Author(s):
Manish Devgun, Nandini, Lalit Singh, Saurabh Sharma
Email(s):
manishdevgun@gmail.com
DOI:
10.52711/0974-360X.2022.00775
Address:
Manish Devgun1*, Nandini2, Lalit Singh3, Saurabh Sharma4
1Institute of Pharmaceutical Sciences, Kurukshetra University, Kurukshetra, Haryana – 136119.
2Rajshree Medical Research Institute, Bareilly, Uttar Pradesh – 243501.
3Department of Pharmacy, Invertis University, Bareilly, Uttar Pradesh – 243123.
4Vivek College of Technical Education, Bijnor, Uttar Pradesh – 246701.
*Corresponding Author
Published In:
Volume - 15,
Issue - 10,
Year - 2022
ABSTRACT:
PHF14 (PHD finger protein 14) is associated with Plant Homeodomain (PHD) Finger Protein family. This chromatin-binding protein interacts with histones. PHF14 overexpression has gained attention due to compelling evidence of its involvement in cell proliferation of various cell lines. PHF14 plays a critical function in the induction of pulmonary fibrosis, and actively participate in cell mitosis which makes it a probable target in the treatment of lung fibrosis and can also be utilized as a biomarker in evaluation and management of non small cell lung cancer. A model of PHF14 protein was prepared by homology modelling and was verified by Ramachandran plot. This model of PHF14 protein was acknowledged by Protein Model Data Base (PMDB) and has been assigned PMDB ID: PM0084114. The DrugBank database was used to obtain ligands, to dock against PHF14 by applying PatchDock technique. The structure of the selected ligand (DB08438) was then modified by means of ACD/ChemSketch 8.0 to secure 22 new in silico ligands, which were subjected to the docking procedure. The docking results identify ligand 31 to possess a high binding affinity with the target protein. The in silico docking results suggests that ligands 31, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 31, 32, and 33 have a high preference for binding with PHF14 and these compounds should be thoroughly probed so as to develop potential chemical entities for the suppression of malignant transformation and tumorigenicity of non small cell lung cancer.
Cite this article:
Manish Devgun, Nandini, Lalit Singh, Saurabh Sharma. Structure Prediction and In-silico Designing of Drugs against Plant Homeodomain Finger Protein 14 for Suppression of Malignant Transformation and Tumorigenicity of Non Small Cell Lung Cancer. Research Journal of Pharmacy and Technology 2022; 15(10):4621-6. doi: 10.52711/0974-360X.2022.00775
Cite(Electronic):
Manish Devgun, Nandini, Lalit Singh, Saurabh Sharma. Structure Prediction and In-silico Designing of Drugs against Plant Homeodomain Finger Protein 14 for Suppression of Malignant Transformation and Tumorigenicity of Non Small Cell Lung Cancer. Research Journal of Pharmacy and Technology 2022; 15(10):4621-6. doi: 10.52711/0974-360X.2022.00775 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2022-15-10-48
REFERENCES:
1. Malik PS, Raina V. Lung cancer: prevalent trends & emerging concepts. Indian J Med Res. 2015;141(1):5-7. doi: 10.4103/0971-5916.154479.
2. Valatina GM, Livinsa ZM. SBIR based screening for lung cancer. Research J Pharm and Tech. 2019;12(1):62-6. doi: 10.5958/0974-360X.2019.00012.X.
3. Yoder LH. An overview of lung cancer: symptoms, pathophysiology and treatment. Medsurg Nurs. 2006;15(4):231-4.
4. Dwivedi N, Dwivedi B, Mishra S, Shukla Y. Lupeol induced apoptosis in human lung cancer cell line: a flow cytometry study. Research Journal of Pharmacology and Pharmacodynamics. 2014;6(4):197-203.
5. Gayathri K, Vaidhehi V. An automatic identification of lung cancer from different types of medical images. Res J Pharm Tech. 2019;12(5):2109-15. doi: 10.5958/0974-360X.2019.00350.0
6. Treating non-small cell lung cancer, Available at: https://www.cancer.org/cancer/lung-cancer/treating-non-small-cell.html, accessed May 2021.
7. Huang Q, Zhang L, Wang Y, Zhang C, Zhou S, Yang G, et al. Depletion of PHF14, a novel histone-binding protein gene, causes neonatal lethality in mice due to respiratory failure. Acta Biochim Biophys Sin (Shanghai). 2013;45(8):622-33. doi: 10.1093/abbs/gmt055.
8. Akazawa T, Yasui K, Gen Y, Yamada N, Tomie A, Dohi O, et al. Aberrant expression of the PHF14 gene in biliary tract cancer cells. Oncol Lett. 2013;5(6):1849-53. doi: 10.3892/ol.2013.1278.
