MOLECULAR DYNAMICS SIMULATIONS OF THE CAFFEIC ACID INTERACTIONS TO DIPEPTIDYL PEPTIDASE IV

ENADE PERDANA ISTYASTONO; FLORENTINUS DIKA OCTA RISWANTO

doi:10.22159/ijap.2022v14i4.44631

Authors

ENADE PERDANA ISTYASTONO Division of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Sanata Dharma University, Campus 3 Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia
FLORENTINUS DIKA OCTA RISWANTO Division of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Sanata Dharma University, Campus 3 Paingan, Maguwoharjo, Depok, Sleman, Yogyakarta 55282, Indonesia

DOI:

https://doi.org/10.22159/ijap.2022v14i4.44631

Keywords:

PyPLIF HIPPOS, YASARA-Structure, Caffeic acid, Dipeptidyl peptidase IV, Molecular dynamics simulations

Abstract

Objective: The research presented in this article aimed to examine the applicability of a recently published software PyPLIF HIPPOS to identify the interactions hotspots between dipeptidyl peptidase IV (DPP4) and its inhibitor caffeic acid during molecular dynamics (MD) simulations.

Methods: Caffeic acid was docked to the binding pocket of DPP4 followed by 50 ns MD simulations, during which snapshots were taken every 10 ps. The molecular docking and the MD simulations were performed in YASARA-Structure 21.12.19. The snapshots were analyzed using the MM/PBSA analysis in YASARA-Structure and PyPLIF HIPPOS to calculate the binding energy (BE) and the caffeic acid-DPP4 interactions hotspots, respectively.

Results: The 50 ns MD simulations of DPP4-caffeic acid had converged since the early stage of the simulations. The BE and the RMSD values of the ligand movement indicated a probable DPP4 allosteric site. PyPLIF HIPPOS identified 15 interacting DPP4 residues to caffeic acid. The residues interacting with caffeic acid in more than 10% snapshots of the MD simulations were Ser59, Arg61, Glu206, and Phe357. The binding residues Ser59 and Arg61 were suggested to be part of the plausible DPP4 allosteric site.

Conclusion: PyPLIF HIPPOS serves as a valuable complement to the MM/PBSA method in the examination of enzyme-inhibitor interactions.

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References

Li N, Wang LJ, Jiang B, Li XQ, Guo CL, Guo SJ, Shi DY. Recent progress of the development of dipeptidyl peptidase-4 inhibitors for the treatment of type 2 diabetes mellitus. Eur J Med Chem. 2018;151:145-57. doi: 10.1016/ j.ejmech.2018.03.041, PMID 29609120.

Fan J, Johnson MH, Lila MA, Yousef G, de Mejia EG. Berry and citrus phenolic compounds inhibit dipeptidyl peptidase IV: implications in diabetes management. Evid Based Complement Alternat Med. 2013;2013:479505. doi: 10.1155/2013/479505, PMID 24069048.

Istyastono EP. Docking studies of curcumin as a potential lead compound to develop novel dipeptydyl peptidase-4 inhibitors. Indones J Chem. 2010;9(1):132-6. doi: 10.22146/ijc.21574.

Cao W, Chen X, Chin Y, Zheng J, Lim PE, Xue C, Tang Q. Identification of curcumin as a potential α-glucosidase and dipeptidyl-peptidase 4 inhibitor: molecular docking study, in vitro and in vivo biological evaluation. J Food Biochem. 2022;46(3):e13686. doi: 10.1111/jfbc.13686. PMID 33817806.

Krol K, Gantner M, Tatarak A, Hallmann E. The content of polyphenols in coffee beans as roasting, origin and storage effect. Eur Food Res Technol. 2020;246(1):33-9. doi: 10.1007/s00217-019-03388-9.

Carlstrom M, Larsson SC. Coffee consumption and reduced risk of developing type 2 diabetes: A systematic review with meta-analysis. Nutr Rev. 2018;76(6):395-417. doi: 10.1093/nutrit/nuy014, PMID 29590460.

Rognan D. Fragment-based approaches and computer-aided drug discovery. Top Curr Chem. 2012;317:201-22. doi: 10.1007/128_2011_182, PMID 21710380.

Gani MR, Istyastono EP. Determination of caffeic acid in ethanolic extract of spent coffee grounds by high-performance liquid chromatography with UV detection. Indones J Chem. 2021;21(5):1281-6. doi: 10.22146/ijc.61462.

Istyastono EP, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS-assisted prediction of molecular determinants of ligand binding to receptors. Molecules. 2021;26(9):1-12. doi: 10.3390/molecules26092452, PMID 33922338.

Istyastono EP, Kooistra AJ, Vischer HF, Kuijer M, Roumen L, Nijmeijer S, Smits RA, de Esch IJP, Leurs R, de Graaf C. Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H 4 receptor. Chem Commun. 2015;6(6):1003-17. doi: 10.1039/C5MD00022J.

