AcunzoJ. BaylotV. SoA. RocchiP. TCTP as therapeutic target in cancers.Cancer Treat. Rev.201440676076910.1016/j.ctrv.2014.02.007 24650927
     [Google Scholar]
  25. Telerman, A.; Amson, R., Eds.; Results and Problems in Cell Differentiation; TCTP/tpt1-Remodeling Signaling from Stem Cell to DiseaseSpringer International Publishing:Cham20176410.1007/978‑3‑319‑67591‑6
     [Google Scholar]
  26. DongX. TangY. ZhanC. WeiG. Green tea extract EGCG plays a dual role in Aβ42 protofibril disruption and membrane protection: A molecular dynamic study.Chem. Phys. Lipids202123410502410.1016/j.chemphyslip.2020.105024 33278382
     [Google Scholar]
  27. JiangY. GaoH. TurduG. Traditional Chinese medicinal herbs as potential AChE inhibitors for anti-Alzheimer’s disease: A review.Bioorg. Chem.201775506110.1016/j.bioorg.2017.09.004 28915465
     [Google Scholar]
  28. KozÖ. EkinciD. PerroneA. PiacenteS. Alankuş-ÇalışkanÖ. BedirE. SupuranC.T. Analysis of saponins and phenolic compounds as inhibitors of α-carbonic anhydrase isoenzymes.J. Enzyme Inhib. Med. Chem.201328241241710.3109/14756366.2011.651464 22299585
     [Google Scholar]
  29. JiangH. FanC. LuY. CuiX. LiuJ. Astragaloside regulates lncRNA LOC100912373 and the miR 17 5p/PDK1 axis to inhibit the proliferation of fibroblast like synoviocytes in rats with rheumatoid arthritis.Int. J. Mol. Med.202148113010.3892/ijmm.2021.4963 34013364
     [Google Scholar]
  30. WangH. YuanR. CaoQ. WangM. RenD. HuangX. WuM. ZhangL. ZhaoX. HuoX. PanY. LiuQ. Astragaloside III activates TACE/ADAM17‐dependent anti‐inflammatory and growth factor signaling in endothelial cells in a p38‐dependent fashion.Phytother. Res.20203451096110710.1002/ptr.6603 32197276
     [Google Scholar]
  31. KagaleS. MarimuthuT. ThayumanavanB. NandakumarR. SamiyappanR. Antimicrobial activity and induction of systemic resistance in rice by leaf extract of Datura metel against Rhizoctonia solani and Xanthomonas oryzae pv. oryzae.Physiol. Mol. Plant Pathol.20046529110010.1016/j.pmpp.2004.11.008
     [Google Scholar]
  32. ShihM.J. KućJ. α and β-solamarine in kennebec Solanum tuberosum leaves and aged tuber slices.Phytochemistry1974136997100010.1016/S0031‑9422(00)91436‑5
     [Google Scholar]
  33. WangY. HuangG. ShiY. TianW. ZhuangC. ChenE. Asymmetric synthesis of (−)-solanidine and (−)-tomatidenol.Org. Biomol. Chem.202016
     [Google Scholar]
  34. LiangQ. MaJ. MaZ. WangY. TanH. XiaoC. LiuM. LuB. ZhangB. GaoY. Chemical comparison of dried rehmannia root and prepared rehmannia root by UPLC-TOF MS and HPLC-ELSD with multivariate statistical analysis.Acta Pharm. Sin. B201331556410.1016/j.apsb.2012.11.001
     [Google Scholar]
  35. ThuV.K. ThoaN.T. HienN.T.T. HangD.T.T. KiemP.V. Iridoid glycosides link with phenylpropanoids from Rehmannia glutinosa: Natural Product Research.Nat. Prod. Res.2021May1610.1080/14786419.2021.1931189 34039230
     [Google Scholar]
  36. PettersenE.F. GoddardT.D. HuangC.C. CouchG.S. GreenblattD.M. MengE.C. FerrinT.E. UCSF Chimera-a visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.20084 15264254
     [Google Scholar]
  37. ZhaoS. ZhuY.Y. WangX.Y. LiuY.S. SunY.X. ZhaoQ.J. LiH.Y. Structural insight into the interactions between structurally similar inhibitors and SIRT6.Int. J. Mol. Sci.2020217260110.3390/ijms21072601 32283646
     [Google Scholar]
  38. LucibelloM. AdantiS. AntelmiE. DeziD. CiafrèS. CarcangiuM.L. ZonfrilloM. NicoteraG. SicaL. De BraudF. PierimarchiP. Phospho-TCTP as a therapeutic target of dihydroartemisinin for aggressive breast cancer cells.Oncotarget2015675275529110.18632/oncotarget.2971 25779659
     [Google Scholar]
  39. D’AmicoS. KrasnowskaE. ManniI. ToiettaG. BaldariS. PiaggioG. RanalliM. GambacurtaA. VernieriC. Di GiacintoF. BernassolaF. de BraudF. LucibelloM. DHA affects microtubule dynamics through reduction of phospho-TCTP levels and enhances the antiproliferative effect of T-DM1 in Trastuzumab-Resistant HER2-positive breast cancer cell lines.Cells202095126010.3390/cells9051260 32438775
     [Google Scholar]
  40. SeoE-J. FischerN. EfferthT. Role of TCTP for cellular differentiation and cancer therapy.Results Probl. Cell Differ.20176426328110.1007/978‑3‑319‑67591‑6_14
     [Google Scholar]
  41. JiaK. Traditional Chinese medicine cancer tumor treatment.Shanxi Zhong Yi19898
     [Google Scholar]
