Login Register Cart 0
Generic placeholder image

Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Structure-based Virtual Screening and Molecular Dynamic Simulation Approach for the Identification of Terpenoids as Potential DPP-4 Inhibitors

Author(s): Ajay Aravind Pulikkottil, Amit Kumar, Kailash Jangid, Vinod Kumar and Vikas Jaitak*

Volume 20, Issue 4, 2024

Published on: 09 June, 2023

Page: [416 - 429] Pages: 14

DOI: 10.2174/1573409919666230515160502

Price: $65

Abstract

Background: Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery.

Objective: In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus.

Methods: Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions.

Results: According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies.

Conclusion: Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.

« Previous
Graphical Abstract

[1]
Association, A.D. Diagnosis and classification of diabetes mellitus. Diabetes Care, 2009, 32(Suppl. 1), S62-S67.
[http://dx.doi.org/10.2337/dc09-S062] [PMID: 19118289]
[2]
Salehi, B.; Ata, A. Sharopov; Ramírez-Alarcón; Ruiz-Ortega; Abdulmajid Ayatollahi; Tsouh Fokou; Kobarfard; Amiruddin Zakaria; Iriti; Taheri; Martorell; Sureda; Setzer; Durazzo; Lucarini; Santini; Capasso; Ostrander; Atta-ur-Rahman; Choudhary, M.I.; Cho, W.C.; Sharifi-Rad, J.; Anil Kumar, V. Antidiabetic potential of medicinal plants and their active components. Biomolecules, 2019, 9(10), 551.
[http://dx.doi.org/10.3390/biom9100551] [PMID: 31575072]
[3]
Turdu, G.; Gao, H.; Jiang, Y.; Kabas, M. Plant dipeptidyl peptidase-IV inhibitors as antidiabetic agents: A brief review. Future Med. Chem., 2018, 10(10), 1229-1239.
[http://dx.doi.org/10.4155/fmc-2017-0235] [PMID: 29749760]
[4]
Genuth, S.M.; Palmer, J.P.; Nathan, D.M. Classification and diagnosis of diabetes. In: Diabetes in America, 3rd ed.; National Institute of Diabetes and Digestive and Kidney Diseases (US): Bethesda (MD); , 2021.
[5]
Shah, B.M.; Modi, P.; Trivedi, P. Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors. J. Biomol. Struct. Dyn., 2021, 39(6), 2021-2043.
[http://dx.doi.org/10.1080/07391102.2020.1750485] [PMID: 32242496]
[6]
Kato, E. Bioactive compounds in plant materials for the prevention of diabetesand obesity. Biosci. Biotechnol. Biochem., 2019, 83(6), 975-985.
[http://dx.doi.org/10.1080/09168451.2019.1580560] [PMID: 30773997]
[7]
Laha, S.; Paul, S. Gymnema sylvestre (Gurmar): A potent herb with anti-diabetic and antioxidant potential. Pharmacogn. J., 2019, 11(2), 201-206.
[http://dx.doi.org/10.5530/pj.2019.11.33]
[8]
Williams, R.; Karuranga, S.; Malanda, B.; Saeedi, P.; Basit, A.; Besançon, S.; Bommer, C.; Esteghamati, A.; Ogurtsova, K.; Zhang, P.; Colagiuri, S. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract., 2020, 162, 108072.
[http://dx.doi.org/10.1016/j.diabres.2020.108072] [PMID: 32061820]
[9]
Lin, S.R.; Chang, C.H.; Tsai, M.J.; Cheng, H.; Chen, J.C.; Leong, M.K.; Weng, C.F. The perceptions of natural compounds against dipeptidyl peptidase 4 in diabetes: from in silico to in vivo. Ther. Adv. Chronic Dis., 2019, 10
[http://dx.doi.org/10.1177/2040622319875305] [PMID: 31555430]
[10]
Pathak, R.; Bridgeman, M.B. Dipeptidyl peptidase-4 (DPP-4) inhibitors in the management of diabetes. P&T, 2010, 35(9), 509-513.
[PMID: 20975810]
[11]
Patel, B.D.; Bhadada, S.V.; Ghate, M.D. Design, synthesis and anti-diabetic activity of triazolotriazine derivatives as dipeptidyl peptidase-4 (DPP-4) inhibitors. Bioorg. Chem., 2017, 72, 345-358.
[http://dx.doi.org/10.1016/j.bioorg.2017.03.004] [PMID: 28302310]
[12]
Chen, S. The pharmacological effects of triterpenoids from Ganoderma lucidum and the regulation of its biosynthesis. Adv. Biol. Chem., 2020, 10(2), 55-65.
