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Volume 21, Issue 16
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

The histone deacetylase family of proteins, which includes the sirtuins, participates in a wide range of cellular processes, and is intimately involved in neurodegenerative illnesses. The research on sirtuins has garnered a lot of interest. However, there are currently no effective therapeutic drugs.

Methods

In order to explore the potential inhibitors of SIRTs, we first screened four potential lead compounds of SIRT2 in Traditional Chinese Medicine (TCM) for nervous disease using the AutoDock Vina method. Then, with Molecular Dynamics (MD) simulation method, we discovered how these inhibitors from Traditional Chinese herbal medicines affect this protein at the atomic level.

Results and Discussion

We found hydrophobic interactions between inhibitors and SIRT2 to be crucial. The small molecules have been found to have strong effect on the residues in the zinc-binding domain, exhibiting relationship with the signaling pathway. Finally, based on the conformational characteristics and the MD properties of the four potential inhibitors in TCM, we have designed the new skeleton molecules according to the parameters of binding energy, fingerprint similarity, 3D similarity, and RO5, with AI method using MolAICal software.

Conclusion

We have proposed the candidate inhibitor of SIRT2. Our research has provided a new approach that can be used to explore potential inhibitors from TCM. This could potentially pave the way for the creation of effective medicines.

© 2024 Bentham Science Publishers
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2024-12-23

