Screening of diverse phytochemicals with Aurora Kinase C protein: An In Silico approach
Abstract
Aurora Kinase C, a vital serine-threonine protein Kinase, is an important member of the Aurora Kinase protein family which plays an important role in mitosis is a part of Chromosomal Passenger Complex (CPC). Aurora Kinase C over expression is found to be linked with several cancer cell lines which demonstrate its oncogenic involvement and activity. Aurora C over expression in certain cancer types makes it an important target to be considered for cancer therapeutics. The present research work focuses on the Aurora Kinase C as an important target for computational studies. The protein model of Aurora Kinase C, as a proten target on docking with 1500 natural compounds (phytochemicals) reveals the binding of the natural ligand 3-beta,23,28-trihydroxy-12-oleanene 23-caffeate belonging to the terpenoid class with highest docking score. This best bound ligand with the protein Aurora Kinase C was chosen for further understanding their protein-ligand interactions at the the molecular level using the molecular dynamic simulation approach. Stability of the protein-ligand complex and its conformation helps in disclosing the potentiality of the best bound ligand to be further chosen as an important small molecule inhibitor that would help playing a lead role in further drug discovery process
Keywords: Aurora Kinase C, Cancer, Phytochemicals, Docking, Molecular Dynamics
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References
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31. Miyamoto S, Kollman PA. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. Journal of computational chemistry. 1992; 13(8):952-62.
2. Fu J, Bian M, Jiang Q, Zhang C. Roles of Aurora kinases in mitosis and tumorigenesis. Molecular Cancer Research. 2007; 5(1):1-0.
3. Kollareddy M, Dzubak P, Zheleva D, Hajduch M. Aurora kinases: structure, functions and their association with cancer. Biomedical Papers of the Medical Faculty of Palacky University in Olomouc. 2008; 152(1).
4. Tang A, Gao K, Chu L, Zhang R, Yang J, Zheng J. Aurora kinases: novel therapy targets in cancers. Oncotarget. 2017; 8(14):23937.
5. Goldenson B, Crispino JD. The aurora kinases in cell cycle and leukemia. Oncogene. 2015; 34(5):537.
6. Quartuccio SM, Schindler K. Functions of Aurora kinase C in meiosis and cancer. Frontiers in cell and developmental biology. 2015; 3:50.
7. Fujii S, Srivastava V, Hegde A, Kondo Y, Shen L, Hoshino K, Gonzalez Y, Wang J, Sasai K, Ma X, Katayama H. Regulation of AURKC expression by CpG island methylation in human cancer cells. Tumor Biology. 2015; 36(10):8147-58.
8. Khan J, Ezan F, Crémet JY, Fautrel A, Gilot D, Lambert M, Benaud C, Troadec MB, Prigent C. Overexpression of active Aurora-C kinase results in cell transformation and tumour formation. PLoS One. 2011; 6(10):e26512.
9. Zekri A, Lesan V, Ghaffari SH, Tabrizi MH, Modarressi MH. Gene amplification and overexpression of Aurora-C in breast and prostate cancer cell lines. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 2012; 20(5-6):241-50.
10. Lin BW, Wang YC, Chang-Liao PY, Lin YJ, Yang ST, Tsou JH, Chang KC, Liu YW, Tseng JT, Lee CT, Lee JC. Overexpression of Aurora-C interferes with the spindle checkpoint by promoting the degradation of Aurora-B. Cell death & disease. 2014; 5(3):e1106.
11. Zekri A, Lesan V, Ghaffari SH, Tabrizi MH, Modarressi MH. Gene amplification and overexpression of Aurora-C in breast and prostate cancer cell lines. Oncol Res. 2012; 20(5-6):241-50
12. Tsou JH, Chang KC, Chang‐Liao PY, Yang ST, Lee CT, Chen YP, Lee YC, Lin BW, Lee JC, Shen MR, Chuang CK. Aberrantly expressed AURKC enhances the transformation and tumourigenicity of epithelial cells. The Journal of pathology. 2011; 225(2):243-54.
13. Hosseini S, Hashemzadeh S, Estiar MA, Ebrahimzadeh R, Fakhree MB, Yousefi B, Sheikholeslami S, Modarresi MH, Sakhinia E. Expression analysis of aurora-C and Survivin, two testis-specific genes, in patients with colorectal cancer. Clin Lab. 2015; 61(5-6):475-80.
14. Dutertre S, Hamard-Péron E, Cremet JY, Thomas Y, Prigent C. The absence of p53 aggravates polyploidy and centrosome number abnormality induced by Aurora-C overexpression. Cell Cycle. 2005; 4(12):1783-7.
15. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ. UniProt: the universal protein knowledgebase. Nucleic acids research. 2004; 32(suppl_1):D115-9.
16. Zhang Y. I-TASSER server for protein 3D structure prediction. BMC bioinformatics. 2008; 9(1):40.
17. Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature protocols. 2010; 5(4):725.
18. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nature methods. 2015; 12(1):7.
19. Yang J, Zhang Y. Protein structure and function prediction using I‐TASSER. Current protocols in bioinformatics. 2015; 52(1):5-8.
20. Zhang Y, Kolinski A, Skolnick J. TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophysical journal. 2003; 85(2):1145-64.
21. Zhang Y, Skolnick J. SPICKER: A clustering approach to identify near‐native protein folds. Journal of computational chemistry. 2004; 25(6):865-71.
22. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophysical journal. 2011; 101(10):2525-34.
23. Zhang J, Liang Y, Zhang Y. Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure. 2011; 19(12):1784-95.
24. Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G. Making optimal use of empirical energy functions: force‐field parameterization in crystal space. Proteins: Structure, Function, and Bioinformatics. 2004; 57(4):678-83.
25. Mangal M, Sagar P, Singh H, Raghava GP, Agarwal SM. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic acids research. 2012; 41(D1):D1124-9.
26. Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G. Making optimal use of empirical energy functions: force‐field parameterization in crystal space. Proteins: Structure, Function, and Bioinformatics. 2004; 57(4):678-83.
27. Krieger E, Vriend G. New ways to boost molecular dynamics simulations. Journal of computational chemistry. 2015; 36(13):996-1007.
28. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Structure, Function, and Bioinformatics. 2006; 65(3):712-25.
29. Patel CN, Georrge JJ, Modi KM, Narechania MB, Patel DP, Gonzalez FJ, Pandya HA. Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease. Journal of Biomolecular Structure and Dynamics. 2017 Dec 28:1-20.
30. Patel CN, Kumar SK, Pandya HA, Modi KM, Patel DP, Gonzalez FJ. Retrieval of promiscuous natural compounds using multiple targets docking strategy: A case study on kinase polypharmacology. In2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2017; 288-291.
31. Miyamoto S, Kollman PA. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. Journal of computational chemistry. 1992; 13(8):952-62.
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Pandya PN, Mankad AU, Rawal RM, Prasanth KS. Screening of diverse phytochemicals with Aurora Kinase C protein: An In Silico approach. JDDT [Internet]. 15Feb.2019 [cited 24Jan.2021];9(1-s):67-4. Available from: http://jddtonline.info/index.php/jddt/article/view/2249
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