  42. ChenC.Y.C. TCM Database@Taiwan: The world’s largest traditional Chinese medicine database for drug screening in silico.PLoS One201161e1593910.1371/journal.pone.0015939 21253603
     [Google Scholar]
  43. BaiQ. Research and development of MolAICal for drug design via deep learning and classical programming.Preprint2020
     [Google Scholar]
  44. EberhardtJ. Santos-MartinsD. TillackA.F. ForliS. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings.J. Chem. Inf. Model.202161838913898
     [Google Scholar]
  45. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.2010312455461
     [Google Scholar]
  46. RudrapalM. CelikI. KhanJ. AnsariM.A. AlomaryM.N. AlatawiF.A. YadavR. SharmaT. TalleiT.E. PasalaP.K. SahooR.K. KhairnarS.J. BendaleA.R. ZothantluangaJ.H. ChetiaD. WalodeS.G. Identification of bioactive molecules from Triphala (Ayurvedic herbal formulation) as potential inhibitors of SARS-CoV-2 main protease (Mpro) through computational investigations.J. King Saud Univ. Sci.202234310182610.1016/j.jksus.2022.101826 35035181
     [Google Scholar]
  47. 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]
  48. SchüttelkopfA.W. van AaltenD.M.F. PRODRG: A tool for high-throughput crystallography of protein–ligand complexes.Acta Crystallogr. D Biol. Crystallogr.20046081355136310.1107/S0907444904011679 15272157
     [Google Scholar]
  49. HessB. KutznerC. van der SpoelD. LindahlE. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation.J. Chem. Theory Comput.20084343544710.1021/ct700301q 26620784
     [Google Scholar]
  50. HumphreyW. DalkeA. SchultenK. VMD: Visual molecular dynamics.J. Mol. Graph.1996141333810.1016/0263‑7855(96)00018‑5
     [Google Scholar]
  51. PyMOL.An open-source molecular graphics tool.Available from: https://docslib.org/doc/9868345/pymol-an-open-source-molecular-graphics-tool
    [Google Scholar]
  52. Valdés-TresancoM.S. Valdés-TresancoM.E. ValienteP.A. MorenoE. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS.J. Chem. Theory Comput.202117106281629110.1021/acs.jctc.1c00645 34586825
     [Google Scholar]
  53. KuhnB. GerberP. Schulz-GaschT. StahlM. Validation and use of the MM-PBSA approach for drug discovery.J. Med. Chem.200548124040404810.1021/jm049081q 15943477
     [Google Scholar]
  54. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.1032936 25835573
     [Google Scholar]
  55. XuY. ZhengQ. YuL. ZhangH. SunC. A molecular dynamics and computational study of human KAT3 involved in KYN pathway.Sci. China Chem.201356451452310.1007/s11426‑012‑4802‑8
     [Google Scholar]
  56. BaiQ. MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.Brief. Bioinform.202022310.1093/bib/bbaa161
     [Google Scholar]
  57. Overview on the Rule of Five. Available from: https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/0471141755.ph0912s49 (Accessed on: Oct. 19, 2022).
  58. BaellJ.B. Holloway†, G.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.J. Med. Chem.201053727192740
     [Google Scholar]
  59. ShultzM.D. Two decades under the influence of the rule of five and the changing properties of approved oral drugs.J. Med. Chem.20196241701171410.1021/acs.jmedchem.8b00686 30212196
     [Google Scholar]
  60. ErtlP. SchuffenhauerA. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.J. Cheminformatics2009
     [Google Scholar]
  61. WillettP. Similarity-based virtual screening using 2D fingerprints.Drug Discov. Today20061123-241046105310.1016/j.drudis.2006.10.005 17129822
     [Google Scholar]
  62. DobiK. HajdúI. FlachnerB. FabóG. SzaszkóM. BognárM. MagyarC. SimonI. SziszD. LőrinczZ. CsehS. DormánG. Combination of 2D/3D ligand-based similarity search in rapid virtual screening from multimillion compound repositories. Selection and biological evaluation of potential PDE4 and PDE5 inhibitors.Molecules20141967008703910.3390/molecules19067008 24879613
     [Google Scholar]
  63. PatroS.G.K. SahuK.K. Normalization: A Preprocessing StagearXiv2015
     [Google Scholar]
  64. 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]
  65. KumariR. KumarV. DhankharP. DalalV. Promising antivirals for PLpro of SARS-CoV-2 using virtual screening, molecular docking, dynamics, and MMPBSA.J. Biomol. Struct. Dyn.20234110
     [Google Scholar]
  66. 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.20224020
     [Google Scholar]
