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G-protein-coupled receptors function as logic gates for nanoparticle binding using systems and synthetic biology approach

  • Aman Chandra Kaushik (a1), Xueying Mao (a2), Cheng-Dong Li (a1), Yan Li (a3), Dong-Qing Wei (a1) and Shakti Sahi (a4)...


G-protein-coupled receptor 142 (GPR142) belongs to rhodopsin family. GPR142 and GPR119, both Gq-coupled receptors, are expressed in pancreatic β cells of pancreas; their activation eventually leads to triggering of insulin secretion. In this paper, through a systems and synthetic biology approach, the effect of a common hit compound has been investigated in GPR142 and GPR119 pathways. This hit that has the potential to be developed as a lead for nanodrug was obtained through high-throughput virtual screening. The hit compound was further docked with nanoparticles (GOLD, SPION, and CeO2). The probable effect of this potential hit on insulin secretion in type 2 diabetes and its dynamic behavior was explored. Kinetic simulation was performed for cross-validation of its role in both the pathways. This study opens up a probable avenue in therapy of type 2 diabetes through regulation of GPR142 and GPR119 receptors. The biological circuit constructed may further have an application as a modulator to control the up- and downregulation of the biochemical pathway and can be implemented as sensors or nanochips for therapy.


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1.Zhu, X., Huang, W., and Qian, H.: GPR119 agonists: a novel strategy for type 2 diabetes treatment. In: Oluwafemi O. Oguntibeju, editor. Diabetes mellitus–insights and perspectives. Rijeka: InTech; 2013. doi: 10.5772/48444. Available from:
2.Overton, H., Fyfe, M., and Reynet, C.: GPR119, a novel G protein-coupled receptor target for the treatment of type 2 diabetes and obesity. Br. J. Pharmacol. 153, S76 (2008).
3.Kahn, S.E., Hull, R.L., and Utzschneider, K.M.: Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840 (2006).
4.Kaushik, A.C., Kumar, S., Wei, D.Q., and Sahi, S.: Structure based virtual screening studies to identify novel potential compounds for GPR142 and their relative dynamic analysis for study of type 2 diabetes. Front. Chem. 6, 23 (2018).
5.Fredriksson, R., Höglund, P.J., Gloriam, D.E., Lagerström, M.C., and Schiöth, H.B.: Seven evolutionarily conserved human rhodopsin G protein-coupled receptors lacking close relatives. FEBS Lett. 554, 381 (2003).
6.Shah, U. and Kowalski, T.J.: GPR119 Agonists for the Potential Treatment of Type 2 Diabetes and Related Metabolic Disorders. Vitamins & Hormones, 84, 415448, Academic Press, Cambridge, Massachusetts, 2010.
7.Lu, M., Jolly, M.K., and Ben-Jacob, E.: Toward decoding the principles of cancer metastasis circuits. Cancer Res. 74, 4574 (2014).
8.Jolly, M.K., Huang, B., Lu, M., Mani, S.A., Levine, H., and Ben-Jacob, E.: Towards elucidating the connection between epithelial-mesenchymal transitions and stemness. J. R. Soc. Interface 11, 20140962 (2014).
9.Kiel, C., Yus, E., and Serrano, L.: Engineering signal transduction pathways. Cell 140, 33 (2010).
10.Bhalla, U.S. and Iyengar, R.: Emergent properties of networks of biological signaling pathways. Science 283, 381 (1999).
11.McMillen, D., Kopell, N., Hasty, J., and Collins, J.: Synchronizing genetic relaxation oscillators by inter cell signaling. Proc. Natl. Acad. Sci. U. S. A 99, 679 (2002).
12.Densmore, D. and Hassoun, S.: Design automation for synthetic biological systems. IEEE Des. Test Comput. Mag. 29, 7 (2012).
13.Hasty, J., Isaacs, F., Dolnik, M., McMillen, D., and Collins, J.J.: Designer gene networks: Toward fundamental cellular control. Chaos 11, 207 (2001).
14.Davidson, E.H., Rast, J.P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., Otim, O.: A genomic regulatory network for development. Science 295, 1669 (2002).
15.Ozbudak, E.M., Thattai, M., Kurtser, I., Grossman, A.D., and Van Oudenaarden, A.: Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69 (2002).
16.Kitano, H.: Computational systems biology. Nature 420, 206 (2002).
