Skip to content
2000
image of Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation of Mucuna pruriens for Parkinson's Disease Treatment

Abstract

Introduction

Parkinson's Disease (PD) is a common neurodegenerative disorder with limited treatment options. Thus, there's a need for new therapies. (MP) seeds are used in traditional treatments for PD, but their mechanisms are not well understood. This research uses methods to explore MP's pharmacological effects as a potential PD treatment.

Methods

We registered the active ingredients in MP and their targets, then analyzed genes related to Parkinson's Disease (PD). This led to the creation of a Protein-Protein Interaction (PPI) network. We examined the binding interactions between hub proteins and compounds using molecular docking and confirmed the results with molecular dynamics analysis.

Results

We revealed sixteen substances in MP seeds that target 113 therapeutic points in PD. The proteins identified in the enrichment analysis regulate actin, endocytosis, and various other cellular processes. Ultimately, we identified eleven hub proteins (TP53, AKT1, MAPK8, ESR1, MAPK3, BCL2, HSP90AA1, PRKACA, CASP3, EGFR, and IL6) that interact with the sixteen active compounds, a finding confirmed by molecular docking and molecular dynamics.

Discussion

The identified hub proteins are key therapeutic targets that regulate crucial processes in Parkinson's disease, highlighting the neuroprotective potential of bioactive compounds in MP seeds. These findings justify further experimental studies to confirm their therapeutic potential in treating Parkinson's disease.

Conclusion

Our findings suggest that, in addition to L-DOPA, other compounds in MP seeds may act synergistically to produce antiparkinsonian effects.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cdt/10.2174/0113894501422075251015075439
2025-11-04
2025-12-15

