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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Research Article

Molecular Docking Studies Reveal Rhein from rhubarb (Rheum rhabarbarum) as a Putative Inhibitor of ATP-binding Cassette Super-family G member 2

Author(s): Muhammad Saad Khan, Bareera Mehmood, Qudsia Yousafi, Shabana Bibi, Sahar Fazal, Shahzad Saleem, Muhammad Wasim Sajid, Awais Ihsan, Muhammad Azhar* and Mohammad Amjad Kamal*

Volume 17, Issue 3, 2021

Published on: 19 December, 2019

Page: [273 - 288] Pages: 16

DOI: 10.2174/1573406416666191219143232

Price: $65

Abstract

Background: ATP-binding cassette Super-family G member 2 protein is an active ATPbinding cassette transporter with the potential to combat cancer stem cells.

Objective: Due to the lack of potential ATP-binding cassette Super-family G member 2 inhibitors, we screened natural inhibitors, which could be a safe source to control multidrug resistance by blocking the regulation of ATP-binding cassette Super-family G member 2 protein.

Methods: Three-dimensional structure of ATP-binding cassette Super-family G member 2 protein downloaded from the protein databank and chemical structures of 166 selected compounds of the training dataset were retrieved from PubChem. Drug-likeness and docking analysis was conducted to shortlist the dataset for pharmacophore generation. LigandScout 4.1.5 used for pharmacophorebased screening of Zbc library of ZINC database and Autodock Vina were utilized for molecular docking against the predicted active pocket of the target protein to evaluate the potential association of protein and ligands. The physiochemical properties of novel compounds were calculated by admetSAR respectively.

Results: Through pharmacophore-based screening, ZINC4098704 (Rhein) was identified as a lead compound which demonstrates the least binding energy (-8.5) and the highest binding affinity with the target protein and showed optimal physiochemical profile. This compound is highly recommended for a laboratory test to confirm its activity as an ATP-binding cassette Super-family G member 2 inhibitors.

Conclusion: Our computer-based study systematically selected natural lead compounds, which could be effective in inhibiting ATP-binding cassette Super-family G member 2 and may help reverse the effect of multidrug resistance to increase the effectiveness of chemotherapy in cancer treatment.

Keywords: ATP-binding cassette Super-family G member 2 inhibitor, drug design, Neoplasms, Pharmacophore, Zbc-lead library, cancer.

