Pharmacophore-based virtual screening of bioactive peptides as dipeptidyl peptidase 4 inhibitor for type 2 diabetes mellitus drug candidates
DOI:
https://doi.org/10.46542/pe.2024.242.145151Keywords:
Anti-diabetes, Bioactive peptide, DPP-4 inhibitor, Virtual screeningAbstract
Background: Diabetes is a chronic disease characterised by high blood sugar levels. Glucose that accumulates in the blood without being properly absorbed by the body's cells can cause various organ problems. If diabetes is not properly controlled, various complications can occur that endanger the life of the affected person.
Objective: To find bioactive peptides that have the potential to inhibit di-peptidyl peptidase 4 (DPP-4) enzyme as anti-diabetes drug candidates.
Method: This research was carried out using pharmacophore-based virtual screening.
Result: The validation of the pharmacophore-based virtual screening method showed that model III, which had five pharmacophore features consisting of three Pi interactions, one hydrogen bond donor, and three hydrogen acceptors, was the best pharmacophore model with the values of AUC 0.59; EF 1.2; Se 0.69; Sp 0.94; ACC 0.84; Ya 0.06; and GH 0.2. The screening of the 168,400 short-chain peptides using validated pharmacophore model III gave 51 tetrapeptides as the hits compounds with a pharmacophore fit score of more than 50.0%.
Conclusion: In total, 51 tetrapeptides were enlisted as potential as anti-diabetes mellitus drug candidates.
References
American Diabetes Association. (2018). Pharmacologic approaches to glycemic treatment: Standards of medical care in Diabetes d2018. Diabetes Care, 41, S73–S85. https://doi.org/10.2337/dc18-S008
Berger, J. P., SinhaRoy, R., Pocai, A., Kelly, T. M., Scapin, G., Gao, Y.-D., Pryor, K. A. D., Wu, J. K., Eiermann, G. J., Xu, S. S., Zhang, X., Tatosian, D. A., Weber, A. E., Thornberry, N. A., & Carr, R. D. (2018). 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. Endocrinology, Diabetes & Metabolism, 1(1), e00002. https://doi.org/10.1002/edm2.2
Braga, R. C., & Andrade, C. H. (2013). Assessing the performance of 3D pharmacophore models in virtual screening: How good are they? Current Topics in Medicinal Chemistry, 13(9), 1127–1138. https://doi.org/10.2174/1568026611313090010
Chen, Z., Li, H. L., Zhang, Q. J., Bao, X. G., Yu, K. Q., Luo, X. M., Zhu, W. L., & Jiang, H. L. (2009). Pharmacophore-based virtual screening versus docking-based virtual screening: A benchmark comparison against eight targets. Acta Pharmacologica Sinica, 30(12), 1694–1708. https://doi.org/10.1038/aps.2009.159
DiPiro, J. T., Talbert, R. L., Yee, G. C., Matzke, G. R., Wells, B. G., & Posey, L. M. (2005). Pharmacotherapy: A pathophysiologic approach. The McGraw-Hill Companies, Inc. https://usahidsolo.ac.id/digilib/repository/Dipiro_Pharmacotherapy_7th.pdf
Fells, J. I., Lee, S. C., Norman, D. D., Tsukahara, R., Kirby, J. R., Nelson, S., Seibel, W., Papoian, R., Patil, R., Miller, D. D., Parrill, A. L., Pham, T. C., Baker, D. L., Bittman, R., & Tigyi, G. (2014). Targeting the hydrophobic pocket of autotaxin with a virtual screening of inhibitors identifies a common aromatic sulfonamide structural motif. FEBS Journal, 281(4), 1017–1028. https://doi.org/10.1111/febs.12674
Guan, W., Liang, W., Zhao, Y., Lian, H., Chen, Z., Li, Y., Liu, X., Chen, R., Tang, C., Wang, T., Ou, C., Li, L., Che, P., Sang, L., Wang, M., Li, J., Li, C., Ou, L., Cheng, B., & He, J. (2020). Comorbidity and its impact on 1,590 patients with Covid-19 in China: A Nationwide Analysis. In European Respiratory Journal, 55(5), 640. https://doi.org/10.1183/13993003.00547-2020
Guo, W., Li, M., Dong, Y., Zhou, H., Zhang, Z., Tian, C., Qin, R., Wang, H., Shen, Y., Du, K., Zhao, L., Fan, H., Luo, S., & Hu, D. (2020). Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes/Metabolism Research and Reviews, 36(7). https://doi.org/10.1002/dmrr.3319
Hocher, B., Sharkovska, Y., Mark, M., Klein, T., & Pfab, T. (2013). The novel DPP-4 inhibitors linagliptin and BI 14361 reduce infarct size after myocardial ischemia/reperfusion in rats. International Journal of Cardiology, 167(1), 87–93. https://doi.org/10.1016/j.ijcard.2011.12.007