  • From This Site

    /content/journals/cdt/10.2174/0113894501422075251015075439
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Triarhou L.C. Dopamine and Parkinson’s disease. Madame Curie Bioscience Database Austin (TX) Landes Bioscience 2013
    [Google Scholar]
  2. Zhou Z.D. Yi L.X. Wang D.Q. Lim T.M. Tan E.K. Role of dopamine in the pathophysiology of Parkinson’s disease. Transl. Neurodegener. 2023 12 1 44 44 10.1186/s40035‑023‑00378‑6 37718439
    [Google Scholar]
  3. Zhu J. Cui Y. Zhang J. Yan R. Su D. Zhao D. Wang A. Feng T. Temporal trends in the prevalence of Parkinson’s disease from 1980 to 2023: A systematic review and meta-analysis. Lancet Healthy Longev. 2024 5 7 e464 e479 10.1016/S2666‑7568(24)00094‑1 38945129
    [Google Scholar]
  4. Lee H.M. Koh S.B. Many faces of Parkinson’s disease: Non-motor symptoms of Parkinson’s disease. J. Mov. Disord. 2015 8 2 92 97 10.14802/jmd.15003 26090081
    [Google Scholar]
  5. Kouli A. Torsney K.M. Kuan W.L. Parkinson’s disease: Etiology, neuropathology, and pathogenesis. Pathog. Clin. Aspects 2018 1 1 3 26 10.15586/codonpublications.parkinsonsdisease.2018.ch1 30702842
    [Google Scholar]
  6. Chastan N. Bair W.N. Resnick S.M. Studenski S.A. Decker L.M. Prediagnostic markers of idiopathic Parkinson’s disease: Gait, visuospatial ability and executive function. Gait Posture 2019 68 500 505 10.1016/j.gaitpost.2018.12.039 30616180
    [Google Scholar]
  7. Jankovic J. Tan E.K. Parkinson’s disease: Etiopathogenesis and treatment. J. Neurol. Neurosurg. Psychiatry 2020 91 8 795 808 10.1136/jnnp‑2019‑322338 32576618
    [Google Scholar]
  8. Dong-Chen X. Yong C. Yang X. Chen-Yu S. Li-Hua P. Signaling pathways in Parkinson’s disease: Molecular mechanisms and therapeutic interventions. Signal Transduct. Target. Ther. 2023 8 1 73 10.1038/s41392‑023‑01353‑3 36810524
    [Google Scholar]
  9. Armstrong M.J. Okun M.S. Diagnosis and treatment of Parkinson disease. JAMA 2020 323 6 548 560 10.1001/jama.2019.22360 32044947
    [Google Scholar]
  10. Mishal B. Shetty A. Wadia P. Adverse effects of medications used to treat motor symptoms of Parkinson’s disease: A narrative review. Ann. Mov. Disord. 2023 6 2 45 57 10.4103/aomd.aomd_37_22
    [Google Scholar]
  11. Yin R. Xue J. Tan Y. Fang C. Hu C. Yang Q. Mei X. Qi D. The positive role and mechanism of herbal medicine in parkinson’s disease. Oxid. Med. Cell. Longev. 2021 2021 1 9923331 10.1155/2021/9923331 34567415
    [Google Scholar]
  12. Rai S.N. Chaturvedi V.K. Singh P. Singh B.K. Singh M.P. Mucuna pruriens in Parkinson's and in some other diseases: Recent advancement and future prospective. 3 Biotech 2020 10 522 10.1007/s13205‑020‑02532‑7
    [Google Scholar]
  13. Kamkaen N. Chittasupho C. Vorarat S. Tadtong S. Phrompittayarat W. Okonogi S. Kwankhao P. Mucuna pruriens seed aqueous extract improved neuroprotective and acetylcholinesterase inhibitory effects compared with synthetic L-dopa. Molecules 2022 27 10 3131 3131 10.3390/molecules27103131 35630617
    [Google Scholar]
  14. Guzmán-Flores J.M. Pérez-Reyes Á. Vázquez-Jiménez S.I. Isiordia-Espinoza M.A. Martínez-Esquivias F. A docking and network pharmacology study on the molecular mechanisms of curcumin in dental caries and Streptococcus mutans. Dent. J. 2024 12 6 153 153 10.3390/dj12060153 38920854
