Main Article Content

Abstract

Background: Dipeptidyl Peptidase IV (DPP-IV) is an established drug discovery target for type 2 diabetes mellitus (T2DM) therapy. On the other hand, molecular dynamics (MD) simulations have been widely employed to obtain insights of the protein-ligand interactions in structure-based drug design research projects. Moreover, a software to identify protein-ligand interactions called PyPLIF HIPPOS was made publicly available recently. Employing PyPLIF HIPPOS to identify the interactions of DPP-IV and its ligand ABT-341 during MD simulations was then of considerable interest. Objectives: The main aim of this study was to identify protein-ligand interactions of ABT-341 to DPP-IV during MD simulations. Material and Methods: The crystal structure of DPP-IV co-crystallized with ABT-341 obtained from the Protein Data Bank with code of 2I78 was used as the main material. YASARA-Structure was employed for performing 10 ns prodution run MD simulations with snapshots in every 100 ps and PyPLIF HIPPOS was used to identify the protein-ligand interactions. Results: There were 23 interactions involving 13 residues identified by employing PyPLIF HIPPOS during the MD simulations. Two of them identified in all snapshots, i.e., hydrophobic interactions to PHE357 and TYR666. Conclusions: PyPLIF HIPPOS was succesfully employed to identify the interactions of ABT-341 to DPP-IV during MD simulations.

Keywords

Dipeptidyl Peptidase IV (DPP-IV) YASARA-Structure Molecular Dynamics PyPLIF HIPPOS

Article Details

Author Biographies

Enade Istyastono, Universitas Sanata Dharma, Yogyakarta, Indonesia.

Fakultas Farmasi, Universitas Sanata Dharma, Yogyakarta, Indonesia.

Michael Gani, Universitas Sanata Dharma, Yogyakarta, Indonesia.

Fakultas Farmasi, Universitas Sanata Dharma, Yogyakarta, Indonesia.

How to Cite
Istyastono, E., & Gani, M. (2021). Identification of Interactions of ABT-341 to Dipeptidyl Peptidase IV during Molecular Dynamics Simulations: Identikasi Interaksi-interaksi ABT-341 dengan Dipeptidil Peptidase IV pada Simulasi Dinamika Molekul. Jurnal Farmasi Galenika (Galenika Journal of Pharmacy) (e-Journal), 7(2), 91 - 98. https://doi.org/10.22487/j24428744.2021.v7.i2.15516

References

  1. Agrawal, P., Gautam, A., Pursnani, N., & Maheshwari, P. K. (2018). Teneligliptin, an economic and effective DPP-4 inhibitor for the management of type-2 diabetes mellitus: A comparative study. J. Assoc. Physicians India, 66(August), 67–69.
  2. Al-Masri, I. M., Mohammad, M. K., & Tahaa, M. O. (2009). Inhibition of dipeptidyl peptidase IV (DPP IV) is one of the mechanisms explaining the hypoglycemic effect of berberine. J. Enzyme Inhib. Med. Chem., 24(5), 1061–1066. https://doi.org/10.1080/14756360802610761
  3. Deng, Z., Chuaqui, C., & Singh, J. (2006). Knowledge-based design of target-focused libraries using protein-ligand interaction constraints. J. Med. Chem., 49(2), 490–500. https://doi.org/10.1021/jm050381x
  4. Fan, J., Johnson, M. H., Lila, M. A., Yousef, G., & De Mejia, E. G. (2013). Berry and citrus phenolic compounds inhibit dipeptidyl peptidase IV: Implications in diabetes management. Evid. Based Complement. Alternat. Med., 2013(479505), 1–13. https://doi.org/10.1155/2013/479505
  5. Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract., 103(2), 137–149. https://doi.org/10.1016/j.diabres.2013.11.002
  6. Hollingsworth, S. A., & Dror, R. O. (2018). Molecular Dynamics Simulation for All. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
  7. Istyastono, Enade P., Radifar, M., Yuniarti, N., Prasasty, V. D., & Mungkasi, S. (2020). PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS. J. Chem. Inf. Model., 60(8), 3697–3702. https://doi.org/10.1021/acs.jcim.0c00305
  8. Istyastono, Enade P, Yuniarti, N., Prasasty, V. D., & Mungkasi, S. (2021). PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors. Molecules, 26(2542), 1–12.
  9. Istyastono, Enade Perdana, & Prasasty, V. D. (2021). Computer-Aided Discovery of Pentapeptide AEYTR as a Potent Acetylcholinesterase Inhibitor. Indones. J. Chem., 21(1), 243–350.
  10. Korb, O., Stützle, T., & Exner, T. E. (2009). Empirical scoring functions for advanced protein-ligand docking with PLANTS. J. Chem. Inf. Model., 49(1), 84–96. https://doi.org/10.1021/ci800298z
  11. Krieger, E., & Vriend, G. (2015). New ways to boost molecular dynamics simulations. J. Comput. Chem., 36(13), 996–1007. https://doi.org/10.1002/jcc.23899
  12. Li, N., Wang, L. J., Jiang, B., Li, X., Guo, C., Guo, S., & Shi, D. (2018). Recent progress of the development of dipeptidyl peptidase-4 inhibitors for the treatment of type 2 diabetes mellitus. European Journal of Medicinal Chemistry, 151(10 May 2018), 145–157. https://doi.org/10.1016/j.ejmech.2018.03.041
  13. Liu, K., Watanabe, E., & Kokubo, H. (2017). Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J. Comput. Aided Mol. Des., 31(2), 201–211. https://doi.org/10.1007/s10822-016-0005-2
  14. Prasasty, V. D., & Istyastono, E. P. (2020). Structure-based design and molecular dynamics simulations of pentapeptide AEYTR as a potential acetylcholinesterase inhibitor. Indones. J. Chem., 20(4), 953–959. https://doi.org/10.22146/ijc.46329
  15. Rognan, D. (2012). Fragment-Based Approaches and Computer-Aided Drug Discovery. Top. Curr. Chem., 317, 201–222. https://doi.org/10.1007/128
  16. Salentin, S., Haupt, V. J., Daminelli, S., & Schroeder, M. (2014). Polypharmacology rescored: Protein-ligand interaction profiles for remote binding site similarity assessment. Prog. Biophys. Mol. Biol., 116(2–3), 174–186. https://doi.org/10.1016/j.pbiomolbio.2014.05.006
  17. ten Brink, T., & Exner, T. E. (2009). Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results. J. Chem. Inf. Model., 49(6), 1535–1546. https://doi.org/10.1021/ci800420z
  18. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 31(2), 455–461. https://doi.org/10.1002/jcc
  19. Wang, B., Sun, Y., Sang, Y., Liu, X., & Liang, J. (2018). Comparison of dipeptidyl peptidase-4 inhibitors and pioglitazone combination therapy versus pioglitazone monotherapy in type 2 diabetes: A system review and meta-analysis. Medicine, 97(46), e12633. https://doi.org/10.1097/MD.0000000000012633
  20. Zhao, Z., Xie, L., Xie, L., & Bourne, P. E. (2016). Delineation of polypharmacology across the human structural kinome using a functional site interaction fingerprint approach. J. Med. Chem., 59(9), 4326–4341. https://doi.org/10.1021/acs.jmedchem.5b02041
  21. Zheng, Y., Ley, S. H., & Hu, F. B. (2018). Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol., 14(2), 88–98. https://doi.org/10.1038/nrendo.2017.151