Istyastono EP, Radifar M, Yuniarti N, Prasasty VD, Mungkasi S. PyPLIF HIPPOS: A molecular interaction fingerprinting tool for docking results of AutoDock Vina and PLANTS. J Chem Inf Model. 2020;60(8):3697-702. doi: 10.1021/acs.jcim.0c00305. PMID 32687350.

Istyastono E, Gani MR. Identification of interactions of ABT-341 to dipeptidyl peptidase IV during molecular dynamics simulations. J Farmasi Galenika (Galenika Journal of Pharmacy). 2021;7:91–8. doi: 10.22487/j24428744.2021.

Byun J, Lee J. Identifying the hot spot residues of the SARS-CoV-2 main protease using MM-PBSA and multiple force fields. Life (Basel). 2021;12(1):54. doi: 10.3390/life12010054, PMID 35054447.

Dash R, Junaid M, Mitra S, Arifuzzaman M, Hosen SMZ. Structure-based identification of potent VEGFR-2 inhibitors from in vivo metabolites of a herbal ingredient. J Mol Model. 2019;25(4):98. doi: 10.1007/s00894-019-3979-6, PMID 30904971.

Krieger E, Vriend G. New ways to boost molecular dynamics simulations. J Comput Chem. 2015;36(13):996-1007. doi: 10.1002/jcc.23899, PMID 25824339.

Feng J, Zhang Z, Wallace MB, Stafford JA, Kaldor SW, Kassel DB, Navre M, Shi L, Skene RJ, Asakawa T, Takeuchi K, Xu R, Webb DR, Gwaltney SL. Discovery of alogliptin: A potent, selective, bioavailable, and efficacious inhibitor of dipeptidyl peptidase IV. J Med Chem. 2007;50(10):2297-300. doi: 10.1021/jm070104l, PMID 17441705.

Korb O, Stutzle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model. 2009;49(1):84-96. doi: 10.1021/ci800298z, PMID 19125657.

Ten Brink T, Exner TE. Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J Chem Inf Model. 2009;49(6):1535-46. doi: 10.1021/ci800420z, PMID 19453150.

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. doi: 10.1002/jcc.21334, PMID 19499576.

Liu K, Watanabe E, Kokubo H. Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J Comput Aid Mol Des. 2017;31(2):201-11. doi: 10.1007/s10822-016-0005-2, PMID 28074360.

Nongonierma AB, Mooney C, Shields DC, Fitzgerald RJ. In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors. Peptides. 2014;57:43-51. doi: 10.1016/j.peptides.2014.04.018, PMID 24793774.

Mooers BHM. Shortcuts for faster image creation in PyMOL. Protein Sci. 2020;29(1):268-76. doi: 10.1002/pro.3781, PMID 31710740.

Berger JP, SinhaRoy R, Pocai A, Kelly TM, Scapin G, Gao YD, Pryor KAD, Wu JK, Eiermann GJ, Xu SS, Zhang X, Tatosian DA, Weber AE, Thornberry NA, Carr RD. A comparative study of the binding properties, dipeptidyl peptidase-4 (DPP-4) inhibitory activity and glucose-lowering efficacy of the DPP-4 inhibitors alogliptin, linagliptin, saxagliptin, sitagliptin and vildagliptin in mice. Endocrinol Diabetes Metab. 2018;1(1):e00002. doi: 10.1002/edm2.2, PMID 30815539.

Tomovic K, Ilic BS, Miljkovic M, Dimov S, Yancheva D, Kojic M, Mavrova AT, Kocic G, Smelcerovic A. Benzo[4,5]thieno[2,3-d]pyrimidine phthalimide derivative, one of the rare noncompetitive inhibitors of dipeptidyl peptidase-4. Arch Pharm. 2020;353(1):e1900238. doi: 10.1002/ardp.201900238, PMID 31710123.

Chen YC. Beware of docking! Trends Pharmacol Sci. 2015;36(2):78-95. doi: 10.1016/j.tips.2014.12.001. PMID 25543280.

Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11(5):905-19. doi: 10.1038/nprot.2016.051, PMID 27077332.

Febrina E, Alamhari RK, Abdulah R, Lestari K, Levita J, Supratman U. Molecular docking and molecular dynamics studies of acalypha indica l. phytochemical constituents with caspase-3. Int J App Pharm 2021;13(4):210-5. doi: 10.22159/ijap.2021.v13s4.43861.

Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449-61. doi: 10.1517/17460441.2015.1032936, PMID 25835573.

Farkhani A, Sauriasari R, Yanuar A. In silico approach for screening of the Indonesian medicinal plant's database to discover potential dipeptidyl peptidase-4 inhibitors. Int J App Pharm. 2020;12:60-8. doi: 10.22159/ijap.2020.v12s1.FF008.

Priyanka SR. A systematic review Indian floral biodiversity as eminent reserves for alternative treatment strategy of diabetes mellitus. Int J Pharm Pharm Sci. 2016;8:10-9.

Vipin AM, Baby B, Kumar MS, Kumar RV, Nazeem PA. Comparative docking studies on the effect of commercial drugs on dipeptidyl peptidase-4 (DPP-4). Int J Pharm Pharm Sci. 2015;7:508-10.