    [Google Scholar]
  15. Shamsol Azman A.N.S. Tan J.J. Abdullah M.N.H. Bahari H. Lim V. Yong Y.K. Network pharmacology and molecular docking analysis of active compounds in tualang honey against atherosclerosis. Foods 2023 12 9 1779 1779 10.3390/foods12091779 37174317
    [Google Scholar]
  16. Guzmán-Flores J.M. Pérez-Vázquez V. Martínez-Esquivias F. Isiordia-Espinoza M.A. Viveros-Paredes J.M. Molecular docking integrated with network pharmacology explores the therapeutic mechanism of Cannabis sativa against type 2 diabetes. Curr. Issues Mol. Biol. 2023 45 9 7228 7241 10.3390/cimb45090457 37754241
    [Google Scholar]
  17. Li W. Yuan G. Pan Y. Wang C. Chen H. Network pharmacology studies on the bioactive compounds and action mechanisms of natural products for the treatment of diabetes mellitus: A review. Front. Pharmacol. 2017 8 74 74 10.3389/fphar.2017.00074 28280467
    [Google Scholar]
  18. Li L. Yang L. Yang L. He C. He Y. Chen L. Dong Q. Zhang H. Chen S. Li P. Network pharmacology: A bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine. Chin. Med. 2023 18 1 146 146 10.1186/s13020‑023‑00853‑2 37941061
    [Google Scholar]
  19. Agu P.C. Afiukwa C.A. Orji O.U. Ezeh E.M. Ofoke I.H. Ogbu C.O. Ugwuja E.I. Aja P.M. Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management. Sci. Rep. 2023 13 1 13398 13398 10.1038/s41598‑023‑40160‑2 37592012
    [Google Scholar]
  20. Wu X. Xu L.Y. Li E.M. Dong G. Application of molecular dynamics simulation in biomedicine. Chem. Biol. Drug Des. 2022 99 5 789 800 10.1111/cbdd.14038 35293126
    [Google Scholar]
  21. Jin Z. Wei Z. Molecular simulation for food protein–ligand interactions: A comprehensive review on principles, current applications, and emerging trends. Compr. Rev. Food Sci. Food Saf. 2024 23 1 e13280 10.1111/1541‑4337.13280 38284571
    [Google Scholar]
  22. Salim I. Hamza A.B. Classification of developmental and brain disorders via graph convolutional aggregation. Cognit. Comput. 2024 16 2 701 716 10.1007/s12559‑023‑10224‑6
    [Google Scholar]
  23. Verma R. Kumar N. Patil A. Kurian N.C. Rane S. Graham S. Vu Q.D. Zwager M. Raza S.E.A. Rajpoot N. Wu X. Chen H. Huang Y. Wang L. Jung H. Brown G.T. Liu Y. Liu S. Jahromi S.A.F. Khani A.A. Montahaei E. Baghshah M.S. Behroozi H. Semkin P. Rassadin A. Dutande P. Lodaya R. Baid U. Baheti B. Talbar S. Mahbod A. Ecker R. Ellinger I. Luo Z. Dong B. Xu Z. Yao Y. Lv S. Feng M. Xu K. Zunair H. Hamza A.B. Smiley S. Yin T.K. Fang Q.R. Srivastava S. Mahapatra D. Trnavska L. Zhang H. Narayanan P.L. Law J. Yuan Y. Tejomay A. Mitkari A. Koka D. Ramachandra V. Kini L. Sethi A. MoNuSAC2020: A multi-organ nuclei segmentation and classification challenge. IEEE Trans. Med. Imaging 2021 40 12 3413 3423 10.1109/TMI.2021.3085712 34086562
    [Google Scholar]
  24. Mohanraj K. Karthikeyan B.S. Vivek-Ananth R.P. Chand R.P.B. Aparna S.R. Mangalapandi P. Samal A. IMPPAT: A curated database of Indian Medicinal plants, phytochemistry and therapeutics. Sci. Rep. 2018 8 1 4329 4329 10.1038/s41598‑018‑22631‑z 29531263
    [Google Scholar]
  25. Swanson K. Walther P. Leitz J. Mukherjee S. Wu J.C. Shivnaraine R.V. Zou J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics 2024 40 7 btae416 10.1093/bioinformatics/btae416
    [Google Scholar]
