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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Virtual Screening and In Silico Simulation Analysis for Rapid and Efficient Identification of Novel Natural GPR40 Agonist

Author(s): Virendra Nath, Rohini Ahuja and Vipin Kumar*

Volume 17, Issue 5, 2020

Page: [533 - 546] Pages: 14

DOI: 10.2174/1570180815666180914162935

Price: $65

Abstract

Background: Diabetes is the foremost health problem worldwide predisposing to increased mortality and morbidity. The available synthetic drugs have serious side effects and thus, emphasize further need to develop effective medication therapy. GPR40 represents an interesting target for developing novel antidiabetic drug. In the current study, searching of potential natural hit candidate as agonist by using structure based computational approach.

Methods: The GPR40 agonistic activity of natural compounds was searched by using Maestro through docking and Molecular Dynamics (MD) simulation application. Virtual screening by using IBScreen library of natural compounds was done and the binding modes of newer natural entity(s) were investigated. Further, MD studies of the GPR40 complex with the most promising hit found in this study justified the stability of these complexes.

Results: The silicone chip-based approach recognized the most capable six hits and the ADME prediction aided the exploration of their pharmacokinetic potential. In this study, the obtained hit (ZINC70692253) after the use of exhaustive screening having binding energy -107.501 kcal/mol and root mean square deviation of hGPR40-ZINC70692253 is around 3.5 Å in 20 ns of simulation.

Conclusion: Successful application of structure-based computational screening gave a novel candidate from Natural Product library for diabetes treatment. So, Natural compounds may tend to cure diabetes with lesser extent of undesirable effects in comparison to synthetic compounds and these novel screened compounds may show a plausible biological response in the hit to lead finding of drug development process. To the best of our knowledge, this is the first example of the successful application of SBVS to discover novel natural hit compounds using hGPR40.

Keywords: GPR40, virtual screening, MD simulation, MMGBSA, in silico, ADME, SBVS.