9. Yang B, Chen S, Wu M, Zhang L, Ruan M, Chen X, et al. PHF14: an innate inhibitor against the progression of renal fibrosis following folic acid-induced kidney injury. Sci Rep. 2017;7:39888. doi: 10.1038/srep39888.
10. Kitagawa M, Takebe A, Ono Y, Imai T, Nakao K, Nishikawa SI, et al. PHF14, a novel regulator of mesenchyme growth via platelet-derived growth factor (PDGF) receptor-α. J Biol Chem. 2012;287(33):27983-96. doi: 10.1074/jbc.M112.350074.
11. Zhang L, Huang Q, Lou J, Zou L, Wang Y, Zhang P, et al. A novel PHD-finger protein14/KIF4A complex overexpressed in lung cancer is involved in cell mitosis regulation and tumorigenesis. Oncotarget. 2017;8(12):19684-98. doi: 10.18632/oncotarget.14962.
12. Ramachandran S, Dokholyan NV. Homology modelling: generating structural models to understand protein function and mechanism. In: Dokholyan N, editor. Computational modelling of biological systems: from molecules to pathways, biological and medical physics, biomedical engineering. New York: Springer Science+Business Media, LLC; 2012. p. 97-116.
13. Xiang Z. Advances in homology protein structure modelling. Curr Protein Pept Sci. 2006;7(3):217-27. doi: 10.2174/138920306777452312.
14. Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modelling a fast tool for drug discovery: current perspective. Indian J Pharm Sci. 2012;74(1):1-17. doi: 10.4103/0250-474X.102537.
15. Devgan M. Homology modelling and molecular docking studies of DNA replication licensing factor minichromosome maintenance protein 5 (MCM5). Asian J Pharm Tech. 2015;5(1):17-22. doi: 10.5958/2231-5713.2015.00004.5.
16. Devgan M. Structure prediction and insilico designing of drugs for the inhibition of Eph A10 tyrosine kinase receptor protein. Acta Bio Sci. 2016;(3):86-94.
17. Devgan M. Structure prediction and insilico designing of drugs against homeobox C8 protein. J Chem Pharm Res. 2016;(8):891-9.
18. Devgan M, Karar PK, Agarwal G, Mohan A, Gangwar P. In silico designing of drugs for the inhibition of AMF-HER2 complex in trastuzumab resistant breast cancer. Ind J Biotec. 2016;15(3):292-8.
19. Devgan M. Structure prediction and in silico designing of drugs against Forkhead-box R2 protein. Research J Pharm and Tech. 2018;11(3):913-920. doi: 10.5958/0974-360X.2018.00170.1.
20. Aswathy, Karavadi B, Nisha H. Homology modeling and docking studies to identify the targets in pancreatic cancer. Research J Pharm and Tech. 2017;10(7):2032-40. doi: 10.5958/0974-360X.2017.00355.9.
21. Mahendran R, Jeyabasker S, Francis A, Manoharan S. Homology modeling and in silico docking analysis of BDNF in the treatment of alzheimer’s disease. Research J Pharm and Tech. 2017;10(9):2899-906. doi: 10.5958/0974-360X.2017.00512.1.
22. Hari S, Akilashree S. In silico homology modeling of presenilin 2-therapeutic target for alzheimer’s disease. Research J Pharm and Tech. 2019;12(7):3405-9. doi: 10.5958/0974-360X.2019.00575.4.
23. The Yang Zhang Lab [Internet]. AnnArbor, Michigan: Yang Zhang’s Research Group; [cited 2021 May 11]. What is FASTA format? [about 2 screens]. Available from: http://zhanglab.ccmb.med.umich.edu/FASTA/
24. National Center for Biotechnology Information [Internet]. Bethesda (MD): National Library of Medicine (US); PHD finger protein 14 [Homo sapiens] [cited 2017 Feb 18]; [About 1 p.]. Available from: https://www.ncbi.nlm.nih.gov/protein/AAI52415.1?report=fasta
25. Wikipedia, The free encyclopedia [Internet]. San Franscisco (CA): Wikipedia Foundation, Inc (US); BLAST [updated 2017 Feb 06; cited 2017 Feb 19]; [About 13 p.]. Available from: https://en.wikipedia.org/wiki/BLAST
26. National Center for Biotechnology Information [Internet]. Bethesda (MD): National Library of Medicine (US); BLASTP [cited 2017 Feb 19]; [About 4 p.]. Available from: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins&PROGRAM=blastp&BLAST_PROGRAMS=blastp&QUERY=AAI52415.1&LINK_LOC=protein&PAGE_TYPE=BlastSearch
27. Kelly LA, Sternberg MJ. Protein structure prediction on the web: a case study using the Phyre server. Nat Protoc. 2009;4(3):363-71. doi: 10.1038/nprot.2009.2.
