Abstract
Keratinocyte growth factor (KGF) protein is a member of the fibroblast growth factor (FGF) family, which is also known as FGF-7. The FGF-7 plays an important role in tumor angiogenesis. In the present work, FGF-7 is treated as a potential therapeutic target to prevent angiogenesis in cancerous tissue. Computational techniques are applied to evaluate and validate the 3D structure of FGF-7 protein. The active site region of the FGF-7 protein is identified based on hydrophobicity calculations using CASTp and Q-site Finder active site prediction tools. The protein–protein docking study of FGF-7 with its natural receptor FGFR2b is carried out to confirm the active site region in FGF-7. The amino acid residues Asp34, Arg67, Glu116, and Thr194 in FGF-7 interact with the receptor protein (FGFR2b). A grid is generated at the active site region of FGF-7 using Glide module of Schrödinger suite. Subsequently, a virtual screening study is carried out at the active site using small molecular structural databases to identify the ligand molecules. The binding interactions of the ligand molecules, with piperazine moiety as a pharmacophore, are observed at Arg67 and Glu149 residues of the FGF-7 protein. The identified ligand molecules against the FGF-7 protein show permissible pharmacokinetic properties (ADME). The ligand molecules with good docking scores and satisfactory pharmacokinetic properties are prioritized and identified as novel ligands for the FGF-7 protein in cancer therapy.
Similar content being viewed by others
References
Carmeliet P (2005) Angiogenesis in life, disease and medicine. Nature 438:932–936
Finch PW, Rubin JS (2006) Keratinocyte growth factor expression and activity in cancer: implications for use in patients with solid tumors. J Natl Cancer Inst 98:812–824
Bergers G, Benjamin LE (2003) Tumorigenesis and the angiogenic switch. Nat Rev Cancer 3:401–410
Ferrara N, Gerber H-P, LeCouter J (2003) The biology of VEGF and its receptors. Nat Med 9:669–676
Coultas L, Chawengsaksophak K, Rossant J (2005) Endothelial cells and VEGF in vascular development. Nature 438:937–945
Hoeben A, Landuyt B, Highley MS, Wildiers H, Van Oosterom AT, De Bruijn EA (2004) Vascular endothelial growth factor and angiogenesis. Pharmacol Rev 56:549–580
Laestander C, Engström W (2014) Role of fibroblast growth factors in elicitation of cell responses. Cell Prolif 47:3–11
Hanahan D, Folkman J (1996) Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell 86:353–364
Tsuboi R, Sato C, Kurita Y, Ron D, Rubin JS, Ogawa H (1993) Keratinocyte growth factor (FGF-7) stimulates migration and plasminogen activator activity of normal human keratinocytes. J Investig Dermatol 101:49–53
Gillis P, Savla U, Volpert OV, Jimenez B, Waters CM, Panos RJ, Bouck NP (1999) Keratinocyte growth factor induces angiogenesis and protects endothelial barrier function. J Cell Sci 112:2049–2057
Beer HD, Gassmann MG, Munz B, Steiling H, Engelhardt F, Bleuel K, Werner S (2000) Expression and function of keratinocyte growth factor and activin in skin morphogenesis and cutaneous wound repair. J Investig Dermatol Symp Proc 5:34–39
Yamayoshi T, Nagayasu T, Matsumoto K, Abo T, Hishikawa Y, Koji T (2004) Expression of keratinocyte growth factor/fibroblast growth factor-7 and its receptor in human lung cancer: correlation with tumour proliferative activity and patient prognosis. J Pathol 204:110–118
Rubin JS, Osada H, Finch PW, Taylor WG, Rudikoff S, Aaronson SA (1989) Purification and characterization of a newly identified growth factor specific for epithelial cells. Proc Natl Acad Sci USA 86:802–806
Birrer MJ, Johnson ME, Hao K, Wong KK, Park DC, Bell A, Welch WR, Berkowitz RS, Mok SC (2007) Whole genome oligonucleotide-based array comparative genomic hybridization analysis identified fibroblast growth factor 1 as a prognostic marker for advanced-stage serous ovarian adenocarcinomas. J Clin Oncol 25:2281–2287
Ricol D, Cappellen D, El Marjou A, Gil-Diez-de-Medina S, Girault JM, Yoshida T, Ferry G, Tucker G, Poupon MF, Chopin D, Thiery JP, Radvanyi F (1999) Tumour suppressive properties of fibroblast growth factor receptor 2-IIIb in human bladder cancer. Oncogene 18:7234–7243