  67. 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]
  68. EichhornT. WinterD. BücheleB. DirdjajaN. FrankM. LehmannW.D. MertensR. Krauth-SiegelR.L. SimmetT. GranzinJ. EfferthT. Molecular interaction of artemisinin with translationally controlled tumor protein (TCTP) of Plasmodium falciparum.Biochem. Pharmacol.2013851384510.1016/j.bcp.2012.10.006 23085438
     [Google Scholar]
  69. YangY. YangF. XiongZ. YanY. WangX. NishinoM. MirkovicD. NguyenJ. WangH. YangX.F. An N-terminal region of translationally controlled tumor protein is required for its antiapoptotic activity.Oncogene200524304778478810.1038/sj.onc.1208666 15870695
     [Google Scholar]
  70. FischerN. SeoE.J. AbdelfatahS. FleischerE. KlingerA. EfferthT. A novel ligand of the translationally controlled tumor protein (TCTP) identified by virtual drug screening for cancer differentiation therapy.Invest. New Drugs202139491492710.1007/s10637‑020‑01042‑w 33492639
     [Google Scholar]
  71. FunstonG. GohW. WeiS.J. TngQ.S. BrownC. Jiah TongL. VermaC. LaneD. GhadessyF. Binding of translationally controlled tumour protein to the N-terminal domain of HDM2 is inhibited by nutlin-3.PLoS One201278e4264210.1371/journal.pone.0042642 22912717
     [Google Scholar]
  72. KumarR. MauryaR. SaranS. Identification of novel inhibitors of the translationally controlled tumor protein (TCTP): Insights from molecular dynamics.Mol. Biosyst.201713351052410.1039/C6MB00850J 28128835
     [Google Scholar]
  73. WangX. SongM. ZhaoS. LiH. ZhaoQ. ShenJ. Molecular dynamics simulations reveal the mechanism of the interactions between the inhibitors and SIRT2 at atom level.Mol. Simul.202046863864910.1080/08927022.2020.1757093
     [Google Scholar]
  74. ZhangY. ChenL. WangX. ZhuY. LiuY. LiH. ZhaoQ. Interactive mechanism of potential inhibitors with glycosyl for SARS-CoV-2 by molecular dynamics simulation.Processes2021910174910.3390/pr9101749
     [Google Scholar]
  75. ChenL. ZhaoS. ZhuY. LiuY. LiH. ZhaoQ. Molecular dynamics simulations reveal the modulated mechanism of STING conformation.Interdiscip. Sci.202113475176510.1007/s12539‑021‑00446‑3 34142362
     [Google Scholar]
  76. LiuZ. ZouY. ZhangQ. ChenP. LiuY. QianZ. Distinct binding dynamics, sites and interactions of fullerene and fullerenols with Amyloid-β peptides revealed by molecular dynamics simulations.Int. J. Mol. Sci.2019208204810.3390/ijms20082048 31027286
     [Google Scholar]
  77. QianZ. ZhuL. LjuH. SunG. Potential mechanisms of several promising small molecules disrupting fibrillar oligomer of tau fragments revealed by molecular dynamics simulation.Biophys. J.20221213189a190a10.1016/j.bpj.2021.11.1783
     [Google Scholar]
  78. PopovaM. IsayevO. TropshaA. Deep reinforcement learning for de novo drug design.Sci. Adv.201847eaap788510.1126/sciadv.aap7885 30050984
     [Google Scholar]
  79. PrykhodkoO. JohanssonS.V. KotsiasP.C. Arús-PousJ. BjerrumE.J. EngkvistO. ChenH. A de novo molecular generation method using latent vector based generative adversarial network.J. Cheminform.20191117410.1186/s13321‑019‑0397‑9 33430938
     [Google Scholar]
  80. MerkD. FriedrichL. GrisoniF. SchneiderG. De Novo design of bioactive small molecules by artificial intelligence.Mol. Inform.2018371-2170015310.1002/minf.201700153 29319225
     [Google Scholar]
  81. ZhavoronkovA. IvanenkovY.A. AliperA. VeselovM.S. AladinskiyV.A. AladinskayaA.V. TerentievV.A. PolykovskiyD.A. KuznetsovM.D. AsadulaevA. VolkovY. ZholusA. ShayakhmetovR.R. ZhebrakA. MinaevaL.I. ZagribelnyyB.A. LeeL.H. SollR. MadgeD. XingL. GuoT. Aspuru-GuzikA. Deep learning enables rapid identification of potent DDR1 kinase inhibitors.Nat. Biotechnol.20193791038104010.1038/s41587‑019‑0224‑x 31477924
     [Google Scholar]
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AI-based Virtual Screening of Traditional Chinese Medicine and the Discovery of Novel Inhibitors of TCTP
Curr. Computeraided Drug Des. 21, 362 (2025); https://doi.org/10.2174/0115734099277605231218071503
/content/journals/cad/10.2174/0115734099277605231218071503
/content/journals/cad/10.2174/0115734099277605231218071503
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  • Article Type:     Research Article
Keyword(s): AI drug design; AI-based virtual screening; molecular docking; molecular dynamics simulation; TCM; TCTP

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