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References

  1. HaigisM.C. SinclairD.A. Mammalian sirtuins: Biological insights and disease relevance.Annu. Rev. Pathol.20105125329510.1146/annurev.pathol.4.110807.092250 20078221
     [Google Scholar]
  2. JiZ. LiuG.H. QuJ. Mitochondrial sirtuins, metabolism, and aging.J. Genet. Genomics202249428729810.1016/j.jgg.2021.11.005 34856390
     [Google Scholar]
  3. KitadaM. OguraY. MonnoI. KoyaD. Sirtuins and type 2 diabetes: Role in inflammation, oxidative stress, and mitochondrial function.Front. Endocrinol.20191018710.3389/fendo.2019.00187 30972029
     [Google Scholar]
  4. NakagawaT. GuarenteL. Sirtuins at a glance.J. Cell Sci.2011124683383810.1242/jcs.081067 21378304
     [Google Scholar]
  5. ManjulaR. AnujaK. AlcainF.J. SIRT1 and SIRT2 activity control in neurodegenerative diseases.Front. Pharmacol.20211158582110.3389/fphar.2020.585821 33597872
     [Google Scholar]
  6. YanJ. ZhangP. TanJ. LiM. XuX. ShaoX. FangF. ZouZ. ZhouY. TianB. Cdk5 phosphorylation-induced SIRT2 nuclear translocation promotes the death of dopaminergic neurons in Parkinson’s disease.NPJ Parkinsons Dis.2022814610.1038/s41531‑022‑00311‑0 35443760
     [Google Scholar]
  7. ZhouW. NiT.K. WronskiA. GlassB. SkibinskiA. BeckA. KuperwasserC. The SIRT2 deacetylase stabilizes slug to control malignancy of basal-like breast cancer.Cell Rep.20161751302131710.1016/j.celrep.2016.10.006 27783945
     [Google Scholar]
  8. ChenG. HuangP. HuC. The role of SIRT2 in cancer: A novel therapeutic target.Int. J. Cancer2020147123297330410.1002/ijc.33118 32449165
     [Google Scholar]
  9. ZhouS. TangX. ChenH.Z. Sirtuins and insulin resistance.Front. Endocrinol.2018974810.3389/fendo.2018.00748 30574122
     [Google Scholar]
  10. PreseguéB.L. VaqueroA. The dual role of sirtuins in cancer.Genes Cancer20112664866210.1177/1947601911417862 21941620
     [Google Scholar]
  11. LiS. LvX. ZhaiK. XuR. ZhangY. ZhaoS. QinX. YinL. LouJ. MicroRNA-7 inhibits neuronal apoptosis in a cellular Parkinson’s disease model by targeting Bax and Sirt2.Am. J. Transl. Res.2016829931004 27158385
     [Google Scholar]
  12. SongM. ZhuY. WeiG. LiH. Carbon nanotube prevents the secondary structure formation of amyloid-β trimers: An all-atom molecular dynamics study.Mol. Simul.20174313-161189119510.1080/08927022.2017.1321757
     [Google Scholar]
  13. PalomerE. FloresM.N. JollyS. VargasP.P. BenvegnùS. PodpolnyM. TeoS. VaherK. SaitoT. SaidoT.C. WhitingP. SalinasP.C. Epigenetic repression of Wnt receptors in AD: A role for Sirtuin2-induced H4K16ac deacetylation of Frizzled1 and Frizzled7 promoters.Mol. Psychiatry20222773024303310.1038/s41380‑022‑01492‑z 35296808
     [Google Scholar]
  14. DonmezG. OuteiroT.F. SIRT1 and SIRT2: Emerging targets in neurodegeneration.EMBO Mol. Med.20135334435210.1002/emmm.201302451 23417962
     [Google Scholar]
  15. Sola-SevillaN. PuertaE. SIRT2 as a potential new therapeutic target for Alzheimer’s disease.Neural Regen. Res.202419112413110.4103/1673‑5374.375315 37488853
     [Google Scholar]
  16. SevillaS.N. LombardoM.A. AleixoM. ExpósitoS. PerdigónD.T. AzquetaA. ZamaniF. SuzukiT. MaioliS. EroliF. MattonA. RamírezM.J. SolasM. TorderaR.M. MartínE.D. PuertaE. SIRT2 inhibition rescues neurodegenerative pathology but increases systemic inflammation in a transgenic mouse model of alzheimer’s disease.J. Neuroimmune Pharmacol.2023183529550
     [Google Scholar]
  17. RumpfT. SchiedelM. KaramanB. RoesslerC. NorthB.J. LehotzkyA. OláhJ. LadweinK.I. SchmidtkunzK. GajerM. PannekM. SteegbornC. SinclairD.A. GerhardtS. OvádiJ. SchutkowskiM. SipplW. EinsleO. JungM. Selective Sirt2 inhibition by ligand-induced rearrangement of the active site.Nat. Commun.201561626310.1038/ncomms7263 25672491
     [Google Scholar]
  18. YamagataK. GotoY. NishimasuH. MorimotoJ. IshitaniR. DohmaeN. TakedaN. NagaiR. KomuroI. SugaH. NurekiO. Structural basis for potent inhibition of SIRT2 deacetylase by a macrocyclic peptide inducing dynamic structural change.Structure201422234535210.1016/j.str.2013.12.001 24389023
     [Google Scholar]
