Computational Outlook of Marine Compounds as Anti-Cancer Representatives Targeting BCL-2 and Survivin

Author(s): Eram Shakeel, Rajnish Kumar, Neha Sharma, Salman Akhtar, Mohd. Kalim Ahmad Khan, Mohtashim Lohani, Mohd. Haris Siddiqui*.

Journal Name: Current Computer-Aided Drug Design

Volume 15 , Issue 3 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Introduction: The regulation of apoptosis via compounds originated from marine organisms signifies a new wave in the field of drug discovery. Marine organisms produce potent compounds as they hold the phenomenal diversity in chemical structures. The main focus of drug development is anticancer therapy.

Methods: Expertise on manifold activities of compounds helps in the discovery of their derivatives for preclinical and clinical experiment that promotes improved activity of compounds for cancer patients.

Results: These marine derived compounds stimulate apoptosis in cancer cells by targeting Bcl-2 and Survivin, highlighting the fact that instantaneous targeting of these proteins by novel derivatives results in efficacious and selective killing of cancer cells.

Conclusion: Our study reports the identification of Aplysin and Haterumaimide J as Bcl-2 inhibitors and Cortistatin A as an inhibitor of survivin protein, from a sequential virtual screening approach.

Keywords: Marine, Aplysin, Haterumaimide J, Cortistatin A, Bcl-2, surviving and molecular docking.