  26. Wang X. Shen Y. Wang S. Li S. Zhang W. Liu X. Lai L. Pei J. Li H. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017 45 W1 W356 W360 10.1093/nar/gkx374 28472422
    [Google Scholar]
  27. Kim S. Chen J. Cheng T. Gindulyte A. He J. He S. Li Q. Shoemaker B.A. Thiessen P.A. Yu B. Zaslavsky L. Zhang J. Bolton E.E. PubChem 2023 update. Nucleic Acids Res. 2023 51 D1 D1373 D1380 10.1093/nar/gkac956 36305812
    [Google Scholar]
  28. Bateman A. Martin M.J. Orchard S. Magrane M. Ahmad S. Alpi E. Bowler-Barnett E.H. Britto R. Bye-A-Jee H. Cukura A. Denny P. Dogan T. Ebenezer T. Fan J. Garmiri P. da Costa Gonzales L.J. Hatton-Ellis E. Hussein A. Ignatchenko A. Insana G. Ishtiaq R. Joshi V. Jyothi D. Kandasaamy S. Lock A. Luciani A. Lugaric M. Luo J. Lussi Y. MacDougall A. Madeira F. Mahmoudy M. Mishra A. Moulang K. Nightingale A. Pundir S. Qi G. Raj S. Raposo P. Rice D.L. Saidi R. Santos R. Speretta E. Stephenson J. Totoo P. Turner E. Tyagi N. Vasudev P. Warner K. Watkins X. Zaru R. Zellner H. Bridge A.J. Aimo L. Argoud-Puy G. Auchincloss A.H. Axelsen K.B. Bansal P. Baratin D. Batista Neto T.M. Blatter M-C. Bolleman J.T. Boutet E. Breuza L. Gil B.C. Casals-Casas C. Echioukh K.C. Coudert E. Cuche B. de Castro E. Estreicher A. Famiglietti M.L. Feuermann M. Gasteiger E. Gaudet P. Gehant S. Gerritsen V. Gos A. Gruaz N. Hulo C. Hyka-Nouspikel N. Jungo F. Kerhornou A. Le Mercier P. Lieberherr D. Masson P. Morgat A. Muthukrishnan V. Paesano S. Pedruzzi I. Pilbout S. Pourcel L. Poux S. Pozzato M. Pruess M. Redaschi N. Rivoire C. Sigrist C.J.A. Sonesson K. Sundaram S. Wu C.H. Arighi C.N. Arminski L. Chen C. Chen Y. Huang H. Laiho K. McGarvey P. Natale D.A. Ross K. Vinayaka C.R. Wang Q. Wang Y. Zhang J. UniProt: The universal protein knowledgebase in 2023. Nucleic Acids Res. 2023 51 D1 D523 D531 10.1093/nar/gkac1052 36408920
    [Google Scholar]
  29. Rappaport N. Fishilevich S. Nudel R. Twik M. Belinky F. Plaschkes I. Stein T.I. Cohen D. Oz-Levi D. Safran M. Lancet D. Rational confederation of genes and diseases: NGS interpretation via GeneCards, MalaCards and VarElect. Biomed. Eng. Online 2017 16 S1 72 72 10.1186/s12938‑017‑0359‑2 28830434
    [Google Scholar]
  30. Wyatt B. Davis A.P. Wiegers T.C. Wiegers J. Abrar S. Sciaky D. Barkalow F. Strong M. Mattingly C.J. Transforming environmental health datasets from the comparative toxicogenomics database into chord diagrams to visualize molecular mechanisms. Front. Toxicol. 2024 6 1437884 1437884 10.3389/ftox.2024.1437884 39104826
    [Google Scholar]
  31. Oliveros J.C. Available from: https://bioinfogp.cnb.csic.es/tools/venny/
  32. Ge S.X. Jung D. Yao R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020 36 8 2628 2629 10.1093/bioinformatics/btz931 31882993
    [Google Scholar]
  33. Szklarczyk D. Kirsch R. Koutrouli M. Nastou K. Mehryary F. Hachilif R. Gable A.L. Fang T. Doncheva N.T. Pyysalo S. Bork P. Jensen L.J. von Mering C. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 51 D1 D638 D646 10.1093/nar/gkac1000 36370105
    [Google Scholar]
  34. Liu Y. Yang X. Gan J. Chen S. Xiao Z.X. Cao Y. CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022 50 W1 W159 W164 10.1093/nar/gkac394 35609983
    [Google Scholar]