International Diabetes Federation. (2015). IDF diabetes atlas 7th Edition. International Diabetes Federation. https://diabetesatlas.org/idfawp/resource-files/2012/07/IDF_diabetes_atlas_seventh_edition_en.pdf
Joung, J. Y., Lee, H. Y., Park, J., Lee, J. Y., Chang, B. H., No, K. T., Nam, K. Y., & Hwang, J. S. (2014). Identification of novel Rab27a/melanophilin blockers by pharmacophore-based virtual screening. Applied Biochemistry and Biotechnology, 172(4), 1882–1897. https://doi.org/10.1007/s12010-013-0615-2
Kim, D., Wang, L., Beconi, M., Eiermann, G. J., Fisher, M. H., He, H., Hickey, G. J., Kowalchick, J. E., Leiting, B., Lyons, K., Marsilio, F., McCann, M. E., Patel, R. A., Petrov, A., Scapin, G., Patel, S. B., Roy, R. S., Wu, J. K., Wyvratt, M. J., & Weber, A. E. (2005). (2R)-4-Oxo-4-[3-(trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a] pyrazin-7(8H)-yl]-1-(2,4,5-tri-fluorophenyl)butan-2-amine: A potent, orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Journal of Medicinal Chemistry, 48(1), 141–151. https://doi.org/10.1021/jm0493156
Liu, Y., Hu, Y., & Liu, T. (2012). Recent advances in non-peptidomimetic dipeptidyl peptidase 4 inhibitors: Medicinal chemistry and preclinical aspects. Current Medicinal Chemistry, 19(23), 3982-99. https://doi.org/10.2174/092986712802002491
Muttaqin, F. Z., Kharisma, D., Asnawi, A., & Kurniawan, F. (2020). Pharmacophore and molecular docking-based virtual screening of B-cell lymphoma 2 (BCL 2) inhibitor from zinc natural database as anti-small cell lung cancer. Journal of Drug Delivery and Therapeutics, 10(2), 143–147. https://doi.org/10.22270/jddt.v10i2.3923
Niu, M., Dong, F., Tang, S., Fida, G., Qin, J., Qiu, J., Liu, K., Gao, W., & Gu, Y. (2013). Pharmacophore modelling and virtual screening for the discovery of new type 4 cAMP phosphodiesterase (PDE4) inhibitors. PLoS ONE, 8(12). https://doi.org/10.1371/journal.pone.0082360
Ou, H. T., Chang, K. C., & Li, C. Y. (2016). Risks of cardiovascular diseases associated with dipeptidyl peptidase-4 inhibitors and other antidiabetic drugs in patients with type 2 diabetes: A nation-wide longitudinal study. Cardiovasc Diabetol, 15, 41. https://doi.org/10.1186/s12933-016-0350-4
Pinto, L., Rados, D., & Barkan, S. (2018). Dipeptidyl peptidase-4 inhibitors, pancreatic cancer and acute pancreatitis: A meta-analysis with trial sequential analysis. Science Report, 8(1), 782. https://doi.org/10.1038/s41598-017-19055-6
Prasasty, V. D., & Istyastono, E. P. (2019). Data of small peptides in SMILES and three-dimensional formats for virtual screening campaigns. Data in Brief, 27. https://doi.org/10.1016/j.dib.2019.104607
Tang, Y. T., & Marshall, G. R. (2011). Virtual screening for lead discovery. In Methods in molecular biology (716, pp. 1–22). Humana Press Inc. https://doi.org/10.1007/978-1-61779-012-6_1
Thomas, L., Eckhardt, M., Langkopf, E., Tadayyon, M., Himmelsbach, F., & Mark, M. (2008). (R)-8-(3-amino-piperidin-1-yl)-7-but-2-ynyl-3-methyl-1-(4-methyl- quinazolin-2-ylmethyl)-3,7-dihydro-purine-2,6-dione (BI 1356), a novel xanthine-based dipeptidyl peptidase 4 inhibitor, has a superior potency and longer duration of action compared with other dipeptidyl peptidase-4 inhibitors. Journal of Pharmacology and Experimental Therapeutics, 325(1), 175–182. https://doi.org/10.1124/jpet.107.135723
Triballeau, N., Acher, F., Brabet, I., Pin, J. P., & Bertrand, H. O. (2005). Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. Journal of Medicinal Chemistry, 48(7), 2534–2547. https://doi.org/10.1021/jm049092j
Wolber, G., & Langer, T. (2005). LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling, 45(1), 160–169. https://doi.org/10.1021/ci049885e
Zhang, Z., Chen, X., & Lu, P. (2017). Incretin-based agents in type 2 diabetic patients at cardiovascular risk: Compare the effect of GLP-1 agonists and DPP-4 inhibitors on cardiovascular and pancreatic outcomes. Cardiovascular diabetology, 16(1), 31. https://doi.org/10.1186/s12933-017-0512-z
