STRUCTURAL AND FUNCTIONAL IMPACT OF G2032R MUTATION IN ROS1 â€“ A THEORETICAL PERSPECTIVE
Objective: Drug resistance is an imperative issue in the treatment of patients with lung cancer. In this work, investigation of the drug resistance mechanism of G2032R mutation in ROS1 is carried out using computational simulation techniques.
Methods: Molecular docking and molecular dynamics (MD) simulation approach have been utilized to uncover the mechanism behind crizotinib resistance in ROS1 at a molecular level. Normal mode analysis was carried out using ElNemo server which examines the movements and conformational changes in the protein structure. ArgusLab, PEARLS, and Autodock were employed for the docking analysis, whereas GROMACS package 4.5.3 was used for MD simulation approach.
Results: The results from our analysis indicates that wild-type ROS1 (Protein Data Bank Code 3ZBF) could be more crucial for the crizotinib binding as it indicates largest binding affinity, minimum number of H-bonds, and higher flexibility than mutant-type ROS1. Moreover, the theoretical basis for the cause of drug insensitivity is the differences in the electrostatic properties of binding site residues between the wild and mutant ROS1 structures. Our analysis theoretically suggests that E-2027 is a key residue responsible for the ROS1 drug selectivity.
Conclusion: Molecular docking and MD simulation results provide an explanation of the resistance caused by G2032R and may give a key clue for the drug design to encounter drug resistance.
2. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science 2002;298(5600):1912-34.
3. Kumar A, Shanthi V, Ramanathan K. Discovery of potential ALK inhibitors by virtual screening approach. 3 Biotech 2016;6:1-12.
4. Christensen JG, Zou HY, Arango ME, Li Q, Lee JH, McDonnell SR, et al. Cytoreductive antitumor activity of PF-2341066, a novel inhibitor of anaplastic lymphoma kinase and c-Met, in experimental models of anaplastic large-cell lymphoma. Mol Cancer Ther 2007;6:3314-22.
5. Roberts PJ. Clinical use of crizotinib for the treatment of non-small cell
lung cancer. Biologics 2013;7:91-101.
6. Cui JJ, Tran-DubÃ© M, Shen H, Nambu M, Kung PP, Pairish M, et al. Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). J Med Chem 2011;54(18):6342-63.
7. Shaw AT, Camidge DR, Engelman JA. Clinical activity of crizotinib inadvanced non-small cell lung cancer (NSCLC) harboring ROS1 gene rearrangement. J Clin Oncol 2012;30:7508.
8. Ou SH, Tan J, Yen Y, Soo RA. ROS1 as a â€˜druggableâ€™ receptor tyrosine kinase: Lessons learned from inhibiting the ALK pathway. Expert Rev Anticancer Ther 2012;12(4):447-56.
9. Kumar A, Shanthi V, Ramanathan K. Computational investigation and experimental validation of crizotinib resistance conferred by C1156Y mutant anaplastic lymphoma kinase. Mol Inform 2015;34(2-3):105-14.
10. Kumar A, Ramanathan K. Analyzing resistance pattern of non-small cell lung cancer to crizotinib using molecular dynamic approaches. Indian J Biochem Biophys 2015;52(1):23-8.
11. Sun H, Li Y, Tian S, Wang J, Hou T. P-loop conformation governed crizotinib resistance in G2032R-mutated ROS1 tyrosine kinase: Clues from free energy landscape. PLoS Comput Biol 2014;10(7):e1003729.
12. Deng Y, Roux B. Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem B 2009;113(8):2234-46.
13. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28(1):235-42.
14. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 1997;18(15):2714-23.
15. Feldman HJ, Snyder KA, Ticoll A, Pintilie G, Hogue CW. A complete small molecule dataset from the protein data bank. FEBS Lett 2006;580(6):1649-53.