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[1]
International Agency for Research on Cancer. Latest Global Cancer Data: Cancer Burden Rises to 18.1 Million New Cases and 9.6 Million Cancer Deaths in 2018; Press Release, 2018, pp. 1-3.
[2]
Macconaill, L.E.; Garraway, L.A. Clinical implications of the cancer genome. J. Clin. Oncol., 2010, 28(35), 5219-5228.
[http://dx.doi.org/10.1200/JCO.2009.27.4944] [PMID: 20975063]
[3]
Longley, D.B.; Johnston, P.G. Molecular mechanisms of drug resistance. J. Pathol., 2005, 205(2), 275-292.
[http://dx.doi.org/10.1002/path.1706] [PMID: 15641020]
[4]
Yuan, R.; Hou, Y.; Sun, W.; Yu, J.; Liu, X.; Niu, Y.; Lu, J-J.; Chen, X. Natural products to prevent drug resistance in cancer chemotherapy: a review. Ann. N. Y. Acad. Sci., 2017, 1401(1), 19-27.
[http://dx.doi.org/10.1111/nyas.13387] [PMID: 28891091]
[5]
Goodman, L.S.; Wintrobe, M.M.; Dameshek, W.; Goodman, M.J.; Gilman, A.; McLennan, M.T. Nitrogen mustard therapy; use of methyl-bis (beta-chloroethyl) amine hydrochloride and tris (beta-chloroethyl) amine hydrochloride for HodgKin’s disease, lymphosarcoma, leukemia and certain allied and miscellaneous disorders. J. Am. Med. Assoc., 1946, 132(3), 126-132.
[http://dx.doi.org/10.1001/jama.1946.02870380008004] [PMID: 20997191]
[6]
Zahreddine, H.; Borden, K.L. Mechanisms and insights into drug resistance in cancer. Front. Pharmacol., 2013, 4, 28-36.
[http://dx.doi.org/10.3389/fphar.2013.00028] [PMID: 23504227]
[7]
Mansoori, B.; Mohammadi, A.; Davudian, S.; Shirjang, S.; Baradaran, B. The different mechanisms of cancer drug resistance: A brief review. Adv. Pharm. Bull., 2017, 7(3), 339-348.
[http://dx.doi.org/10.15171/apb.2017.041] [PMID: 29071215]
[8]
Housman, G.; Byler, S.; Heerboth, S.; Lapinska, K.; Longacre, M.; Snyder, N.; Sarkar, S. Drug resistance in cancer: an overview. Cancers (Basel), 2014, 6(3), 1769-1792.
[http://dx.doi.org/10.3390/cancers6031769] [PMID: 25198391]
[9]
Dinic, J.; Podolski-Renic, A.; Stankovic, T.; Bankovic, J.; Pesic, M. New approaches with natural product drugs for overcoming multidrug resistance in cancer. Curr. Pharm. Des., 2015, 21(38), 5589-5604.
[http://dx.doi.org/10.2174/1381612821666151002113546] [PMID: 26429711]
[10]
Winter, E.; Gozzi, G.J.; Chiaradia-Delatorre, L.D.; Daflon-Yunes, N.; Terreux, R.; Gauthier, C.; Mascarello, A.; Leal, P.C.; Cadena, S.M.; Yunes, R.A.; Nunes, R.J.; Creczynski-Pasa, T.B.; Di Pietro, A. Quinoxaline-substituted chalcones as new inhibitors of breast cancer resistance protein ABCG2: polyspecificity at B-ring position. Drug Des. Devel. Ther., 2014, 8, 609-619.
[PMID: 24920885]
[11]
Mo, W.; Zhang, J-T. Human ABCG2: structure, function, and its role in multidrug resistance. Int. J. Biochem. Mol. Biol., 2012, 3(1), 1-27.
[PMID: 22509477]
[12]
Taylor, N.M.I.; Manolaridis, I.; Jackson, S.M.; Kowal, J.; Stahlberg, H.; Locher, K.P. Structure of the human multidrug transporter ABCG2. Nature, 2017, 546(7659), 504-509.
[http://dx.doi.org/10.1038/nature22345] [PMID: 28554189]
[13]
Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25(13), 1605-1612.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[14]
Bairoch, A; Gattiker, A; Wilkins, M.R; Gasteiger, E; Duvaud, E.; Appel, R.D; Hoogland, C. Protein Identification and Analysis Tools on the ExPASy Server. In: In The Proteomics Protocols Handbook; , 2009; pp. 571-607.
[15]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951] [PMID: 26400175]
[16]
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform., 2011, 3(1), 33-47.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID: 21982300]
[17]
Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52(11), 3099-3105.
[http://dx.doi.org/10.1021/ci300367a] [PMID: 23092397]
[18]
Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docKing with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[19]
Milne, G.W.; Nicklaus, M.C.; Wang, S. Pharmacophores in drug design and discovery. SAR QSAR Environ. Res., 1998, 9, 23-38.
[http://dx.doi.org/10.1080/10629369808039147]
[20]
Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2005, 45(1), 160-169.
[http://dx.doi.org/10.1021/ci049885e] [PMID: 15667141]
[21]
Irwin, J.J.; Shoichet, B.K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182.
[http://dx.doi.org/10.1021/ci049714+] [PMID: 15667143]
[22]
Wallace, A.C.; Laskowski, R.A.; Thornton, J.M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng., 1995, 8(2), 127-134.
