Skip to content
2000
Volume 25, Issue 26
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Introduction

Hyperpigmentation disorders are caused by excess production of the pigment melanin, catalyzed by the enzyme tyrosinase. Novel tyrosinase inhibitors are needed as therapeutic agents to treat these conditions.

Methods

To discover new inhibitors, we performed a virtual screening of the ZINC20 library containing 1.4 billion compounds. An initial filter for drug-likeness, ADMET properties, and synthetic accessibility reduced the library to 10,217 hits. Quantitative structure-activity relationship (QSAR) modeling of this subset predicted nanomolar inhibitory potency for several chemical scaffolds. Comparative molecular docking studies and rigorous binding energy calculations further prioritized four cysteine-containing dipeptide compounds based on predicted strong binding affinity and mode to tyrosinase.

Results

Microsecond-long molecular dynamics simulations provided additional atomistic insights into the stability of inhibitor-enzyme binding interactions. This integrated computational workflow effectively sampled an extremely large chemical space to discover four novel tyrosinase inhibitors with half-maximal inhibitory concentration values below 10 nM.

Conclusion

Overall, this demonstrates the power of virtual screening and multi-faceted computational techniques to accelerate the discovery of potent bioactive ligands from massive compound libraries by efficiently sampling chemical space.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266333084240918051716
2024-10-02
2025-12-15