16. LÃ³pez G, Valencia A, Tress ML. Firestar-prediction of functionally important residues using structural templates and alignment reliability. Nucleic Acids Res 2007;35:W573-7.
17. Yuan Z, Bailey TL, Teasdale RD. Prediction of protein B-factor profiles. Proteins 2005;58(4):905-12.
18. Parthasarathy S, Murthy MR. Protein thermal stability: Insights from atomic displacement parameters (B values). Protein Eng 2000;13(1):9-13.
19. Hinkle A, Tobacman LS. Folding and function of the troponin tail domain. Effects of cardiomyopathic troponin T mutations. J Biol Chem 2003;278(1):506-13.
20. Suhre K, Sanejouand YH. ElNemo: A normal mode web server for protein movement analysis and the generation of templates for molecular replacement. Nucleic Acids Res 2004;32:W610-4.
21. Langer T, Krovat EM. Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr Opin Drug Discov Devel 2003;6(3):370-6.
22. Wilson RP, Yusuf S. In silico design, docking, synthesis and antimicrobial evaluation of 2,5-disubstituted 1,3,4-oxadiazole derivatives. Int J Pharm Sci Res 2016;7(5):2074-82.
23. Oda A, Okayasu M, Kamiyama Y, Yoshida T, Takahashi O, Matsuzaki H. Evaluation of docking accuracy and investigation of roles of parameters and each term in scoring functions for protein-ligand docking using ArgusLab software. Bull Chem Soc Jpn 2007;80(10):1920-25.
24. Kumar A, Ramanathan K. Exploring the structural and functional impact of the ALK F1174L mutation using bioinformatics approach. J Mol Model 2014;20(7):2324.
25. Han LY, Lin HH, Li ZR, Zheng CJ, Cao ZW, Xie B, et al. PEARLS: Program for energetic analysis of receptor-ligand system. J Chem Inf Model 2006;46(1):445-50.
26. Chaitanya M, Babajan B, Anuradha CM, Naveen M, Rajasekhar C, Madhusudana P, et al. Exploring the molecular basis for selective binding of Mycobacterium tuberculosis Asp kinase toward its natural substrates and feedback inhibitors: A docking and molecular dynamics study. J Mol Model 2010;16(8):1357-67.
27. Hess B, Kutzner C, Spoel D, Lindahl E. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 2008;4(3):435-47.
28. Meagher KL, Carlson HA. Solvation influences flap collapse in HIV-1 protease. Proteins 2005;58(1):119-25.
29. Darden T, Perera L, Li L, Pedersen L. New tricks for modelers from the crystallography toolkit: The particle mesh Ewald algorithm and its use in nucleic acid simulations. Structure 1999;7(3):R55-60.
30. van Gunsteren WF, Berendsen HJ. Algorithms for macromolecular dynamics and constraint dynamics. Mol Phys 1977;34(5):1311-27.
31. Rajasekaran R, George Priya Doss C, Sudandiradoss C, Ramanathan K, Purohit R, Sethumadhavan R. Effect of deleterious nsSNP on the HER2 receptor based on stability and binding affinity with herceptin: A computational approach. C R Biol 2008;331(6):409-17.
32. Chandran D, Pappachen LK, Prathap M, Jinsha MJ, Jilsha G. In silico drug design and molecular docking studies of some novel benzothiazole derivatives as anti-cancer and anti-inflammatory agents. Int J Pharm Pharm Sci 2014;6 Suppl:203-8.
33. Nishtha P, Kant PR, Raj ZN. Comparison of commercially available drugs for type 2 diabetes with natural molecule from tinospora. IJPPS 2014;8(7):173-5.
34. Awad MM, Katayama R, McTigue M, Liu W, Deng YL, Brooun A, et al. Acquired resistance to crizotinib from a mutation in CD74-ROS1. N Engl J Med 2013;368(25):2395-401.
35. Wallace AC, Laskowski RA, Thornton JM. LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995;8(2):127-34.
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