Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Computational investigations of bio-active phytoconstituents from Chamaecostus cuspidatus (Nees & Mart.) C. Specht & D.W. Stev. against peroxisome proliferator-activated receptor gamma (PPARG) protein of type 2 diabetes mellitus

DOI
https://doi.org/10.14719/pst.5933
Submitted
18 October 2024
Published
10-04-2025
Versions

Abstract

Abnormalities in the body's propensity to control and take advantage of sugar as fuel result in type 2 diabetes mellitus (T2DM). Targeting the transcription factor peroxisome proliferator-activated receptor gamma (PPARG) protein, which controls the expression of proteins critical to the progression of type 2 diabetes mellitus (T2DM), is an intriguing approach for treating T2DM. Therefore, the current study focuses on predicting more effective natural compounds for better treatment. Chamaecostus cuspidatus (Nees & Mart.) C. Specht & D. W. Stev. belonging to the family Costaceae, traditionally acknowledged as an insulin herb, has been taken for the study. Phytocompounds were collected from the published literature, followed by in silico ADMET toxicity checking and molecular docking study against the PPARG protein at its specific binding sites. A quantum computation study was performed to check the reactivity of the ligands and normal mode analysis (NMA) was employed to study and characterize the selected protein's flexibility and stability with network analysis. Anti-diabetic drug Biguanide (Metformin) was taken as a standard drug. From this study, Kaempferol resulted with a premier imperative affinity of -7.1 kcal/Mol, with the lowest band gap energy that forms one conventional hydro bond with His466, which is suggested as a new drug molecule for T2DM treatment. In molecular dynamics simulation, the natural compound Kaempferol reflected better stability with the target protein PPARG.

