Comparative Modeling and Molecular Docking Study of P53 and AKT1, Genes of Lung Cancer Pathways
International Journal of Clinical Oncology and Cancer Research
Volume 1, Issue 1, December 2016, Pages: 6-14
Received: Oct. 15, 2016;
Accepted: Dec. 3, 2016;
Published: Jan. 9, 2017
Views 3984 Downloads 196
Asif Mir, Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad, Pakistan
Syeda Naqsh e Zahra, Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad, Pakistan
Sobiah Rauf, Department of Bioinformatics and Biotechnology, International Islamic University, Islamabad, Pakistan
The fundamentals of structure-based drug designing rely on protein-ligand interactions, which play a significant role to open a gateway from identification of active residues and development of potential drugs. The endeavor behind this work is to select most susceptible genes p53 and AKT1 which plays a vital role in lung cancer pathogencity. In a queue to deliberate the crucial role of these genes in-silico experimental strategy was adopted. 3-D structure of p53 generated by YASARA showed 50.9% sequence identity with 2PCX-A and Z-score of -0.276 while AKT1 showed 66.3% sequence identity with 3QKL-A and Z-score of 0.036. Mutational analysis revealed that R273L and C275Y mutations of p53 destabilize the DNA binding domain, while E17K mutation of AKT1directly affect the binding of the ligand as this residues lines the pocket. Molecular docking was performed using ligands Staurosporine and Nutlin-3 retrieved form ZINC database. Blind docking experiment revealed that p53 involve non polar (Leu206, Leu188, Pro190), acidic (Glu204, Tyr 205) and basic (Arg202) as most interacting residues. AKT1 interactions with ligand Staurosporine revealed nonpolar (Val164, Phe438, Phe442, Phe 236, Phe 237, Phe 161), polar (Gly159, Gly157, Gly234, Gly 278), basic (Lys163, Lys158, Lys 276, Lys 179), acidic (Asp439, Glu278) as most interacting residues. It is assumed that current study will play a significant contribution to design potential drug inhibitors by utilizing most interactive residue information with Nutlin-3 and Staurosporine ligands to restrain the interaction between p53 pathways and epidermal growth pathways. Structural based receptor-ligand interactions likely to be used against anti-cancer therapy.
Syeda Naqsh e Zahra,
Comparative Modeling and Molecular Docking Study of P53 and AKT1, Genes of Lung Cancer Pathways, International Journal of Clinical Oncology and Cancer Research.
Vol. 1, No. 1,
2016, pp. 6-14.
Kiyohara C, Otsu A, Shirakawa T, Fukuda S, Hopkin MJ (2002) Genetic polymorphisms and lung cancer susceptibility: a review. Lung Cancer 37: 241-256.
Blot WJ, McLaughlin JK (1998) Passive smoking and lung cancer risk: what is the story now. Journal of the National Cancer Institute 90: 1416-1417.
Hackshaw KA, Law RM, Wald JN (1997) The accumulated evidence on lung cancer and environmental tobacco smoke. British Medical Journal 315: 980.
Wang Y, Yang H, Li H, Li L, Wang H, Liu C, Zheng Y (2009) Association between X-ray repair cross complementing group 1 codon 399 and 194 polymorphisms and lung cancer risk: A meta-analysis. Cancer Letters 285: 134-140.
Zhou W, Liu G, Miller PD, Thurston WS, Xu LL, Wain CJ, Lynch JT, Su L, Christiani CD (2003) Polymorphisms in the DNA Repair Genes XRCC1 and ERCC2, Smoking, and Lung Cancer Risk. Cancer Epidemiology, Biomarkers & Prevention 12: 359–365.
Nicholson IR, Gee WMJ, Harper EM (2001) EGFR and cancer prognosis. European Journal of Cancer 37: 9–15.
Shigematsu H, Gazdar AF (2006) Somatic mutations of epidermal growth factor receptor signaling pathway in lung cancers. International Journal of Cancer 118: 257–262.
Prasad KST, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan SD, Sebastian A, Rani S, Ray S, Kishore HJC, Kanth S, Ahmed M, Kashyap KM, Mohmood R, Ramachandra LY, Krishna V, Rahiman AB, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A (2009) Human Protein Reference Database—2009 update. Nucleic Acids Research 37: D767–D772.
Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge LC, Haase J, Janes J, WHuss J, Su IA (2009) BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biology 10: R130.
Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK - a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography 26:283–291.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Research 28:235-242.
Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Science 2: 1511-1519.
Irwin JJ, Shoichet KB (2005) ZINC – A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 45: 177–182.
Mendelsohn LD (2004) ChemDraw 8 Ultra: Windows and Macintosh Versions. Journal of Chemical Information and Computer Sciences 44: 2225–2226.
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera--a visualization system for exploratory research and analysis. Journal of Computational Chemistry 13: 1605-12.