Zhang Y, Wang H, Toratani S, Sato JD, Kan M, McKeehan WL, Okamoto T (2001) Growth inhibition by keratinocyte growth factor receptor of human salivary adenocarcinoma cells through induction of differentiation and apoptosis. Proc Natl Acad Sci U S A 98:11336–11340
Maretzky T, Evers A, Zhou W, Swendeman SL, Wong PM, Rafii S, Reiss K, Blobel CP (2011) Migration of growth factor-stimulated epithelial and endothelial cells depends on EGFR trans activation by ADAM17. Nat Commun 2:229
Bansal GS, Cox HC, Marsh S, Gomm JJ, Yiangou C, Luqmani Y, Coombes RC, Johnston CL (1997) Expression of keratinocyte growth factor and its receptor in human breast cancer. Br J Cancer 75:1567–1574
Ornitz DM, Marie PJ (2002) FGF signaling pathways in endochondral and intramembranous bone development and human genetic disease. Genes Dev 16:1446–1465
Turner N, Grose R (2010) Fibroblast growth factor signalling: from development to cancer. Nat Rev Cancer 10:116–129
Cross MJ, Claesson-Welsh L (2001) FGF and VEGF function in angiogenesis: signalling pathways, biological responses and therapeutic inhibition. Trends Pharmacol Sci 22:201–207
Ahmad I, Iwata T, Leung HY (2012) Mechanisms of FGFR-mediated carcinogenesis. Biochim Biophys Acta 1823:850–860
Finch PW, Yee LK, Chu MY, Chen TM, Lipsky MH, Maciag T, Friedman S, Epstein MH, Calabresi P (1997) Inhibition of growth factor mitogenicity and growth of tumor cell xenografts by a sulfonated distamycin A derivative. Pharmacology 55:269–278
Takahama Y, Ochiya T, Tanooka H, Yamamoto H, Sakamoto H, Nakano H, Terada M (1999) Adenovirus-mediated transfer of HST-1/FGF-4 gene protects mice from lethal irradiation. Oncogene 18:5943–5947
Min D, Taylor PA, Panoskaltsis-Mortari A, Chung B, Danilenko DM, Farrell C, Lacey DL, Blazar BR, Weinberg KI (2002) Protection from thymic epithelial cell injury by keratinocyte growth factor: a new approach to improve thymic and peripheral T-cell reconstitution after bone marrow transplantation. Blood 99:4592–4600
Yamamoto H, Ochiya T, Tamamushi S, Toriyama-Baba H, Takahama Y, Hirai K, Sasaki H, Sakamoto H, Saito I, Iwamoto T, Kakizoe T, Terada M (2002) HST-1/FGF-4 gene activation induces spermatogenesis and prevents adriamycin-induced testicular toxicity. Oncogene 21:899–908
Harjes U, Bensaad K, Harris AL (2012) Endothelial cell metabolism and implications for cancer therapy. Br J Cancer 107:1207–1212
Beenken A, Mohammadi M (2009) The FGF family: biology, pathophysiology and therapy. Nat Rev Drug Discov 8:235–253
Presta M, Dell’Era P, Mitola S, Moroni E, Ronca R, Rusnati M (2005) Fibroblast growth factor/fibroblast growth factor receptor system in angiogenesis. Cytokine Growth Factor Rev 16:159–178
Albini A, Tosetti F, Li VW, Noonan DM, Li WW (2012) Cancer prevention by targeting angiogenesis. Nat Rev Clin Oncol 9:498–509
Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797
Dorn M, E Silva MB, Buriol LS, Lamb LC (2014) Three-dimensional protein structure prediction: methods and computational strategies. Comput Biol Chem 53PB:251–276
Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A (2003) ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res 31:3784–3788
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402
Cole C, Barber JD, Barton GJ (2008) The Jpred 3 secondary structure prediction server. Nucleic Acids Res 36:W197–W201
Kelley LA, Sternberg MJ (2009) Protein structure prediction on the web: a case study using the Phyre server. Nat Protoc 4:363–371
Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680
Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M (1995) Evaluation of comparative protein modeling by MODELLER. Proteins 23:318–326
Martí-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325
Fiser A, Do RK, Sali A (2000) Modeling of loops in protein structures. Protein Sci 9:1753–1773
Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723
Laskowsky RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereo chemical quality of protein structures. J Appl Crystallogr 26:283–291
Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364
Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99
Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410
Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118
Laurie AT, Jackson RM (2005) Q-Site Finder: an energy-based method for the prediction of protein–ligand binding sites. Bioinformatics 21:1908–1916
Chen R, Li L, Weng Z (2003) ZDOCK: an initial-stage protein docking algorithm. Proteins 52:80–87
Pierce B, Weng Z (2007) ZRANK: reranking protein docking predictions with an optimized energy function. Proteins 67:1078–1108
Park MS, Gao C, Stern HA (2011) Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins 79:304–314
Kawatkar S, Wang H, Czerminski R, Joseph-McCarthy D (2009) Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using Glide. J Comput Aided Mol Des 23:527–539
Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949
Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749
Chen IJ, Foloppe N (2010) Drug-like bioactive structures and conformational coverage with the ligprep/confgen suite: comparison to programs MOE and catalyst. J Chem Inf Model 50:822–839
Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196
Vasavi M, Kiran KM, Sarita RP, Uma V (2012) Modeling of alternate RNA polymerase sigma D factor and identification of novel inhibitors by virtual screening. Cell Mol Bioeng 5:363–374
Durrant JD, Friedman AJ, Rogers KE, McCammon JA (2013) Comparing neural-network scoring functions and the state of the art: applications to common library screening. J Chem Inf Model 53:1726–1735
Ioakimidis L, Thoukydidis L, Mirza A, Naeem S, Reynisson J (2008) Benchmarking the reliability of QikProp. Correlation between experimental and predicted values. QSAR Comb Sci 27:445–456
Malkhed V, Mustyala KK, Potlapally SR, Vuruputuri U (2014) Identification of novel leads applying in silico studies for mycobacterium multidrug resistant (MMR) protein. J Biomol Struct Dyn 32:1889–1906
Ramatenki V, Potlapally SR, Dumpati RK, Vadija R, Vuruputuri U (2015) Homology modeling and virtual screening of ubiquitin conjugation enzyme E2A for designing a novel selective antagonist against cancer. J Recept Signal Transduct Res 35:536–549
Singh T, Biswas D, Jayaram B (2011) AADS—an automated active site identification, docking and scoring protocol for protein targets based on physicochemical descriptors. J Chem Inf Model 51:2515–2527
Girke T, Cheng LC, Raikhel N (2005) ChemMine. A compound mining database for chemical genomics. Plant Physiol 138:573–577
Boobis A, Gundert-Remy U, Kremers P, Macheras P, Pelkonen O (2002) In silico prediction of ADME and pharmacokinetics report of an expert meeting organised by COST B15. Eur J Pharm Sci 17:183–193
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26
Congreve M, Carr R, Murray C, Jhoti H (2003) A ’rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877
Acknowledgments
R.V. acknowledges the Council of Scientific and Industrial Research (CSIR)-INDIA for financial support. The authors R.K.D. and S.P.V. acknowledge the UGC-INDIA for financial support. The author V.R. acknowledges the Council of Scientific and Industrial Research (CSIR)-INDIA for financial support. The authors R.V., K.K.M., N.N., R.D., R.K.D., V.R., and S.P.V. acknowledge the Principal and the Head Department of Chemistry, University College of Science, Osmania University, Hyderabad, for providing facilities to carry out the work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Electronic supplementary material
Supplementary Table 1
(DOCX 16 kb)
Supplementary Table 2
(DOCX 17 kb)
Supplementary Table 3
(DOCX 16 kb)
Supplementary Table 4
(DOCX 245 kb)
Supplementary Fig 1
(GIF 14 kb)
Supplementary Fig 2
(GIF 15 kb)
Supplementary Fig 3a
(GIF 18 kb)
Supplementary Fig 3b
(GIF 16 kb)
Supplementary Fig 4
(GIF 11 kb)
Supplementary Fig 5
(GIF 11 kb)
Rights and permissions
About this article
Cite this article
Vadija, R., Mustyala, K.K., Nambigari, N. et al. Homology modeling and virtual screening studies of FGF-7 protein—a structure-based approach to design new molecules against tumor angiogenesis. J Chem Biol 9, 69–78 (2016). https://doi.org/10.1007/s12154-016-0152-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12154-016-0152-x