28. Kelly L, Sternberg M. Phyre2 [Internet]. South Kensington Campus (London): Structural Bioinformatics Group, Imperial College (UK); [cited 2017 Feb 20]. Available from: http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index
29. Research Collaboratory for Structural Bioinformatics. RCSB Protein Data Bank [Internet]. New Jersey, California: Rutgers, UCSanDiego (US); [cited 2017 Mar 06]. Available from: http://www.rcsb.org/pdb/home.do
30. Kuntal BK, Aparoy P, Reddanna P. EasyModeller: a graphical interface to modeller. BMC Res Notes. 2010 Aug 16;3:226. doi: 10.1186/1756-0500-3-226.
31. Kuntal BK. EasyModeller (Version 2.0) [Internet]. Hyderabad: Kuntal Kumar Bhusan, University of Hyderabad (India); [cited 2017 Mar 08]. Available from: http://www.sites.google.com/site/bioinformatikz
32. Duex N, Peitsch M, Schwede T, Diemand A. DeepView/Swiss-PdbViewer [Internet]. Bazel: Structural Bioinformatics Groups, Swiss Institute of Bioinformatics, Biozentrum University of Bazel (Switzerland); c1995-2001 [cited 2017 Mar 08]. Available from: http://Spdbv.vital-it.ch/
33. Andrej Sali Lab [Internet]. San Francisco: Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, and California Institute for Quantitative Biomedical Research, University of California; c1989-2017 [updated 2014 Mar 07; cited 2017 Mar 08]. Modeller Tutorials; [about 7 screens]. Available from: https://salilab.org/modeller/tutorial/basic.html
34. Bakker PD, Lovell S. Rampage [Internet]. Cambridge (UK): Department of Biochemistry, University of Cambridge. [Cited 2017 Mar 11]. Available from: http://mordred.bioc.cam.ac.uk/~rapper/rampage.php
35. Fiser A, Do RK, Sali A. Modelling of loops in protein structures. Protein Sci. 2000;9(9):1753-73. doi: 10.1110/ps.9.9.1753.
36. Fiser A, Sali A. ModLoop: automated modelling of loops in protein structures. Bioinformatics 2003 Dec 12;19(18):2500-1. doi: 10.1093/bioinformatics/btg362.
37. Fiser A. ModLoop [Internet]. San Francisco (CA): University of California (USA); [cited 2017 Mar 13]. Available from: http://modbase.compbio.ucsf.edu/modloop/
38. European Bioinformatics Institute (EMBL-EBI). PDBsum [Internet]. Hinxton, Cambridgeshire: Wellcome Trust Genome Campus (UK); © EMBL-EBI 2017 [cited 2017 Mar 13]. Available from: https://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/pdbsum/GetPage.pl?pdbcode=n/a&template=doc_procheck.html
39. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 2006 Jan 1; 34(Database issue):D668-72. doi: 10.1093/nar/gkj067.
40. Wishart DS. DrugBank [Internet]. Canada: Canadian Institutes of Health Research, Alberta Innovates-Health Solutions, The Metabolomics Innovation Centre; [cited 2017 Mar 19]. Available from: http://www.drugbank.ca
41. Forli S. Charting a path to success in virtual screening. Molecules. 2015;20(10):18732-58. doi: 10.3390/molecules201018732.
42. Sravani M, Duganath N, Gade DR, Reddy CH S. Insilico analysis and docking of imatinib derivatives targeting BCR-ABL oncoprotein for chronic myeloid leukemia. Asian J Research Chem. 2012;5(1):153-8.
43. Chandran M, George S, Santhalingam, Gangwar P, Rajasekar D. Molecular docking of 3,5,7-trihydroxy-2-(4-hydroxy-3-methoxyphenyl)-4h-chromen-4-one derivatives againstII-6 for rheumatoid arthritis. Asian J Research Chem. 2011;4(8):1254-57.
44. Hemalatha K, Chakkaravarthi V, Murthy KG, Kayatri R, Girija K. Molecular properties and docking studies of benzimidazole derivatives as potential peptide deformylase inhibitors. Asian J Research Chem. 2011;7(7):644-8.
45. PerkinElmer. ChemBio3D [Internet]. Waltham, Massachusetts: PerkinElmer Inc. (USA); c1998-2017 [cited 2017 Mar 24]. Available from: http://www.cambridgesoft.com/Ensemble for chemistry/chemBio3D/
46. Schrödinger. PyMol [Internet]. New York: Schrödinger (USA); c2005-17 [cited 2017 Mar 24]. Available from: http://www.pymol.org.
47. Duhovny D, Nussinov R, Wolfson HJ. Efficient unbound docking of rigid molecules. Gusfield, Guigo R, Editors. Proceedings of the 2nd Workshop on Algorithms in Bioinformatics(WABI) Rome, Italy, Lecture Notes in Computer Science 2452, pp. 185-200, Springer Verlag; 2002.
48. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ. PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005 July 1;33(web server issue): W363-7. doi: 10.1093/nar/gki481.