17.Khalil, A.S. and Collins, J.J.: Synthetic biology: Applications come of age. Nat. Rev. Genet. 11, 367 (2010).
18.Friesner, R.A., Murphy, R.B., Repasky, M.P., Frye, L.L., Greenwood, J.R., Halgren, T.A., Sanschagrin, P.C., and Mainz, D.T.: Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. J. Med. Chem. 49, 61776196 (2006).
19.Halgren, T.A., Murphy, R.B., Friesner, R.A., Beard, H.S., Frye, L.L., Pollard, W.T., and Banks, J.L.: Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 17501759 (2004).
20.Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A., Klicic, J.J., Mainz, D.T., Repasky, M.P., Knoll, E.H., Shelley, M., and Perry, J.K.: Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 17391749 (2004).
21.Farid, R., Day, T., Friesner, R.A., and Pearlstein, R.A.: New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg. Med. Chem. 14, 31603173 (2006).
22.Sherman, W., Day, T., Jacobson, M.P., Friesner, R.A., and Farid, R.: Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem. 49, 534553 (2006).
23.Sherman, W., Beard, H.S., and Farid, R.: Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 67, 8384 (2006).
24.Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., and Kummer, U.: COPASI—A complex pathway simulator. Bioinformatics 22, 30673074 (2006).
25.Inubushi, T., Kamemura, N., Oda, M., Sakurai, J., Nakaya, Y., Harada, N., Suenaga, M., Matsunaga, Y., Ishidoh, K., and Katunuma, N.: L-tryptophan suppresses rise in blood glucose and preserves insulin secretion in type-2 diabetes mellitus rats. J. Nutr. Sci. Vitaminol. 58, 415422 (2012).
26.Kaushik, A.C., Kumar, A., Dwivedi, V.D., Bharadwaj, S., Kumar, S., Bharti, K., Kumar, P., Chaudhary, R.K., Mishra, S.K.: Deciphering the biochemical pathway and pharmacokinetic study of amyloid β-42 with superparamagnetic iron oxide nanoparticles (SPIONs) using systems biology approach. Mol. Neurobiol. 55, 32243236 (2018).
27.Shaikh, T., Pandey, A., Talpur, F.N., Kaushik, A., Niazi, J.H.: Gold nanoparticles based sensor for in vitro analysis of drug-drug interactions using imipramine and isoniazid drugs: A proof of concept approach. Sens. Actuators, B 252, 10551062 (2017).
28.Kaushik, A.C., Bharadwaj, S., Kumar, S., and Wei, D.Q.: Nano-particle mediated inhibition of Parkinson’s disease using computational biology approach. Sci. Rep. 8, 9169 (2018).
29.Chen, C., Huang, H., and Wu, C.H.: Protein Bioinformatics Databases and Resources (Protein Bioinformatics, Humana Press, New York, NY, 2017).
30.Pundir, S., Martin, M.J., and O’Donovan, C.: UniProt Protein Knowledgebase (Protein Bioinformatics, Humana Press, New York, NY, 2017).
31.Kaushik, A.C. and Sahi, S.: Boolean network model for GPR142 against type 2 diabetes and relative dynamic change ratio analysis using systems and biological circuits approach. Synth. Syst. Biol. 9, 45 (2015).
32.Kaushik, A.C. and Sahi, S.: Deciphering evolutionarily conserved orphan G-protein-coupled receptors from homolog cluster. Int. J. Bioinf. Res. Appl. 13, 264 (2017).
33.Kaushik, A.C. and Sahi, S.: Insights into unbound–bound states of GPR142 receptor in a membrane-aqueous system using molecular dynamics simulations. J. Biomol. Struct. Dyn. 36, 1788 (2018).
34.Harmar, A.J., Hills, R.A., Rosser, E.M., Jones, M., Buneman, O.P., Dunbar, D.R., Greenhill, S.D., Hale, V.A., Sharman, J.L., Bonner, T.I., Catterall, W.A.: IUPHAR-DB: The IUPHAR database of G protein-coupled receptors and ion channels. Nucleic Acids Res. 37, D680 (2008).
35.Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.: Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42, D199 (2013).
36.Muto, A., Kotera, M., Tokimatsu, T., Nakagawa, Z., Goto, S., and Kanehisa, M.: Modular architecture of metabolic pathways revealed by conserved sequences of reactions. J. Chem. Inf. Model. 53, 613 (2013).
37.Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A.C., and Kanehisa, M.: KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182 (2007).
38.Moriya, Y., Shigemizu, D., Hattori, M., Tokimatsu, T., Kotera, M., Goto, S., Kanehisa, M.: PathPred: An enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res. 38, W138W143 (2010).
39.Yamanishi, Y., Hattori, M., Kotera, M., Goto, S., and Kanehisa, M.: Enzyme predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics 25, i179 (2009).
40.Kaushik, A.C., Bharadwaj, S., Kumar, S., and Wei, D.Q.: Nano-particle mediated inhibition of Parkinson's disease using computational biology approach. Sci. Rep. 8, 9169 (2018).
41.Kanehisa, M.: Toward pathway engineering: A new database of genetic and molecular pathways. Sci. Technol. Jpn. 59, 34 (1996).
42.Kanehisa, M.: A database for post-genome analysis. Trends Genet. 13, 375376 (1997).
43.Kanehisa, M.: The KEGG Database, ‘In Silico’ Simulation of Biological Processes: Novartis Foundation Symposium 247 (John Wiley & Sons, Ltd, Chichester, UK, 2002); pp. 91103.
44.Kanehisa, M. and Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 2730 (2000).
45.Kanehisa, M., Goto, S., Kawashima, S., and Nakaya, A.: The KEGG databases at GenomeNet. Nucleic Acids Res. 30, 4246 (2002).
46.Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., and Hattori, M.: The KEGG resource for deciphering the genome. Nucleic Acids Res. 32(Suppl. 1), D277D280 (2004).
47.Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., and Hirakawa, M.: From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res. 34(Suppl. 1), D354D357 (2006).
48.Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., and Tokimatsu, T.: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36(Suppl. 1), D480D484 (2007).
49.Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., and Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38(Suppl. 1), D355D360 (2009).
50.Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M.: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109D114 (2011).
51.Kanehisa, M.: Organizing and Computing Metabolic Pathway Data in Terms of Binary Relations (Pacific Symposium on Biocomputing, Citeseer, 1997).
52.Okuda, S., Yamada, T., Hamajima, M., Itoh, M., Katayama, T., Bork, P., Goto, S., and Kanehisa, M.: KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res. 36(Suppl. 2), W423W426 (2008).
53.Kotera, M., Yamanishi, Y., Moriya, Y., Kanehisa, M., and Goto, S.: GENIES: Gene network inference engine based on supervised analysis. Nucleic Acids Res. 40, W162W167 (2012).
54.Nakaya, A., Katayama, T., Itoh, M., Hiranuka, K., Kawashima, S., Moriya, Y., Okuda, S., Tanaka, M., Tokimatsu, T., Yamanishi, Y., and KEGG, O.C.: A large-scale automatic construction of taxonomy-based ortholog clusters. Nucleic Acids Res. 41, D353D357 (2012).
55.Hattori, M., Okuno, Y., Goto, S., and Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125, 1185311865 (2003).
56.Hattori, M., Tanaka, N., Kanehisa, M., and Goto, S.: SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses. Nucleic Acids Res. 38(Suppl. 2), W652W656 (2010).
57.Oh, M., Yamada, T., Hattori, M., Goto, S., and Kanehisa, M.: Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J. Chem. Inf. Model. 47, 17021712 (2007).
58.Funahashi, A., Morohashi, M., Kitano, H., and Tanimura, N.: CellDesigner: A process diagram editor for gene-regulatory and biochemical networks. Biosilico 1, 159162 (2003).
59.Funahashi, A., Jouraku, A., Matsuoka, Y., and Kitano, H.: Integration of CellDesigner and SABIO-RK. Silico Biol. 7(Suppl. 2), 8190 (2007).
60.Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., and Kitano, H.: CellDesigner 3.5: A versatile modeling tool for biochemical networks. Proc. IEEE 96, 12541265 (2008).
61.Arkin, A., Ross, J., and McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 149, 16331648 (1998).
62.Mendes, P.: GEPASI: A software package for modelling the dynamics, steady states and control of biochemical and other systems. Bioinformatics 9, 563571 (1993).
63.Burch, C.: Logisim: A graphical system for logic circuit design and simulation. J. Educ. Resour. Comput. 2, 516 (2002).


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G-protein-coupled receptors function as logic gates for nanoparticle binding using systems and synthetic biology approach

  • Aman Chandra Kaushik (a1), Xueying Mao (a2), Cheng-Dong Li (a1), Yan Li (a3), Dong-Qing Wei (a1) and Shakti Sahi (a4)...


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