[http://dx.doi.org/10.4236/abc.2020.102005]
[13]
Paul, R.K.; Nath, V.; Kumar, V. Structure based virtual screening of natural compounds and molecular dynamics simulation: Butirosin as Dipeptidyl peptidase (DPP-IV) inhibitor. Biocatal. Agric. Biotechnol., 2021, 35, 102042.
[http://dx.doi.org/10.1016/j.bcab.2021.102042]
[14]
Yong-Lin, W.; Yan, Z.; Yan, T.; Yuan-Fang, K.; Yu-Long, HU.; Jie-Ming, LI.; Shao-Pei, W.; Chun-Hong, D.; Xiao-Fei, LI. Exploring of hypoglycemic mechanism of a Chinese Medicine Xiao-Ke-An based on target dipeptidyl peptidase-4: A molecular docking and molecular dynamics study., 2021.
[15]
Tahrani, A.A.; Bailey, C.J.; Del Prato, S.; Barnett, A.H. Management of type 2 diabetes: New and future developments in treatment. Lancet, 2011, 378(9786), 182-197.
[http://dx.doi.org/10.1016/S0140-6736(11)60207-9] [PMID: 21705062]
[16]
Upadhyay, S.; Dixit, M. Role of polyphenols and other phytochemicals on molecular signaling. Oxid. Med. Cell. Longev., 2015, 2015.
[http://dx.doi.org/10.1155/2015/504253]
[17]
Pàmies, L.G. Identification of natural products as antidiabetic agents using computer-aided drug design methods; Universitat Rovira i Virgili, 2011.
[18]
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. 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.
[http://dx.doi.org/10.1002/edm2.2] [PMID: 30815539]
[19]
Schnapp, G.; Klein, T.; Hoevels, Y.; Bakker, R.A.; Nar, H. Comparative analysis of binding kinetics and thermodynamics of dipeptidyl peptidase-4 inhibitors and their relationship to structure. J. Med. Chem., 2016, 59(16), 7466-7477.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00475] [PMID: 27438064]
[20]
Yoshida, T.; Akahoshi, F.; Sakashita, H.; Sonda, S.; Takeuchi, M.; Tanaka, Y.; Nabeno, M.; Kishida, H.; Miyaguchi, I.; Hayashi, Y. Fused bicyclic heteroarylpiperazine-substituted l-prolylthiazolidines as highly potent DPP-4 inhibitors lacking the electrophilic nitrile group. Bioorg. Med. Chem., 2012, 20(16), 5033-5041.
[http://dx.doi.org/10.1016/j.bmc.2012.06.033] [PMID: 22824762]
[21]
Kaur, K.K.; Allahbadia, G.; Singh, M. Monoterpenes-a class of terpenoid group of natural products as a source of natural antidiabetic agents in the future—A review. CPQ Nutrition, 2019, 3(4), 1-21.
[22]
Perveen, S.; Al-Taweel, A. Terpenes and terpenoids; BoD–Books on Demand, 2018.
[http://dx.doi.org/10.5772/intechopen.71175]
[23]
Putta, S.; Sastry Yarla, N.; Kumar Kilari, E.; Surekha, C.; Aliev, G.; Basavaraju Divakara, M.; Sridhar Santosh, M.; Ramu, R.; Zameer, F.; Prasad, M.N. N.; Chintala, R.; Vijaya Rao, P.; Shiralgi, Y.; Lakkappa Dhananjaya, B.N Therapeutic potentials of triterpenes in diabetes and its associated complications. Curr. Top. Med. Chem., 2016, 16(23), 2532-2542.
[http://dx.doi.org/10.2174/1568026616666160414123343] [PMID: 27086788]
[24]
Tsiaka, T.; Kritsi, E.; Tsiantas, K.; Christodoulou, P.; Sinanoglou, V.J.; Zoumpoulakis, P. Design and development of novel nutraceuticals: Current trends and methodologies. Nutraceuticals, 2022, 2(2), 71-90.
[http://dx.doi.org/10.3390/nutraceuticals2020006]
[25]
Sorokina, M.; Merseburger, P.; Rajan, K.; Yirik, M.A.; Steinbeck, C. COCONUT online: Collection of open natural products database. J. Cheminform., 2021, 13(1), 2.
[http://dx.doi.org/10.1186/s13321-020-00478-9] [PMID: 33423696]
[26]
Capecchi, A.; Reymond, J.L. Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning. J. Cheminform., 2021, 13(1), 82.
[http://dx.doi.org/10.1186/s13321-021-00559-3] [PMID: 34663470]
[27]
Alshehri, B.; Vijayakumar, R.; Senthilkumar, S.; Ismail, A.; Abdelhadi, A.; Choudhary, R.K.; Albenasy, K.S.; Banawas, S.; Alaidarous, M.A.; Manikandan, P. Molecular target prediction and docking of anti-thrombosis compounds and its activation on tissue-plasminogen activator to treat stroke. J. King Saud Univ. Sci., 2022, 34(1), 101732.