[1]
Malve, H. Exploring the ocean for new drug developments: Marine pharmacology. J. Pharm. Bioallied Sci., 2016, 8(2), 83.
[2]
Alice, O.D.; Elegbede, I.O. Impact and challenges of marine medicine to man and its environment. Poult. Fish Wildl. Sci, 2016, 4, 160.
[3]
Ruiz-Torres, V.; Encinar, J.A.; Herranz-López, M.; Pérez-Sánchez, A.; Galiano, V.; Barrajón-Catalán, E.; Micol, V. An updated review on marine anticancer compounds: the use of virtual screening for the discovery of small-molecule cancer drugs. Molecules, 2017, 22(7), 1037.
[4]
Khan, Z.; Khan, A.A.; Yadav, H.; Prasad, G.B.; Bisen, P.S. Survivin, a molecular target for therapeutic interventions in squamous cell carcinoma. Cell. Mol. Biol. Lett., 2017, 22(1), 8.
[5]
Bae, I.S.; Kim, C.H.; Kim, J.M.; Cheong, J.H.; Ryu, J.I.; Han, M.H. Correlation of survivin and B-cell lymphoma 2 expression with pathological malignancy and anti-apoptotic properties of glial cell tumors. Biomed. Rep., 1899, 6(4), 396-400.
[6]
Rocha, J.; Peixe, L.; Gomes, N.; Calado, R. Cnidarians as a source of new marine bioactive compounds-An overview of the last decade and future steps for bioprospecting. Mar. Drugs, 2011, 9(10), 1860-1886.
[7]
Rasul, A.; Khan, M.; Ali, M.; Li, J.; Li, X. Targeting apoptosis pathways in cancer with alantolactone and isoalantolactone. Sci. World J., 2013, 2013, 248532.
[8]
Sithranga, B.N.; Kathiresan, K. Anticancer drugs from marine flora: An overview. J. Oncol., 2010, 2010, 214186.
[9]
Liu, J.; Ma, L.; Wu, N.; Liu, G.; Zheng, L.; Lin, X. Aplysin sensitizes cancer cells to TRAIL by suppressing P38 MAPK/survivin pathway. Mar. Drugs, 2014, 12(9), 5072-5088.
[10]
Uddin, J.; Ueda, K.; Siwu, E.R.; Kita, M.; Uemura, D. Cytotoxic labdane alkaloids from an ascidian Lissoclinum sp.: Isolation, structure elucidation, and structure-activity relationship. Bioorg. Med. Chem., 2006, 14(20), 6954-6961.
[11]
Aoki, S.; Watanabe, Y.; Sanagawa, M.; Setiawan, A.; Kotoku, N.; Kobayashi, M.; Cortistatins, A. B, C, and D, anti-angiogenic steroidal alkaloids, from the marine sponge Corticium simplex. J. Am. Chem. Soc., 2006, 128(10), 3148-3149.
[12]
Bajwa, N.; Liao, C.; Nikolovska-Coleska, Z. Inhibitors of the anti-apoptotic Bcl-2 proteins: A patent review. Expert Opin. Ther. Pat., 2012, 22(1), 37-55.
[13]
Pyrko, P.; Soriano, N.; Kardosh, A.; Liu, Y.T.; Uddin, J.; Petasis, N.A.; Schönthal, A.H. Downregulation of survivin expression and concomitant induction of apoptosis by celecoxib and its non-cyclooxygenase-2-inhibitory analog, dimethyl-celecoxib (DMC), in tumor cells in vitro and in vivo. Mol. Cancer, 2006, 5(1), 19.
[14]
Gong, A.; Ge, N.; Yao, W.; Lu, L.; Liang, H. Aplysin enhances temozolomide sensitivity in glioma cells by increasing miR-181 level. Cancer Chemother. Pharmacol., 2014, 74(3), 531-538.
[15]
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., 1997, 23(1-3), 3-25.
[16]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19(14), 1639-1662.
[17]
Morris, G.M.; Goodsell, D.S.; Huey, R.; Olson, A.J. Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. J. Comput. Aided Mol. Des., 1996, 10(4), 293-304.
[18]
Goodsell, D.S.; Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins, 1990, 8(3), 195-202.
[19]
The brookhaven protein data bank website. http://www.rcsb.org (Accessed January 24 , 2016).
[20]
Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Bryant, S. H. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 2016. 37(suppl_2), W623-W633.
[21]
Hess, B.; Kutzner, C.; Van Der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput., 2008, 4(3), 435-447.
[22]
SchuÈttelkopf, A.W.; Van Aalten, D.M. PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D, 2004, 60(8), 1355-1363.
[23]
van Gunsteren, W.F.; Billeter, S.R.; Eising, A.A.; Hünenberger, P.H.; Krüger, P.K.H.C.; Mark, A.E.; Tironi, I.G. Biomolecular simulation: The GROMOS96 manual and user guide, 1996.
[24]
Berendsen, H.J.; Postma, J.P.; van Gunsteren, W.F.; Hermans, J. Interaction models forwater in relation to protein hydration. Intermol. Forces, 1981, 14, 331-342.
[25]
Hess, B.; Bekker, H.; Berendsen, H.J.; Fraaije, J.G. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 1997, 18(12), 1463-1472.
[26]
Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J. Chem. Phys., 1993, 98(12), 10089-10092.
[27]
Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys., 2007, 126(1), 014101.
[28]
Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 1981, 52(12), 7182-7190.
[29]
Singh, S.; Gupta, A.K.; Verma, A. Molecular Properties and Bioactivity score of the Aloe vera antioxidant compounds-in order to lead finding. Res. J. Pharm. Biol. Chem. Sci., 2013, 4(2), 876-881.
[30]
Bonate, P.L.; Howard, D.R., Eds.; Pharmacokinetics in Drug Development: Regulatory and development paradigms; (Vol. 2). Springer Science & Business Media, 2005.
[31]
Simmons, T.L.; Andrianasolo, E.; McPhail, K.; Flatt, P.; Gerwick, W.H. Marine natural products as anticancer drugs. Mol. Cancer Ther., 2005, 4(2), 333-342.
[32]
Newman, D.J.; Cragg, G.M. Marine natural products and related compounds in clinical and advanced preclinical trials. J. Nat. Prod., 2004, 67(8), 1216-1238.
[33]
Sakoguchi-Okada, N.; Takahashi-Yanaga, F.; Fukada, K.; Shiraishi, F.; Taba, Y.; Miwa, Y.; Sasaguri, T. Celecoxib inhibits the expression of survivin via the suppression of promoter activity in human colon cancer cells. Biochem. Pharmacol., 2007, 73(9), 1318-1329.
[34]
Konc, J.; Lešnik, S.; Janežič, D. Modeling enzyme-ligand binding in drug discovery. J. Cheminform., 2015, 7, 48.
[35]
Konc, J.; Janežič, D. Binding site comparison for function prediction and pharmaceutical discovery. Curr. Opin. Struct. Biol., 2014, 25, 34-39.
[36]
Konc, J.; Miller, B.T.; Stular, T.; Lesnik, S.; Woodcock, H.L.; Brooks, B.R.; Janezic, D. ProBiS-CHARMMing: Web interface for prediction and optimization of ligands in protein binding sites. J. Chem. Inf. Model., 2015, 55, 2308-2314.
[37]
Kumar, R.; Sharma, A.; Tiwari, R.K. Can we predict blood brain barrier permeability of ligands using computational approaches? Interdiscip. Sci., 2013, 5(2), 95-101.
[38]
Ogrizek, M.; Turk, S.; Lešnik, S.; Sosič, I.; Hodošček, M.; Mirković, B.; Kos, J.; Janežič, D.; Gobec, S.; Konc, J. Molecular dynamics to enhance structure-based virtual screening on cathepsin B. J. Comput. Aided Mol. Des., 2015, 29(8), 707-712.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 3
Year: 2019
Page: [265 - 276]
Pages: 12
DOI: 10.2174/1573409915666190130173138
Price: $58

Article Metrics

PDF: 34
HTML: 3
EPUB: 1