  • From This Site

    /content/journals/ctmc/10.2174/0115680266333084240918051716
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. HearingV.J. JiménezM. Mammalian tyrosinase — The critical regulatory control point in melanocyte pigmentation.Int. J. Biochem.198719121141114710.1016/0020‑711X(87)90095‑43125075
     [Google Scholar]
  2. LinJ.Y. FisherD.E. Melanocyte biology and skin pigmentation.Nature2007445713084385010.1038/nature0566017314970
     [Google Scholar]
  3. KumariS. ThngS. VermaN. GautamH. Melanogenesis Inhibitors.Acta Derm. Venereol.2018981092493110.2340/00015555‑300229972222
     [Google Scholar]
  4. PillaiyarT. ManickamM. NamasivayamV. Skin whitening agents: Medicinal chemistry perspective of tyrosinase inhibitors.J. Enzyme Inhib. Med. Chem.201732140342510.1080/14756366.2016.125688228097901
     [Google Scholar]
  5. Casanola-MartinG. Le-Thi-ThuH. Marrero-PonceY. Castillo-GaritJ. TorrensF. RescignoA. AbadC. KhanM. Tyrosinase enzyme: 1. An overview on a pharmacological target.Curr. Top. Med. Chem.201414121494150110.2174/156802661466614052312142724853560
     [Google Scholar]
  6. NevesB.J. DantasR.F. SengerM.R. Melo-FilhoC.C. ValenteW.C.G. de AlmeidaA.C.M. Rezende-NetoJ.M. LimaE.F.C. PaveleyR. FurnhamN. MuratovE. KamentskyL. CarpenterA.E. BragaR.C. Silva-JuniorF.P. AndradeC.H. Discovery of new anti-schistosomal hits by integration of QSAR-based virtual screening and high content screening.J. Med. Chem.201659157075708810.1021/acs.jmedchem.5b0203827396732
     [Google Scholar]
  7. SabeV.T. NtombelaT. JhambaL.A. MaguireG.E.M. GovenderT. NaickerT. KrugerH.G. Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review.Eur. J. Med. Chem.202122411370510.1016/j.ejmech.2021.11370534303871
     [Google Scholar]
  8. IrwinJ.J. TangK.G. YoungJ. DandarchuluunC. WongB.R. KhurelbaatarM. MorozY.S. MayfieldJ. SayleR.A. ZINC20 — A free ultralarge-scale chemical database for ligand discovery.J. Chem. Inf. Model.202060126065607310.1021/acs.jcim.0c0067533118813
     [Google Scholar]
  9. JiaC.Y. LiJ.Y. HaoG.F. YangG.F. A drug-likeness toolbox facilitates ADMET study in drug discovery.Drug Discov. Today202025124825810.1016/j.drudis.2019.10.01431705979
     [Google Scholar]
  10. PollastriM.P. Overview on the rule of five.Current Protocols in PharmacologyWiley201010.1002/0471141755.ph0912s49
     [Google Scholar]
  11. NorinderU. BergströmC.A.S. Prediction of ADMET properties.ChemMedChem20061992093710.1002/cmdc.200600155
     [Google Scholar]
  12. BhalS.K. KassamK. PeirsonI.G. PearlG.M. The Rule of Five Revisited: Applying log D in place of log P in drug-likeness filters.Mol. Pharmaceutics20074455656010.1002/cmdc.200600155
     [Google Scholar]
  13. BaellJ.B. HollowayG.A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.J. Med. Chem.20105372719274010.1021/jm901137j20131845
     [Google Scholar]
  14. PatilV.M. GuptaS.P. MasandM. BalasubramanianK. Experimental and computational models to understand protein-ligand, metal-ligand and metal-DNA interactions pertinent to targeted cancer and other therapies.Eur. J. Med. Chem.2024102772417410.1016/j.ejmcr.2024.100133
     [Google Scholar]
  15. KarplusM. PetskoG.A. Molecular dynamics simulations in biology.Nature1990347629463163910.1038/347631a02215695
     [Google Scholar]
  16. LiX. PanF. YangZ. GaoF. LiJ. ZhangF. Construction of QSAR model based on cysteine-containing dipeptides and screening of natural tyrosinase inhibitors.J. Food Biochem.20224610e1433810.1111/jfbc.14338
     [Google Scholar]
  17. ChengA. MerzK.M. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships.J. Med. Chem.200346173572358010.1021/jm020266b12904062
     [Google Scholar]
  18. EganW.J. LauriG. Prediction of intestinal permeability.Adv. Drug Deliv. Rev.200254327328910.1016/S0169‑409X(02)00004‑211922948
     [Google Scholar]
  19. EganW.J. MerzK.M.Jr BaldwinJ.J. Prediction of drug absorption using multivariate statistics.J. Med. Chem.200043213867387710.1021/jm000292e11052792
     [Google Scholar]
  20. SusnowR.G. DixonS.L. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition.J. Chem. Inf. Comput. Sci.20034341308131510.1021/ci030283p12870924
     [Google Scholar]
  21. ChengA. DixonS.L. In silico models for the prediction of dose-dependent human hepatotoxicity.J. Comput. Aided Mol. Des.2003171281182310.1023/B:JCAM.0000021834.50768.c615124930
     [Google Scholar]
  22. VotanoJ.R. ParhamM. HallL.M. HallL.H. KierL.B. OloffS. TropshaA. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.J. Med. Chem.200649247169718110.1021/jm051245v17125269
     [Google Scholar]
  23. BaiQ. TanS. XuT. LiuH. HuangJ. YaoX. MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.Brief. Bioinform.2021223bbaa16110.1093/bib/bbaa16132778891
     [Google Scholar]
  24. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑234265844
     [Google Scholar]
  25. ConwayP. TykaM.D. DiMaioF. KonerdingD.E. BakerD. Relaxation of backbone bond geometry improves protein energy landscape modeling.Protein Sci.2014231475510.1002/pro.238924265211
     [Google Scholar]
  26. LatourR.A. Molecular simulation of protein-surface interactions: Benefits, problems, solutions, and future directions (Review).Biointerphases200833FC2FC1210.1116/1.296513219809597
     [Google Scholar]
  27. 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]
  28. MaierJ.A. MartinezC. KasavajhalaK. WickstromL. HauserK.E. SimmerlingC. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB.J. Chem. Theory Comput.20151183696371310.1021/acs.jctc.5b0025526574453
     [Google Scholar]
  29. WangJ. WolfR.M. CaldwellJ.W. KollmanP.A. CaseD.A. Development and testing of a general amber force field.J. Comput. Chem.20042591157117410.1002/jcc.2003515116359
     [Google Scholar]
  30. PanF. LiJ. ZhaoL. TuersuntuohetiT. MehmoodA. ZhouN. HaoS. WangC. GuoY. LinW. A molecular docking and molecular dynamics simulation study on the interaction between cyanidin-3-O ‐glucoside and major proteins in cow’s milk.J. Food Biochem.2021451e1357010.1111/jfbc.1357033222207
     [Google Scholar]
  31. DillerD.J. MerzK.M.Jr High throughput docking for library design and library prioritization.Proteins200143211312411276081
     [Google Scholar]
  32. DillerD.J. LiR. Kinases, homology models, and high throughput docking.J. Med. Chem.200346224638464710.1021/jm020503a14561083
     [Google Scholar]
  33. RaoS.N. HeadM.S. KulkarniA. LaLondeJ.M. Validation studies of the site-directed docking program LibDock.J. Chem. Inf. Model.20074762159217110.1021/ci600429917985863
     [Google Scholar]
  34. WuG. RobertsonD.H. BrooksC.L.III ViethM. Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm.J. Comput. Chem.200324131549156210.1002/jcc.1030612925999
     [Google Scholar]
  35. WuH. LiuY. GuoM. XieJ. JiangX. A virtual screening method for inhibitory peptides of Angiotensin I-converting enzyme.J. Food Sci.2014799C1635C164210.1111/1750‑3841.1255925154376
     [Google Scholar]
/content/journals/ctmc/10.2174/0115680266333084240918051716
Loading
Identification of Novel Tyrosinase Inhibitors with Nanomolar Potency Using Virtual Screening Approaches
Curr. Top. Med. Chem. 25, 3056 (2025); https://doi.org/10.2174/0115680266333084240918051716
/content/journals/ctmc/10.2174/0115680266333084240918051716
/content/journals/ctmc/10.2174/0115680266333084240918051716
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): molecular docking; molecular dynamics simulation; QSAR, Hyperpigmentation disorders, ZINC20; Tyrosinase

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