[http://dx.doi.org/10.1016/j.jksus.2021.101732]
[28]
Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G.A.; Dahlgren, M.K.; Russell, E.; Von Bargen, C.D.; Abel, R.; Friesner, R.A.; Harder, E.D. OPLS4: Improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput., 2021, 17(7), 4291-4300.
[http://dx.doi.org/10.1021/acs.jctc.1c00302] [PMID: 34096718]
[29]
Madhavi Sastry, G.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[30]
Teli, D.M.; Shah, M.B.; Chhabria, M.T. In silico screening of natural compounds as potential inhibitors of SARS-CoV-2 main protease and spike RBD: Targets for COVID-19. Front. Mol. Biosci., 2021, 7, 599079.
[http://dx.doi.org/10.3389/fmolb.2020.599079] [PMID: 33542917]
[31]
Rohane, S.H.; Makwana, A.G. In silico study for the prediction of multiple pharmacological activities of novel hydrazone derivatives. Indian J. Chem., 2019, 58, 387-402.
[32]
Greenwood, J.R.; Calkins, D.; Sullivan, A.P.; Shelley, J.C. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided Mol. Des., 2010, 24(6-7), 591-604.
[http://dx.doi.org/10.1007/s10822-010-9349-1] [PMID: 20354892]
[33]
Shelley, J.C.; Cholleti, A.; Frye, L.L.; Greenwood, J.R.; Timlin, M.R.; Uchimaya, M. Epik: A software program for pK a prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des., 2007, 21(12), 681-691.
[http://dx.doi.org/10.1007/s10822-007-9133-z] [PMID: 17899391]
[34]
Marondedze, E.F.; Govender, K.K.; Govender, P.P. Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules. J. Mol. Graph. Model., 2020, 101, 107711.
[http://dx.doi.org/10.1016/j.jmgm.2020.107711] [PMID: 32898834]
[35]
Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47(7), 1750-1759.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
[36]
Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model., 2010, 29(2), 157-170.
[http://dx.doi.org/10.1016/j.jmgm.2010.05.008] [PMID: 20579912]
[37]
Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model., 2010, 50(5), 771-784.
[http://dx.doi.org/10.1021/ci100062n] [PMID: 20450209]
[38]
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), 1509-1519.
[http://dx.doi.org/10.1021/ct900587b] [PMID: 26615687]
[39]
Li, J.; Abel, R.; Zhu, K.; Cao, Y.; Zhao, S.; Friesner, R.A. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins, 2011, 79(10), 2794-2812.
[http://dx.doi.org/10.1002/prot.23106] [PMID: 21905107]
[40]
Croitoru, A.; Park, S.J.; Kumar, A.; Lee, J. Im, W.; MacKerell, A.D., Jr; Aleksandrov, A. Additive CHARMM36 force field for nonstandard amino acids. J. Chem. Theory Comput., 2021, 17(6), 3554-3570.
[http://dx.doi.org/10.1021/acs.jctc.1c00254] [PMID: 34009984]
[41]
Evans, D.J.; Holian, B.L. The nose–hoover thermostat. J. Chem. Phys., 1985, 83(8), 4069-4074.
[http://dx.doi.org/10.1063/1.449071]
[42]
Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; DiNola, A.; Haak, J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys., 1984, 81(8), 3684-3690.
[http://dx.doi.org/10.1063/1.448118]
[43]
Lin, X.; Xu, Y.; Pan, X.; Xu, J.; Ding, Y.; Sun, X.; Song, X.; Ren, Y.; Shan, P.F. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci. Rep., 2020, 10(1), 14790.
[http://dx.doi.org/10.1038/s41598-020-71908-9] [PMID: 32901098]
[44]
Chen, D.; Oezguen, N.; Urvil, P.; Ferguson, C.; Dann, S.M.; Savidge, T.C. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci. Adv., 2016, 2(3), e1501240.
[http://dx.doi.org/10.1126/sciadv.1501240] [PMID: 27051863]
[45]
Kufareva, I.; Abagyan, R. Methods of Protein Structure Comparison. In: Homology Modeling: Methods and Protocols; Orry, A.J.W.; Abagyan, R., Eds.; Humana Press: Totowa, NJ, 2012; pp. 231-257.