[http://dx.doi.org/10.1093/protein/8.2.127] [PMID: 7630882]
[23]
Daneman, R.; Prat, A. The blood-brain barrier. Cold Spring Harb. Perspect. Biol., 2015, 7(1), a020412-a020435.
[http://dx.doi.org/10.1101/cshperspect.a020412] [PMID: 25561720]
[24]
Mao, Q.; Unadkat, J.D. Role of the breast cancer resistance protein (BCRP/ABCG2) in drug transport--an update. AAPS J., 2015, 17(1), 65-82.
[http://dx.doi.org/10.1208/s12248-014-9668-6] [PMID: 25236865]
[25]
Protein Physico-Chemical Properties Available at: http://www.intertek.com/pharmaceutical/biopharmaceuticals/protein-physicochemical/
[26]
Mirza, M.U.; Ikram, N. Integrated computational approach for virtual hit identification against ebola viral proteins VP35 and VP40. Int. J. Mol. Sci., 2016, 17(11), 1748-1779.
[http://dx.doi.org/10.3390/ijms17111748] [PMID: 27792169]
[27]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[28]
Desuzinges-Mandon, E.; Arnaud, O.; Martinez, L.; Huché, F.; Di Pietro, A.; Falson, P. ABCG2 transports and transfers heme to albumin through its large extracellular loop. J. Biol. Chem., 2010, 285(43), 33123-33133.
[http://dx.doi.org/10.1074/jbc.M110.139170] [PMID: 20705604]
[29]
Ntie-Kang, F.; Lifongo, L.L.; Mbah, J.A.; Owono, L.C.O.; Megnassan, E.; Mbaze, L.M.; Judson, P.N.; Sippl, W.; Efange, S.M.N. Silico drug metabolism and pharmacoKinetic profiles of natural products from medicinal plants in the congo basin. In Silico Pharmacol., 2013, 1, 1-11.
[30]
Ballabh, P.; Braun, A.; Nedergaard, M. The blood-brain barrier: an overview: structure, regulation, and clinical implications. Neurobiol. Dis., 2004, 16(1), 1-13.
[http://dx.doi.org/10.1016/j.nbd.2003.12.016] [PMID: 15207256]
[31]
Wessel, M.D.; Jurs, P.C.; Tolan, J.W.; Muskal, S.M. Prediction of human intestinal absorption of drug compounds from molecular structure. J. Chem. Inf. Comput. Sci., 1998, 38(4), 726-735.
[http://dx.doi.org/10.1021/ci980029a] [PMID: 9691477]
[32]
Zhou, Y-X.; Xia, W.; Yue, W.; Peng, C.; Rahman, K.; Zhang, H. Rhein: A Review of pharmacological activities. evidence-based complement. Altern. Med, 2015, 2015, 578107-578116.
[http://dx.doi.org/10.1155/2015/578107]
[33]
Lahlou, M. The success of natural products in drug discovery. Pharmacol. Pharm., 2013, 4(3A), 17-31.
[http://dx.doi.org/10.4236/pp.2013.43A003]
[34]
Rizwan, S.; Mehmood, A.; Khalid, I.; Khan, M.S.; Yousafi, Q.; Kalsoom, S.; Rashid, H. Polypharmacology approach against migraine with aura and brain edema for the development of an efficient inhibitor and its analogues. Curr. Comput. Aided. Drug Des., 2018, 14(4), 385-390.
[http://dx.doi.org/10.2174/1573409914666180514092618] [PMID: 29756582]
[35]
Bibi, S.; Sakata, K. Current status of computer-aided drug design for type 2 diabetes. Curr. Comput. Aided. Drug Des, 2016, 12(2), 167-177.
[36]
Bibi, S.; Sakata, K. An integrated computational approach for plant-based protein tyrosine phosphatase non-receptor type 1 inhibitors. Curr. Comput. Aided. Drug Des., 2017, 13(4), 319-335.
[http://dx.doi.org/10.2174/1573409913666170406145607] [PMID: 28382867]
[37]
Chen, Z.; Li, H.L.; Zhang, Q.J.; Bao, X.G.; Yu, K.Q.; Luo, X.M.; Zhu, W.L.; Jiang, H.L. Pharmacophore-based virtual screening versus docKing-based virtual screening: a benchmark comparison against eight targets. Acta Pharmacol. Sin., 2009, 30(12), 1694-1708.
[http://dx.doi.org/10.1038/aps.2009.159] [PMID: 19935678]
[38]
Fuller, J.C.; Burgoyne, N.J.; Jackson, R.M. Predicting druggable binding sites at the protein-protein interface. Drug Discov. Today, 2009, 14(3-4), 155-161.
[http://dx.doi.org/10.1016/j.drudis.2008.10.009] [PMID: 19041415]
[39]
Hoelder, S.; Clarke, P.A.; Workman, P. Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol. Oncol., 2012, 6(2), 155-176.
[http://dx.doi.org/10.1016/j.molonc.2012.02.004] [PMID: 22440008]
[40]
Fogel, D.B. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp. Clin. Trials Commun., 2018, 11, 156-164.
[http://dx.doi.org/10.1016/j.conctc.2018.08.001] [PMID: 30112460]
[41]
Tuccinardi, T.; Poli, G.; Corchia, I.; Granchi, C.; Lapillo, M.; Macchia, M.; Minutolo, F.; Ortore, G.; Martinelli, A. A virtual screening study for lactate dehydrogenase 5 inhibitors by using a pharmacophore-based approach. Mol. Inform., 2016, 35(8-9), 434-439.
[http://dx.doi.org/10.1002/minf.201501026] [PMID: 27546047]

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