References

  1. Frkic RL, Richter K, Bruning JB. The therapeutic potential of inhibiting PPAR? phosphorylation to treat type 2 diabetes. J Biol Chem. 2021;297(3):101030. https://doi.org/10.1016/j.jbc.2021.101030
  2. Lefterova MI, Haakonsson AK, Lazar MA, Mandrup S. PPAR? and the global map of adipogenesis and beyond. Trends Endocrinol Metab. 2014;25(6):293–302. https://doi.org/10.1016/j.tem.2014.04.001
  3. Kim HY, Jang HJ, Muthamil S, Shin UC, Lyu JH, Kim SW, et al. Novel insights into regulators and functional modulators of adipogenesis. Biomed Pharmacother. 2024;177:117073. https://doi.org/10.1016/j.biopha.2024.117073
  4. Shelake G, Baviskar S, Panda AK, Solankure S, Pandey K, Chauthe S, et al. Exploring the rare variants associated with type 2 diabetes mellitus in Indian population and its disease-drug association studies: an in-silico approach. J Biomol Struct Dyn. 2024;42(12):6307–22. https://doi.org/10.1080/07391102.2023.2233634
  5. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375–91. https://doi.org/10.1152/ajpcell.00379.2020
  7. Mathew F, Varghese B. A review on medicinal exploration of Costus igneus: the insulin plant. Int J Pharm Sci Rev Res. 2019;54(2):51–57.
  8. Bhavsar D, Kutre S, Shikhare P, Kumar S, Behera SK, Chauthe SK. Pharmacoinformatics approach for type 2 diabetes mellitus therapeutics using phytocompounds from Costus genus: an in-silico investigation. J Biomol Struct Dyn. 2024;1–7. https://doi.org/10.1080/07391102.2024.2330712
  9. UniProt Consortium T. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2018;46(5):2699. https://doi.org/10.1093/nar/gky092
  10. Velankar S, Burley SK, Kurisu G, Hoch JC, Markley JL. The protein data bank archive. In: Structural proteomics: High-throughput methods. Humana, New York; 2021. p. 3–21. https://doi.org/10.1007/978-1-0716-1406-8_1
  11. Rani D. Phytochemical and pharmacological overview of Chemoecostus cuspidatus. Plant Arch. 2019;19(2):4565–73.
  12. Shinde S, Surwade S, Sharma R. Costus Igneus: Insulin plant and its preparations as remedial approach for diabetes mellitus. Int J Pharm Sci Res. 2022;13:1551–58. https://orcid.org/10.13040/IJPSR.0975-8232.13(4).1551-58
  13. Thiruchenduran S, Maheswari KU, Prasad TN, Rajeswari B, Suneetha WJ. UV-Vis scanning coupled with PCA as an alternative method for phytochemical screening of natural products –Costus Igneus leaf metabolites. J Pharmacogn Phytochem. 2017;6(1):411–16.
  14. Nascimento CC, Vasconcelos SD, Camacho AC, Nascimento SF, Oliveira JF, Nogueira RI, et al. A literature review on the medicinal properties and toxicological profile of Costus spicatus plant. Res J Life Sci Bioinform Pharm Chem Sci. 2016;2(2):56-68.
  15. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, et al. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–13. https://doi.org/10.1093/nar/gkv951
  16. Pawar SS, Rohane SH. Review on discovery studio: An important tool for molecular docking. Asian J Res Chem. 2021;14(1):1–3. https://doi.org/10.5958/0974-4150.2021.00014.6
  17. Karami TK, Hailu S, Feng S, Graham R, Gukasyan HJ. Eyes on Lipinski's rule of five: A New “rule of thumb” for physicochemical design space of ophthalmic drugs. J Ocul Pharmacol Ther. 2022;38(1):43–55. https://doi.org/10.1089/jop.2021.0069
  18. Yao ZJ, Dong J, Che YJ, Zhu MF, Wen M, Wang NN, et al. TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models. J Comput Aided Mol Des. 2016;30:413–24. https://doi.org/10.1007/s10822-016-9915-2
  19. Banerjee P, Eckert AO, Schrey AK, Preissner R. ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018;46(W1):W257–63. https://doi.org/10.1093/nar/gky318
  20. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 2018;46(W1):W363–67. https://doi.org/10.1093/nar/gky473
  21. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010;31(2):455–61. https://doi.org/10.1002/jcc.21334
  22. Mahata S, Behera SK, Kumar S, Sahoo PK, Sarkar S, Fazil MH, et al. In-silico and in-vitro investigation of STAT3-PIM1 heterodimeric complex: Its mechanism and inhibition by curcumin for cancer therapeutics. Int J Biol Macromol. 2022;208:356–66. https://doi.org/10.1016/j.ijbiomac.2022.03.137
  23. Orio M, Pantazis DA, Neese F. Density functional theory. Photosynth Res. 2009;102:443–53. https://doi.org/10.1007/s11120-009-9404-8
  24. Neese F. The ORCA program system. WIREs Comput Mol Sci. 2012;2(1):73–78. https://doi.org/10.1002/wcms.81
  25. Mering CV, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258–61. https://doi.org/10.1093/nar/gkg034
  26. Bauer JA, Pavlovi? J, Bauerová-Hlinková V. Normal mode analysis as a routine part of a structural investigation. Molecules. 2019;24(18):3293. https://doi.org/10.3390/molecules24183293
  27. López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacón P. iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res. 2014;42(W1):W271–76. https://doi.org/10.1093/nar/gku339
  28. Behera SK, Vhora N, Contractor D, Shard A, Kumar D, Kalia K, et al. Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir. Sci Rep. 2021;11(1):7307. https://doi.org/10.1038/s41598-021-86712-2
  29. Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol. 2011;9:1–9. https://doi.org/10.1186/1741-7007-9-71
  30. Raghu R, Devaraji V, Leena K, Riyaz SD, Rani BP, Kumar BS, et al. Virtual screening and discovery of novel aurora kinase inhibitors. Curr Top Med Chem. 2014;14(17):2006–19. https://doi.org/10.2174/1568026614666140929151140
  31. Shivakumar D, Williams J, Wu Y, Damm W, Shelley J, Sherman W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J Chem Theory Comput. 2010;6(5):1509–19. https://doi.org/10.1021/ct900587b
  32. Aier I, Varadwaj PK, Raj U. Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep. 2016;6(1):34984. https://doi.org/10.1038/srep34984
  33. Choudhary P, Bhowmik A, Chakdar H, Khan MA, Selvaraj C, Singh SK, et al. Understanding the biological role of PqqB in Pseudomonas stutzeri using molecular dynamics simulation approach. J Biomol Struct Dyn. 2022;40(9):4237–49. https://doi.org/10.1080/07391102.2020.1854860
  34. Deniz U, Ozkirimli E, Ulgen KO. A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors. J Mol Graph Model. 2016;63:110–24. https://doi.org/10.1016/j.jmgm.2015.11.004
  35. Tandon G, Jaiswal S, Iquebal MA, Kumar S, Kaur S, Rai A, Kumar D. Evidence of salicylic acid pathway with EDS1 and PAD4 proteins by molecular dynamics simulation for grape improvement. J Biomol Struct Dyn. 2015;33(10):2180–91. https://doi.org/10.1080/07391102.2014.996187
  36. Araki E, Inagaki N, Tanizawa Y, Oura T, Takeuchi M, Imaoka T. Efficacy and safety of once-weekly dulaglutide in combination with sulphonylurea and/or biguanide compared with once-daily insulin glargine in Japanese patients with type 2 diabetes: a randomized, open-label, phase III, non-inferiority study. Diabetes Obes Metab. 2015;17(10):994–1002. https://doi.org/10.1111/dom.12540
  37. Sharchil C, Vijay A, Ramachandran V, Bhagavatheeswaran S, Devarajan R, Koul B, et al. Zebrafish: a model to study and understand the diabetic nephropathy and other microvascular complications of type 2 diabetes mellitus. Vet Sci. 2022;9(7):312. https://doi.org/10.3390/vetsci9070312
  38. Yang Y, Chen Z, Zhao X, Xie H, Du L, Gao H, et al. Mechanisms of Kaempferol in the treatment of diabetes: A comprehensive and latest review. Front Endocrinol. 2022;13:990299. https://doi.org/10.3389/fendo.2022.990299

Downloads

Download data is not yet available.