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[1]
Fröde, T.S.; Medeiros, Y.S. Animal models to test drugs with potential antidiabetic activity. J. Ethnopharmacol., 2008, 115(2), 173-183.
[http://dx.doi.org/10.1016/j.jep.2007.10.038] [PMID: 18068921]
[2]
Jung, M.; Park, M.; Lee, H.C.; Kang, Y.H.; Kang, E.S.; Kim, S.K. Antidiabetic agents from medicinal plants. Curr. Med. Chem., 2006, 13(10), 1203-1218.
[http://dx.doi.org/10.2174/092986706776360860] [PMID: 16719780]
[3]
Shen, J.; Xu, X.; Cheng, F.; Liu, H.; Luo, X.; Shen, J.; Chen, K.; Zhao, W.; Shen, X.; Jiang, H. Virtual screening on natural products for discovering active compounds and target information. Curr. Med. Chem., 2003, 10(21), 2327-2342.
[http://dx.doi.org/10.2174/0929867033456729] [PMID: 14529345]
[4]
Liu, J.J.; Wang, Y.; Ma, Z.; Schmitt, M.; Zhu, L.; Brown, S.P.; Dransfield, P.J.; Sun, Y.; Sharma, R.; Guo, Q.; Zhuang, R.; Zhang, J.; Luo, J.; Tonn, G.R.; Wong, S.; Swaminath, G.; Medina, J.C.; Lin, D.C.; Houze, J.B. Optimization of GPR40 agonists for type 2 diabetes. ACS Med. Chem. Lett., 2014, 5(5), 517-521.
[http://dx.doi.org/10.1021/ml400501x] [PMID: 24900872]
[5]
Sum, C.S.; Tikhonova, I.G.; Neumann, S.; Engel, S.; Raaka, B.M.; Costanzi, S.; Gershengorn, M.C. Identification of residues important for agonist recognition and activation in GPR40. J. Biol. Chem., 2007, 282(40), 29248-29255.
[http://dx.doi.org/10.1074/jbc.M705077200] [PMID: 17699519]
[6]
Srivastava, A.; Yano, J.; Hirozane, Y.; Kefala, G.; Gruswitz, F.; Snell, G.; Lane, W.; Ivetac, A.; Aertgeerts, K.; Nguyen, J.; Jennings, A.; Okada, K. High-resolution structure of the human GPR40 receptor bound to allosteric agonist TAK-875. Nature, 2014, 513(7516), 124-127.
[http://dx.doi.org/10.1038/nature13494] [PMID: 25043059]
[7]
Shonberg, J.; Kling, R.C.; Gmeiner, P.; Löber, S. GPCR crystal structures: Medicinal chemistry in the pocket. Bioorg. Med. Chem., 2015, 23(14), 3880-3906.
[http://dx.doi.org/10.1016/j.bmc.2014.12.034] [PMID: 25638496]
[8]
Rives, M-L.; Rady, B.; Swanson, N.; Zhao, S.; Qi, J.; Arnoult, E.; Bakaj, I.; Mancini, A.; Breton, B.; Lee, S.P.; Player, M.R.; Pocai, A. GPR40-mediated gα12 activation by allosteric full agonists highly efficacious at potentiating glucose-stimulated insulin secretion in human islets. Mol. Pharmacol., 2018, 93(6), 581-591.
[http://dx.doi.org/10.1124/mol.117.111369] [PMID: 29572336]
[9]
Mohammad, S. GPR40 agonists for the treatment of type 2 Diabetes Mellitus: Benefits and challenges. Curr. Drug Targets, 2016, 17(11), 1292-1300.
[http://dx.doi.org/10.2174/1389450117666151209122702] [PMID: 26648068]
[10]
Darwish, K.M.; Salama, I.; Mostafa, S.; Gomaa, M.S.; Khafagy, E.S.; Helal, M.A. Synthesis, biological evaluation, and molecular docking investigation of benzhydrol- and indole-based dual PPAR-γ/FFAR1 agonists. Bioorg. Med. Chem. Lett., 2018, 28(9), 1595-1602.
[http://dx.doi.org/10.1016/j.bmcl.2018.03.051] [PMID: 29615345]
[11]
Mancini, A.D.; Poitout, V. GPR40 agonists for the treatment of type 2 diabetes: Life after ‘TAKing’ a hit. Diabetes Obes. Metab., 2015, 17(7), 622-629.
[http://dx.doi.org/10.1111/dom.12442] [PMID: 25604916]
[12]
Tsuda, N.; Kawaji, A.; Sato, T.; Takagi, M.; Higashi, C.; Kato, Y.; Ogawa, K.; Naba, H.; Ohkouchi, M.; Nakamura, M.; Hosaka, Y.; Sakaki, J. A novel free fatty acid receptor 1 (GPR40/FFAR1) agonist, MR1704, enhances glucose-dependent insulin secretion and improves glucose homeostasis in rats. Pharmacol. Res. Perspect., 2017, 5(4), 1-12.
[http://dx.doi.org/10.1002/prp2.340] [PMID: 28805970]
[13]
Liu, H.; Li, Y.; Song, M.; Tan, X.; Cheng, F.; Zheng, S.; Shen, J.; Luo, X.; Ji, R.; Yue, J.; Hu, G.; Jiang, H.; Chen, K. Structure-based discovery of potassium channel blockers from natural products: Virtual screening and electrophysiological assay testing. Chem. Biol., 2003, 10(11), 1103-1113.
[http://dx.doi.org/10.1016/j.chembiol.2003.10.011] [PMID: 14652078]
[14]
Grover, J.K.; Yadav, S.; Vats, V. Medicinal plants of India with anti-diabetic potential. J. Ethnopharmacol., 2002, 81(1), 81-100.
[http://dx.doi.org/10.1016/S0378-8741(02)00059-4] [PMID: 12020931]
[15]