  19. HuangS. SongC. WangX. ZhangG. WangY. JiangX. SunQ. HuangL. XiangR. HuY. LiL. YangS. Discovery of new SIRT2 inhibitors by utilizing a consensus docking/scoring strategy and structure–activity relationship analysis.J. Chem. Inf. Model.201757466967910.1021/acs.jcim.6b00714 28301150
     [Google Scholar]
  20. TaoS ChenG YangM DengS ZhangJ GuoD Identification of the major constituents in Shi-Quan-Da-Bu decoction by HPLC-ESI-MS/MS.Nat. Prod. Commun.200831934578X0800300
     [Google Scholar]
  21. Zee-ChengR.K. Shi-quan-da-bu-tang (ten significant tonic decoction), SQT. A potent Chinese biological response modifier in cancer immunotherapy, potentiation and detoxification of anticancer drugs.Methods Find. Exp. Clin. Pharmacol.1992149725736 1294861
     [Google Scholar]
  22. PengW. HanT. XinW.B. ZhangX.G. ZhangQ.Y. JiaM. QinL.P. Comparative research of chemical constituents and bioactivities between petroleum ether extracts of the aerial part and the rhizome of Atractylodes macrocephala.Med. Chem. Res.201120214615110.1007/s00044‑010‑9311‑8
     [Google Scholar]
  23. KumW.F. DurairajanS.S.K. BianZ.X. ManS.C. LamY.C. XieL.X. LuJ.H. WangY. HuangX.Z. LiM. Treatment of idiopathic Parkinson’s disease with traditional chinese herbal medicine: A randomized placebo-controlled pilot clinical study.Evid. Based Complement. Alternat. Med.201120111810.1093/ecam/nep116 19692449
     [Google Scholar]
  24. ChaoW.W. LinB.F. Bioactivities of major constituents isolated from Angelica sinensis (Danggui).Chin. Med.2011612910.1186/1749‑8546‑6‑29 21851645
     [Google Scholar]
  25. Effects of fuling shengmai yin on superoxide dismutase, malondialdehyde and lipofuscin level of rat hippocampal neurons in vitro 2011Available from: https://www.researchgate.net/publication/296747054_Effects_of_fuling_shengmai_yin_on_ superoxide_ dismutase_malondialdehyde_and_lipofuscin_level_of_ rat_hippocampal_neurons_in_vitro
  26. 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]
  27. WangT. LiuY. ZhuangX. LuanF. ZhaoC. Interaction of isoflavone phytoestrogens with ERα and ERβ by molecular docking and molecular dynamics simulations.Curr Comput Aided Drug Des202117655665
     [Google Scholar]
  28. XuJ. YangY. Traditional Chinese medicine in the Chinese health care system.Health Policy2009902-313313910.1016/j.healthpol.2008.09.003 18947898
     [Google Scholar]
  29. EfferthT. LiP.C.H. KonkimallaV.S.B. KainaB. From traditional Chinese medicine to rational cancer therapy.Trends Mol. Med.200713835336110.1016/j.molmed.2007.07.001 17644431
     [Google Scholar]
  30. LiW.L. ZhengH.C. BukuruJ. De KimpeN. Natural medicines used in the traditional Chinese medical system for therapy of diabetes mellitus.J. Ethnopharmacol.200492112110.1016/j.jep.2003.12.031 15099842
     [Google Scholar]
  31. 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]
  32. 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]
  33. ArabiA.A. Artificial intelligence in drug design: Algorithms, applications, challenges and ethics.Future Drug Discov.202132FDD5910.4155/fdd‑2020‑0028
     [Google Scholar]
  34. MerkD. GrisoniF. FriedrichL. SchneiderG. Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators.Commun. Chem.2018116810.1038/s42004‑018‑0068‑1
     [Google Scholar]
  35. LipinskiC.F. MaltarolloV.G. OliveiraP.R. da SilvaA.B.F. HonorioK.M. Advances and perspectives in applying deep learning for drug design and discovery.Front. Robot. AI2019610810.3389/frobt.2019.00108 33501123
     [Google Scholar]
  36. ErtlP. SchuffenhauerA. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.J. Cheminform.200911810.1186/1758‑2946‑1‑8 20298526
     [Google Scholar]
  37. BlaschkeT. PousA.J. ChenH. MargreitterC. TyrchanC. EngkvistO. PapadopoulosK. PatronovA. REINVENT 2.0: An AI tool for de novo drug design.J. Chem. Inf. Model.202060125918592210.1021/acs.jcim.0c00915 33118816
     [Google Scholar]
  38. DuchW. SwaminathanK. MellerJ. Artificial intelligence approaches for rational drug design and discovery.Curr. Pharm. Des.20071314971508
     [Google Scholar]
  39. 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]
  40. WangY. ChiuJ.F. HeQ.Y. Proteomics in computer-aided drug design.Curr. Computeraided Drug Des.200511435210.2174/1573409052952260
     [Google Scholar]
  41. BredaA. BassoL. SantosD. De AzevedoW.Jr Virtual screening of drugs: Score functions, docking, and drug design. Curr. Comput.Aided Drug Des.20084265272
     [Google Scholar]
  42. PungpoP. PunkvangA. SaparpakornP. WolschannP. HannongbuaS. Recent Advances in NNRTI Design. Comput.-Aid. Molecul. Desig. Approach.CAD20095174199
     [Google Scholar]
  43. 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]
  44. SakkiahS. AroojM. KumarM.R. EomS.H. LeeK.W. Identification of inhibitor binding site in human sirtuin 2 using molecular docking and dynamics simulations.PLoS One201381e5142910.1371/journal.pone.0051429 23382805
     [Google Scholar]
  45. ZhuA.Y. ZhouY. KhanS. DeitschK.W. HaoQ. LinH. Plasmodium falciparum Sir2A preferentially hydrolyzes medium and long chain fatty acyl lysine.ACS Chem. Biol.20127115515910.1021/cb200230x 21992006
     [Google Scholar]
  46. 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]
  47. 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/bbaa161 32778891
     [Google Scholar]
  48. 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]
  49. 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]
  50. 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]
  51. HumphreyW. DalkeA. SchultenK. VMD: Visual molecular dynamics.J. Mol. Graph.199614133-38 27-28.10.1016/0263‑7855(96)00018‑58744570
    [Google Scholar]
  52. SeeligerD. de GrootB.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina.J. Comput. Aided Mol. Des.201024541742210.1007/s10822‑010‑9352‑6 20401516
     [Google Scholar]
  53. Salomon-FerrerR. CaseD.A. WalkerR.C. An overview of the Amber biomolecular simulation package.Wiley Interdiscip. Rev. Comput. Mol. Sci.20133219821010.1002/wcms.1121
     [Google Scholar]
  54. OnufrievA. BashfordD. CaseD.A. Exploring protein native states and large‐scale conformational changes with a modified generalized born model.Proteins200455238339410.1002/prot.20033 15048829
     [Google Scholar]
  55. OnufrievA. BashfordD. CaseD.A. Modification of the generalized born model suitable for macromolecules.J. Phys. Chem. B2000104153712372010.1021/jp994072s
     [Google Scholar]
  56. MoY. LuY. WeiG. DerreumauxP. Structural diversity of the soluble trimers of the human amylin(20–29) peptide revealed by molecular dynamics simulations.J. Chem. Phys.20091301212510110.1063/1.3097982 19334894
     [Google Scholar]
  57. KumariR. KumarR. Open source drug discovery consortium and lynn A 2014 g_mmpbsa —A GROMACS tool for high-throughput MM-PBSA calculations.J. Chem. Inf. Model.20145419511962
     [Google Scholar]
  58. ZhaoS. ZhuY-Y. WangX-Y. LiuY-S. SunY-X. ZhaoQ-J. Li, H-Y Structural insight into the interactions between structurally similar inhibitors and SIRT6.Int. J. Mol. Sci.20202172601
     [Google Scholar]
  59. LiuX. PengL. ZhouY. ZhangY. ZhangJ.Z.H. Computational alanine scanning with interaction entropy for protein–ligand binding free energies.J. Chem. Theory Comput.20181431772178010.1021/acs.jctc.7b01295 29406753
     [Google Scholar]
  60. SunY. XiW. WeiG. Atomic-level study of the effects of O4 molecules on the structural properties of protofibrillar Aβ trimer: β-sheet stabilization, salt bridge protection, and binding mechanism.J. Phys. Chem. B201511972786279410.1021/jp508122t 25608630
     [Google Scholar]
  61. Nedumpully-GovindanP. JemecD.B. DingF. CSAR benchmark of flexible medusadock in affinity prediction and nativelike binding pose selection.J. Chem. Inf. Model.20165661042105210.1021/acs.jcim.5b00303 26252196
     [Google Scholar]
  62. DingF. DokholyanN.V. Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark.J. Chem. Inf. Model.20135381871187910.1021/ci300478y 23237273
     [Google Scholar]
  63. 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]
  64. 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]