  35. Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. Tunyasuvunakool K. Bates R. Žídek A. Potapenko A. Bridgland A. Meyer C. Kohl S.A.A. Ballard A.J. Cowie A. Romera-Paredes B. Nikolov S. Jain R. Adler J. Back T. Petersen S. Reiman D. Clancy E. Zielinski M. Steinegger M. Pacholska M. Berghammer T. Bodenstein S. Silver D. Vinyals O. Senior A.W. Kavukcuoglu K. Kohli P. Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021 596 7873 583 589 10.1038/s41586‑021‑03819‑2 34265844
    [Google Scholar]
  36. Abraham M.J. Murtola T. Schulz R. Páll S. Smith J.C. Hess B. Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015 1-2 19 25 10.1016/j.softx.2015.06.001
    [Google Scholar]
  37. Lindorff-Larsen K. Piana S. Palmo K. Maragakis P. Klepeis J.L. Dror R.O. Shaw D.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010 78 8 1950 1958 10.1002/prot.22711 20408171
    [Google Scholar]
  38. Kagami L. Wilter A. Diaz A. Vranken W. The ACPYPE web server for small-molecule MD topology generation. Bioinformatics 2023 39 6 btad350 10.1093/bioinformatics/btad350
    [Google Scholar]
  39. Valdés-Tresanco M.S. Valdés-Tresanco M.E. Valiente P.A. Moreno E. gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. J. Chem. Theory Comput. 2021 17 10 6281 6291 10.1021/acs.jctc.1c00645 34586825
    [Google Scholar]
  40. Pathania R. Chawla P. Khan H. Kaushik R. Khan M.A. An assessment of potential nutritive and medicinal properties of Mucuna pruriens: A natural food legume. 3 Biotech 2020 10 261 10.1007/s13205‑020‑02253‑x
    [Google Scholar]
  41. Shrihastini V. Muthuramalingam P. Adarshan S. Sujitha M. Chen J.T. Shin H. Ramesh M. Plant derived bioactive compounds, their anti-cancer effects and in silico approaches As an alternative target treatment strategy for breast cancer: An updated overview. Cancers 2021 13 24 6222 6222 10.3390/cancers13246222 34944840
    [Google Scholar]
  42. Alves B.S. Schimith L.E. da Cunha A.B. Dora C.L. Hort M.A. Omega-3 polyunsaturated fatty acids and Parkinson’s disease: A systematic review of animal studies. J. Neurochem. 2024 168 8 1655 1683 10.1111/jnc.16154 38923542
    [Google Scholar]
  43. Tang K.S. Protective effect of arachidonic acid and linoleic acid on 1-methyl-4-phenylpyridinium-induced toxicity in PC12 cells. Lipids Health Dis. 2014 13 1 197 197 10.1186/1476‑511X‑13‑197 25522984
    [Google Scholar]
  44. Mori M.A. Delattre A.M. Carabelli B. Pudell C. Bortolanza M. Staziaki P.V. Visentainer J.V. Montanher P.F. Del Bel E.A. Ferraz A.C. Neuroprotective effect of omega-3 polyunsaturated fatty acids in the 6-OHDA model of Parkinson’s disease is mediated by a reduction of inducible nitric oxide synthase. Nutr. Neurosci. 2018 21 5 341 351 10.1080/1028415X.2017.1290928 28221817
    [Google Scholar]
  45. Alarcon-Gil J. Sierra-Magro A. Morales-Garcia J.A. Sanz-SanCristobal M. Alonso-Gil S. Cortes-Canteli M. Niso-Santano M. Martínez-Chacón G. Fuentes J.M. Santos A. Perez-Castillo A. Neuroprotective and anti-inflammatory effects of linoleic acid in models of parkinson’s disease: The implication of lipid droplets and lipophagy. Cells 2022 11 15 2297 2297 10.3390/cells11152297 35892594
    [Google Scholar]
  46. Kar A. Ghosh P. Patra P. Chini D.S. Nath A.K. Saha J.K. Chandra Patra B. Omega-3 fatty acids mediated cellular signaling and its regulation in human health. Clin. Nutr. Open Sci. 2023 52 72 86 10.1016/j.nutos.2023.10.004