Lee, Y.S.; Kim, W.S.; Kim, K.H.; Yoon, M.J.; Cho, H.J.; Shen, Y.; Ye, J.M.; Lee, C.H.; Oh, W.K.; Kim, C.T.; Hohnen-Behrens, C.; Gosby, A.; Kraegen, E.W.; James, D.E.; Kim, J.B. Berberine, a natural plant product, activates AMP-activated protein kinase with beneficial metabolic effects in diabetic and insulin-resistant states. Diabetes, 2006, 55(8), 2256-2264.
[http://dx.doi.org/10.2337/db06-0006] [PMID: 16873688]
[16]
Hung, H.Y.; Qian, K.; Morris-Natschke, S.L.; Hsu, C.S.; Lee, K.H. Recent discovery of plant-derived anti-diabetic natural products. Nat. Prod. Rep., 2012, 29(5), 580-606.
[http://dx.doi.org/10.1039/c2np00074a] [PMID: 22491825]
[17]
Sastry, G.M.; 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]
[18]
Lu, S.Y.; Jiang, Y.J.; Lv, J.; Wu, T.X.; Yu, Q.S.; Zhu, W.L. Molecular docking and molecular dynamics simulation studies of GPR40 receptor-agonist interactions. J. Mol. Graph. Model., 2010, 28(8), 766-774.
[http://dx.doi.org/10.1016/j.jmgm.2010.02.001] [PMID: 20227312]
[19]
Kawatkar, S.; Wang, H.; Czerminski, R.; Joseph-McCarthy, D. Virtual fragment screening: An exploration of various docking and scoring protocols for fragments using Glide. J. Comput. Aided Mol. Des., 2009, 23(8), 527-539.
[http://dx.doi.org/10.1007/s10822-009-9281-4] [PMID: 19495993]
[20]
Chen, I.J.; Foloppe, N. Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: Comparison to programs MOE and catalyst. J. Chem. Inf. Model., 2010, 50(5), 822-839.
[http://dx.doi.org/10.1021/ci100026x] [PMID: 20423098]
[21]
Sirin, S.; Kumar, R.; Martinez, C.; Karmilowicz, M.J.; Ghosh, P.; Abramov, Y.A.; Martin, V.; Sherman, W. A computational approach to enzyme design: Predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring. J. Chem. Inf. Model., 2014, 54(8), 2334-2346.
[http://dx.doi.org/10.1021/ci5002185] [PMID: 25005922]
[22]
Huang, Z.; Wong, C.F. inexpensive method for selecting receptor structures for virtual screening. J. Chem. Inf. Model., 2016, 56(1), 21-34.
[http://dx.doi.org/10.1021/acs.jcim.5b00299] [PMID: 26651874]
[23]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov., 2015, 10(5), 449-461.
[http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]
[24]
Malik, R.; Bunkar, D.; Choudhary, B.S.; Srivastava, S.; Mehta, P.; Sharma, M. High throughput virtual screening and in silico ADMET analysis for rapid and efficient identification of potential PAP248-286 aggregation inhibitors as anti-HIV agents. J. Mol. Struct., 2016, 1122, 239-246.
[http://dx.doi.org/10.1016/j.molstruc.2016.05.086]
[25]
Vyas, V.K.; Ghate, M.; Goel, A. Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB β) inhibitors. J. Mol. Graph. Model., 2013, 42, 17-25.
[http://dx.doi.org/10.1016/j.jmgm.2013.01.010] [PMID: 23507201]
[26]
Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph., 1996, 14(1), 33-38, 27-28.
[http://dx.doi.org/10.1016/0263-7855(96)00018-5] [PMID: 8744570]
[27]
Yki-Järvinen, H. Thiazolidinediones. N. Engl. J. Med., 2004, 351(11), 1106-1118.
[http://dx.doi.org/10.1056/NEJMra041001] [PMID: 15356308]
[28]
Willson, T.M.; Cobb, J.E.; Cowan, D.J.; Wiethe, R.W.; Correa, I.D.; Prakash, S.R.; Beck, K.D.; Moore, L.B.; Kliewer, S.A.; Lehmann, J.M. The structure-activity relationship between peroxisome proliferator-activated receptor γ agonism and the antihyperglycemic activity of thiazolidinediones. J. Med. Chem., 1996, 39(3), 665-668.
[http://dx.doi.org/10.1021/jm950395a] [PMID: 8576907]
[29]
Kong, A.P.S.; Yamasaki, A.; Ozaki, R.; Saito, H.; Asami, T.; Ohwada, S.; Ko, G.T.C.; Wong, C.K.; Leung, G.T.C.; Lee, K.F.; Yeung, C.Y.; Chan, J.C. A randomized-controlled trial to investigate the effects of rivoglitazone, a novel PPAR gamma agonist on glucose-lipid control in type 2 diabetes. Diabetes Obes. Metab., 2011, 13(9), 806-813.
[http://dx.doi.org/10.1111/j.1463-1326.2011.01411.x] [PMID: 21492364]
[30]
Henry, W.L. Perspectives in diabetes. J. Natl. Med. Assoc., 1962, 54(1), 476-478.
[PMID: 13906557]
[31]
Oh, D.Y.; Olefsky, J.M. G protein-coupled receptors as targets for anti-diabetic therapeutics. Nat. Rev. Drug Discov., 2016, 15, 161-172.
[http://dx.doi.org/ [https://doi.org/10.1038/nrd.2015.4]

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