[46]
Prashant, S.; Murthy, D.K. In silico evaluation of 2, 4-diaminoquinazoline derivatives as possible anticancer agents. Curr. Trends Biotechnol. Pharm., 2022, 16(1), 14-22.
[47]
Mendie, L.E.; Hemalatha, S. Molecular docking of phytochemicals targeting gfrs as therapeutic sites for cancer: An in silico study. Appl. Biochem. Biotechnol., 2022, 194(1), 215-231.
[http://dx.doi.org/10.1007/s12010-021-03791-7] [PMID: 34988844]
[48]
Baber, J.; Feher, M. Predicting synthetic accessibility: Application in drug discovery and development. Mini Rev. Med. Chem., 2004, 4(6), 681-692.
[http://dx.doi.org/10.2174/1389557043403765] [PMID: 15279602]
[49]
Karmakar, S.; Basak, H.K.; Paswan, U.; Pramanik, A.K.; Chatterjee, A. Designing of next-generation dihydropyridine-based calcium channel blockers: An in silico study. J. Appl. Pharm. Sci., 2022, 12(04), 127-135.
[http://dx.doi.org/10.7324/JAPS.2022.120414]
[50]
Warr, W.A. A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility. Mol. Inform., 2014, 33(6-7), 469-476.
[http://dx.doi.org/10.1002/minf.201400052] [PMID: 27485985]
[51]
Geetha, R.V.; Roy, A. Essential oil repellents-a short review. Int. J. Drug Dev. Res., 2014, 6, 20-27.
[52]
Peng, J.; Franzblau, S.G.; Zhang, F.; Hamann, M.T. Novel sesquiterpenes and a lactone from the Jamaican sponge Myrmekioderma styx. Tetrahedron Lett., 2002, 43(52), 9699-9702.
[http://dx.doi.org/10.1016/S0040-4039(02)02369-9]
[53]
Serra, S. Enantioselective synthesis of the bisabolane sesquiterpene (+)-1-hydroxy-1,3,5-bisabolatrien-10-one and revision of its absolute configuration. Nat. Prod. Commun., 2012, 7(4), 1934578X1200700.
[http://dx.doi.org/10.1177/1934578X1200700409] [PMID: 22574440]
[54]
Vernin, G.; Lageot, C.; Gaydou, E.M.; Parkanyi, C. Analysis of the essential oil ofLippia graveolens HBK from El Salvador. Flavour Fragrance J., 2001, 16(3), 219-226.
[http://dx.doi.org/10.1002/ffj.984]
[55]
Zdero, C.; Bohlmann, F.; Niemeyer, H.M. Sesquiterpene lactones from Perityle emoryi. Phytochemistry, 1990, 29(3), 891-894.
[http://dx.doi.org/10.1016/0031-9422(90)80040-N]
[56]
Cox-Georgian, D.; Ramadoss, N.; Dona, C.; Basu, C. Therapeutic and medicinal uses of terpenes. J. Med. Plant Res., 2019, 333-359.
[57]
Brahmachari, G. Andrographolide: A Molecule of Antidiabetic Promise. In: Discovery and Development of Antidiabetic Agents from Natural Products; Brahmachari, G., Ed.; Elsevier, 2017; pp. 1-27.
[http://dx.doi.org/10.1016/B978-0-12-809450-1.00001-6]
[58]
Salim, B.; Hocine, A.; Said, G. First study on anti-diabetic effect of rosemary and salvia by using molecular docking. J. Pharm. Res., 2017, 1-12.
[59]
Sette-de-Souza, P. H.; Souza, B. A. A.; Costa, M. J. F.; da Costa Araújo, F. A. Kuguacin: biological activities of triterpenoid from Momordica charantia-a scoping review. Adv. Trad. Med.,, 2021, 1-8.
[60]
Singla, R.; Singla, N.; Jaitak, V. Stevia rebaudiana targeting α-amylase: An in-vitro and in-silico mechanistic study. Nat. Prod. Res., 2019, 33(4), 548-552.
[http://dx.doi.org/10.1080/14786419.2017.1395433] [PMID: 29072099]
[61]
Matsabisa, M.G.; Chukwuma, C.I.; Chaudhary, S.K.; Kumar, C.S.; Baleni, R.; Javu, M.; Oyedemi, S.O. Dicoma anomala (Sond.) abates glycation and DPP-IV activity and modulates glucose utilization in Chang liver cells and 3T3-L1 adipocytes. S. Afr. J. Bot., 2020, 128, 182-188.
[http://dx.doi.org/10.1016/j.sajb.2019.09.013]
[62]
Kato, E.; Kawakami, K.; Kawabata, J. Macrocarpal C isolated from Eucalyptus globulus inhibits dipeptidyl peptidase 4 in an aggregated form. J. Enzyme Inhib. Med. Chem., 2018, 33(1), 106-109.
[http://dx.doi.org/10.1080/14756366.2017.1396458] [PMID: 29148282]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy