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in silico Studies on Components of Murraya koenigii as Activators of Insulin Receptor
Corresponding Author(s) : K. Lahari
Asian Journal of Chemistry,
Vol. 30 No. 12 (2018): Vol 30 Issue 12
Abstract
In this study, components of Murraya koenigii are subjected to molecular docking studies targeting insulin receptor in order to develop a new lead for diabetes. Initially among all the ligands best one is identified by docking analysis using CCDC GOLD software and further analogs are prepared for it. In the present study we have used commercial computational tools like Accelrys Discovery Studio 2.5 to identify the novel analogs from the docking studies and their ADME (absorption, distribution, metabolism and excretion) and toxicity profiles were studied using in silico tools. We found that among all the ligands girnimbine is having a high dock score and higher interaction energy, so analogs were prepared for it. Computational analysis of the results showed that the substitution of carboxy ethyl and hydroxyl groups at R1 and R2 positions on the side chains of girnimbine showed higher dock score and interaction energy than girnimbine and their corresponding molecular properties, ADME and toxicity profiles were also studied using in silico tools.
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- The Wealth of India, Council of Scientific and Industrial Research, New Delhi, pp. 446-448 (1998).
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- O.F. Guner, Pharmacophore Perception-Development and Use in Drug Design, International University Line: La Jolla, California, USA (2000).
- W.L. Jorgensen, Science, 303, 1813 (2004); https://doi.org/10.1126/science.1096361.
References
The Wealth of India, Council of Scientific and Industrial Research, New Delhi, pp. 446-448 (1998).
S. Joseph and K.V. Peter, Econ. Bot., 39, 68 (1985); https://doi.org/10.1007/BF02861176.
N.D. Prajapati, S.S. Purohit, A.K. Sharma and T. Kumar, A Handbook of Medicinal Plants, Agrobios: Jodhpur, pp. 352-353 (2003).
A.F. Amos, D.J. McCarty and P. Zimmet, Diabet. Med., 14(Suppl. 5), S7 (1997); https://doi.org/10.1002/(SICI)1096-9136(199712)14:5+3.3.CO;2-I.
R.J. Marles and N.R. Farnsworth, Phytomedicine, 2, 137 (1995); https://doi.org/10.1016/S0944-7113(11)80059-0.
G.L. Gupta and S.S. Nigam, Planta Med., 18, 83 (1970); https://doi.org/10.1055/s-0028-1099809.
S.S. Nigam and R.M. Purohit, Perfum. Ess. Oil Rec., 11, 152 (1961).
J.J. Uddin Chowdhury, M.N.I. Bhuiyan and M. Yusuf, Bangladesh J. Pharmacol., 3, 59 (2008).
N. Haslizawati, A.B.M. Aspollah Sukari, M. Rahmani, A.M. Sharif, K. Khalid and U.K. Yusuf, Malays. J. Anal. Sci., 11, 173 (2007).
S. Yadav, V. Vats, Y. Dhunnoo and J.K. Grover, J. Ethnopharmacol., 82, 111 (2002); https://doi.org/10.1016/S0378-8741(02)00167-8.
M.K. Vinuthan, V. Girish and J.P. Kumar, Indian J. Physiol. Pharmacol., 48, 348 (2004).
A.N. Kesari, R.K. Gupta and G. Watal, J. Ethnopharmacol., 97, 247 (2005); https://doi.org/10.1016/j.jep.2004.11.006.
D. Iyer and D.P. Uma, Pharmacogn. Rev., 2, 180 (2008).
H. Jhoti and A.R. Leach, Structure-Based Drug Discovery, Springer: Berlin (2007).
J.H. Van Drie, J. Comput. Aided Mol. Des., 11, 39 (1997); https://doi.org/10.1023/A:1008019326401.
D.K. Agrafioti, eds.: P.V.R. Schleyer, N.L. Allinger, T. Clark, J. Gasteiger, P.A. Kollman, H.F. Schaefer III and P.R. Schreiner, Diversity of Chemical Libraries, In: The Encyclopedia of Computational Chemistry, John Wiley & Sons: Chichester, vol. 1, pp. 742-761 (1998).
G. Schneider and U. Fechner, Nat. Rev. Drug Discov., 4, 649 (2005); https://doi.org/10.1038/nrd1799.
C.N. Cohen, Guidebook on Molecular Modeling in Drug Design, Academic Press: Boston (1996).
O.F. Guner, Pharmacophore Perception-Development and Use in Drug Design, International University Line: La Jolla, California, USA (2000).
W.L. Jorgensen, Science, 303, 1813 (2004); https://doi.org/10.1126/science.1096361.