    [Google Scholar]
  47. Park H.R. Kim J.Y. Park K.Y. Lee J.W. Lipotoxicity of palmitic Acid on neural progenitor cells and hippocampal neurogenesis. Toxicol. Res. 2011 27 2 103 110 10.5487/TR.2011.27.2.103 24278558
    [Google Scholar]
  48. Urso C.J. Zhou H. Palmitic acid lipotoxicity in microglia cells is ameliorated by unsaturated fatty acids. Int. J. Mol. Sci. 2021 22 16 9093 9093 10.3390/ijms22169093 34445796
    [Google Scholar]
  49. Vesga-Jiménez D.J. Martin C. Barreto G.E. Aristizábal-Pachón A.F. Pinzón A. González J. Fatty acids: An insight into the pathogenesis of neurodegenerative diseases and therapeutic potential. Int. J. Mol. Sci. 2022 23 5 2577 2577 10.3390/ijms23052577 35269720
    [Google Scholar]
  50. Bajracharya R. Bustamante S. O Ballard J.W. Stearic acid supplementation in high protein to carbohydrate (P:C) ratio diet improves physiological and mitochondrial functions of drosophila melanogaster parkin null mutants. J. Gerontol. A Biol. Sci. Med. Sci. 2019 74 10 1564 1572 10.1093/gerona/glx246 29236963
    [Google Scholar]
  51. Huang Q. Chen C. Zhang Z. Xue Q. Anti-inflammatory effects of myristic acid mediated by the NF-κB pathway in lipopolysaccharide-induced BV-2 microglial cells. Mol. Omics 2023 19 9 726 734 10.1039/D3MO00063J 37466104
    [Google Scholar]
  52. Kumbhare S.D. Ukey S.S. Gogle D.P. Antioxidant activity of Flemingia praecox and Mucuna pruriens and their implications for male fertility improvement. Sci. Rep. 2023 13 1 19360 19360 10.1038/s41598‑023‑46705‑9 37938242
    [Google Scholar]
  53. Choowong-in P. Sattayasai J. Boonchoong P. Poodendaen C. Wu A.T.H. Tangsrisakda N. Sawatpanich T. Arun S. Uabundit N. Iamsaard S. Protective effects of Thai Mucuna pruriens (L.) DC. var. pruriens seeds on sexual behaviors and essential reproductive markers in chronic unpredictable mild stress mice. J. Tradit. Complement. Med. 2022 12 4 402 413 10.1016/j.jtcme.2021.12.001 35747354
    [Google Scholar]
  54. Luong X.G. Conti M. RNA binding protein networks and translational regulation in oocytes. In Human Reproductive and Prenatal Genetics Oxford, England Academic Press Leung P.C.K. Qiao J. 2019 193 220 10.1016/B978‑0‑12‑813570‑9.00009‑7
    [Google Scholar]
  55. Ma L.Y. Wang L. Liang J. Huo L. Investigating the neuroprotective potential of rAAV2-PCBP1-EGFP gene therapy against a 6-OHDA-induced model of Parkinson’s disease. Brain Behav. 2024 14 1 e3376 e3376 10.1002/brb3.3376 38376022
    [Google Scholar]
  56. Kushida N. Yamaguchi O. Kawashima Y. Akaihata H. Hata J. Ishibashi K. Aikawa K. Kojima Y. Uni-axial stretch induces actin stress fiber reorganization and activates c-Jun NH2 terminal kinase via RhoA and Rho kinase in human bladder smooth muscle cells. BMC Urol. 2016 16 1 9 9 10.1186/s12894‑016‑0127‑9 26928204
    [Google Scholar]
  57. Iyer M. Subramaniam M.D. Venkatesan D. Cho S.G. Ryding M. Meyer M. Vellingiri B. Role of RhoA-ROCK signaling in Parkinson’s disease. Eur. J. Pharmacol. 2021 894 173815 173815 10.1016/j.ejphar.2020.173815 33345850
    [Google Scholar]
  58. Oevel K. Hohensee S. Kumar A. Rosas-Brugada I. Bartolini F. Soykan T. Haucke V. Rho GTPase signaling and mDia facilitate endocytosis via presynaptic actin. eLife 2024 12 RP92755 RP92755 10.7554/eLife.92755 38502163
    [Google Scholar]