  65. ZhangY. ZhuY. YueH. ZhaoQ. LiH. Exploring the misfolding and self-assembly mechanism of TTR (105–115) peptides by all-atom molecular dynamics simulation.Front. Mol. Biosci.2022998227610.3389/fmolb.2022.982276 36120541
     [Google Scholar]
  66. TanY. ChenY. LiuX. TangY. LaoZ. WeiG. Dissecting how ALS-associated D290V mutation enhances pathogenic aggregation of hnRNPA2286–291 peptides: Dynamics and conformational ensembles.Int. J. Biol. Macromol.202324112465910.1016/j.ijbiomac.2023.124659 37119915
     [Google Scholar]
  67. MoniotS. SchutkowskiM. SteegbornC. Crystal structure analysis of human Sirt2 and its ADP-ribose complex.J. Struct. Biol.2013182213614310.1016/j.jsb.2013.02.012 23454361
     [Google Scholar]
  68. NarangS.S. GoyalD. GoyalB. Inhibition of Alzheimer’s amyloid-β 42 peptide aggregation by a bi-functional bis-tryptoline triazole: key insights from molecular dynamics simulations.J. Biomol. Struct. Dyn.20193861598161110.1080/07391102.2019.1614093 31046642
     [Google Scholar]
  69. SongM. SunY. LuoY. ZhuY. LiuY. LiH. Exploring the mechanism of inhibition of Au nanoparticles on the aggregation of amyloid-β(16-22) peptides at the atom level by all-atom molecular dynamics.Int. J. Mol. Sci.2018196181510.3390/ijms19061815 29925792
     [Google Scholar]
  70. TengY.B. JingH. AramsangtienchaiP. HeB. KhanS. HuJ. LinH. HaoQ. Efficient demyristoylase activity of SIRT2 revealed by kinetic and structural studies.Sci. Rep.201551852910.1038/srep08529 25704306
     [Google Scholar]
  71. JingH. HuJ. HeB. Negrón AbrilY.L. StupinskiJ. WeiserK. CarbonaroM. ChiangY.L. SouthardT. GiannakakouP. WeissR.S. LinH.A. SIRT2-selective inhibitor promotes c-myc oncoprotein degradation and exhibits broad anticancer activity.Cancer Cell201629329731010.1016/j.ccell.2016.02.007 26977881
     [Google Scholar]
  72. YangY. HsiehC-Y. KangY. HouT. LiuH. YaoX. Deep generation model guided by the docking score for active molecular design.J. Chem. Inf. Model.2023631029832991
     [Google Scholar]
  73. PopovaM. IsayevO. TropshaA. Deep reinforcement learning for de novo drug design.Sci. Adv.201847eaap788510.1126/sciadv.aap7885 30050984
     [Google Scholar]
  74. 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]
  75. LuM. YinJ. ZhuQ. LinG. MouM. LiuF. PanZ. YouN. LianX. LiF. ZhangH. ZhengL. ZhangW. ZhangH. ShenZ. GuZ. LiH. ZhuF. Artificial intelligence in pharmaceutical sciences.Engineering20232013S2095809923001649
     [Google Scholar]
  76. KadurinA. AliperA. KazennovA. MamoshinaP. VanhaelenQ. KhrabrovK. ZhavoronkovA. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology.Oncotarget201787108831089010.18632/oncotarget.14073 28029644
     [Google Scholar]
  77. ParkB.K. KitteringhamN.R. O’NeillP.M. Metabolism of fluorine-containing drugs.Annu. Rev. Pharmacol. Toxicol.200141144347010.1146/annurev.pharmtox.41.1.443 11264465
     [Google Scholar]
  78. ShahP. WestwellA.D. The role of fluorine in medicinal chemistry.J. Enzyme Inhib. Med. Chem.200722552754010.1080/14756360701425014 18035820
     [Google Scholar]
  79. DeBernardisJ.F. KerkmanD.J. WinnM. BushE.N. ArendsenD.L. McClellanW.J. BashaF. Conformationally defined adrenergic agents. 1. Design and synthesis of novel alpha 2 selective adrenergic agents: Electrostatic repulsion based conformational prototypes.J. Med. Chem.1985281013981404
     [Google Scholar]
  80. LiangT. NeumannC.N. RitterT. Introduction of fluorine and fluorine-containing functional groups.Angew. Chem. Int. Ed.201352328214826410.1002/anie.201206566 23873766
     [Google Scholar]
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Exploring and Designing Potential Inhibitors of SIRT2 in Natural Products by Artificial Intelligence (AI) and Molecular Dynamics Methods
Letters in Drug Design & Discovery 21, 3542 (2024); https://doi.org/10.2174/0115701808288696240308052948
/content/journals/lddd/10.2174/0115701808288696240308052948
/content/journals/lddd/10.2174/0115701808288696240308052948
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  • Article Type:     Research Article
Keyword(s): 3D; binding energy; finger; inhibitor; molecular dynamics simulation; RO5; SIRT2; TCM

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