  59. Zou L. Tian Y. Zhang Z. Dysfunction of Synaptic Vesicle Endocytosis in Parkinson’s Disease. Front. Integr. Nuerosci. 2021 15 619160 619160 10.3389/fnint.2021.619160 34093144
    [Google Scholar]
  60. García-Ortegón M. Simm G.N.C. Tripp A.J. Hernández-Lobato J.M. Bender A. Bacallado S. DOCKSTRING: Easy molecular docking yields better benchmarks for ligand design. J. Chem. Inf. Model. 2022 62 15 3486 3502 10.1021/acs.jcim.1c01334 35849793
    [Google Scholar]
  61. Sayers E.W. Bolton E.E. Brister J.R. Canese K. Chan J. Comeau D.C. Connor R. Funk K. Kelly C. Kim S. Madej T. Marchler-Bauer A. Lanczycki C. Lathrop S. Lu Z. Thibaud-Nissen F. Murphy T. Phan L. Skripchenko Y. Tse T. Wang J. Williams R. Trawick B.W. Pruitt K.D. Sherry S.T. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022 50 D1 D20 D26 10.1093/nar/gkab1112 34850941
    [Google Scholar]
  62. Terrin F. Tesoriere A. Plotegher N. Dalla Valle L. Sex and brain: The role of sex chromosomes and hormones in brain development and Parkinson’s disease. Cells 2023 12 11 1486 1486 10.3390/cells12111486 37296608
    [Google Scholar]
  63. Liu J. Yuan S. Niu X. Kelleher R. Sheridan H. ESR1 dysfunction triggers neuroinflammation as a critical upstream causative factor of the Alzheimer’s disease process. Aging 2022 14 21 8595 8614 10.18632/aging.204359 36326669
    [Google Scholar]
  64. Tansey M.G. Wallings R.L. Houser M.C. Herrick M.K. Keating C.E. Joers V. Inflammation and immune dysfunction in Parkinson disease. Nat. Rev. Immunol. 2022 22 11 657 673 10.1038/s41577‑022‑00684‑6 35246670
    [Google Scholar]
  65. Quan W. Li J. Jin X. Liu L. Zhang Q. Qin Y. Pei X. Chen J. Identification of potential core genes in parkinson’s disease using bioinformatics analysis. Parkinsons Dis. 2021 2021 1 10 10.1155/2021/1690341 34580608
    [Google Scholar]
  66. Jia E. Pan M. Liu Z. Zhou Y. Zhao X. Dong J. Bai Y. Ge Q. Transcriptomic profiling of differentially expressed genes and related pathways in different brain regions in Parkinson’s disease. Neurosci. Lett. 2020 732 135074 135074 10.1016/j.neulet.2020.135074 32446776
    [Google Scholar]
  67. Ruan S. Xie J. Wang L. Guo L. Li Y. Fan W. Ji R. Gong Z. Xu Y. Mao J. Xie J. Nicotine alleviates MPTP-induced nigrostriatal damage through modulation of JNK and ERK signaling pathways in the mice model of Parkinson’s disease. Front. Pharmacol. 2023 14 1088957 1088957 10.3389/fphar.2023.1088957 36817162
    [Google Scholar]
  68. Zahra W. Birla H. Singh S.S. Rathore A.S. Dilnashin H. Singh R. Keshri P.K. Gautam P. Singh S.P. Neuroprotection by Mucuna pruriens in neurodegenerative diseases. Neurochem. Res. 2022 47 7 1816 1829 10.1007/s11064‑022‑03591‑3 35380400
    [Google Scholar]
/content/journals/cdt/10.2174/0113894501422075251015075439
Loading
Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation of Mucuna pruriens for Parkinson's Disease Treatment
Bentham Science Publishers , 1; https://doi.org/10.2174/0113894501422075251015075439
/content/journals/cdt/10.2174/0113894501422075251015075439
/content/journals/cdt/10.2174/0113894501422075251015075439
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: parkinson's disease, sleep disorders ; network pharmacology ; Mucuna pruriens ; molecular docking ; herbal medicine

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