In silico design and analysis of a new hyperglycosylated analog of erythropoietin to improve drug efficacy

Authors
Anvarsadat Kianmehr 1
Hamid Shahbaz Mohammadi 2
Mohammad Ali Shokrgozar 3
Eskandar Omidinia 2
1 Biochemistry Department, Genetic and Metabolism Research Group, Pasteur Institute of Iran, Tehran; Department of Medical Biotechnology, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
2 Biochemistry Department, Genetic and Metabolism Research Group, Pasteur Institute of Iran, Tehran, Iran
3 National Cell Bank of Iran, New Technologies Research Group, Pasteur Institute of Iran, Tehran, Iran
Abstract
Background: The enhancement of glycosylation by applying glycoengineering approaches has become widely used to boost properties for protein therapeutics. The objective of this work was to engineer a new hyperglycosylated analog of erythropoietin (EPO) with appropriately targeted N-linked carbohydrates through bioinformatics tools.
Materials and Methods: The EPO protein sequence was retrieved from NCBI protein sequence database. Prediction of N-glycosylation sites for the target protein was done using the prediction server, NetNGlyc. The three-dimensional model of glycoengineered EPO (named as kypoetin) was constructed using the homology modeling program. Ramchandran plot obtained from PROCHECK server was used to check stereochemical property. Meanwhile, 3D model of kypoetin with attached N-carbohydrates was built up using the GlyProt server.
Results: In the new modified analog, three additional N-glycosylation sites at amino-acid positions 30, 34 and 86 were inserted. Ramchandran plot analysis showed 81.6% of the residues in the most favored region, 15.6% in the additional allowed, 1.4% in the generously allowed regions and 1.4% in the disallowed region. 3D structural modeling showed that attached carbohydrates were on the proper spatial position. The whole solvent accessible surface areas of kypoetin (15132.69) were higher than EPO (9938.62).
Conclusions: Totally, various model evaluation methods indicated that the glycoengineered version of EPO had considerably good geometry and acceptable profiles for clinical studies and could be considered as the effective drug.

Keywords

1.
Sola RJ, Griebenow K. Glycosylation of therapeutic proteins: An effective strategy to optimize efficacy. Bio Drugs 2010;24:9-21.  Back to cited text no. 1
    
2.
Vidal D, Garcia-Serna R, Mestres J. Ligand-based approaches to in silico pharmacology. Methods Mol Biol 2011;672:489-502.  Back to cited text no. 2
    
3.
Elliott S, Lorenzini T, Asher S, Aoki K, Brankow D, Buck L, et al. Enhancement of therapeutic protein in vivo activities through glycoengineering. Nat Biotechnol 2003;21:414-21.   Back to cited text no. 3
    
4.
Frank M, Schloissnig S. Bioinformatics and molecular modeling in glycobiology. Cell Mol Life Sci 2010;67:2749-72.  Back to cited text no. 4
    
5.
Ortega SS, Cara LC, Salvador MK. In silico pharmacology for a multidisciplinary drug discovery process. Drug Metab Drug Interact 2012; 27:199-207.  Back to cited text no. 5
    
6.
Carlson HA, Masukawa KM, Rubins K, Bushman FD, Jorgenson WL, Lins RD, et al. Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 2000;43:2100-14.  Back to cited text no. 6
    
7.
Flohr S, Kurz M, Kostenis E, Brkovich A, Fournier A, Klabunde T. Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure - activity relationships and nuclear magnetic resonance studies on urotensin II. J Med Chem 2002;45:1799-805.  Back to cited text no. 7
    
8.
Debnath AK. Generation of predictive pharmacophore models for CCR5 anatagonists: Study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors. J Med Chem 2003;46:4501-15.  Back to cited text no. 8
    
9.
Kurogi Y, Miyata K, Okamura T, Hashimoto K, Tsutsumi K, Nasu M. Discovery of novel mesangial cell proliferation inhibitors using a three-dimensional database searching method. J Med Chem 2001;44:2304-7.  Back to cited text no. 9
    
10.
Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br J Pharmacol 2007;152:9-20.  Back to cited text no. 10
    
11.
Elliott S, Chang D, Delorme E, Eris T, Lorenzini T. Structural requirement for additional N- linked carbohydrate on recombinant human erythropoietin. J Biol Chem 2004;279:16854- 62.  Back to cited text no. 11
    
12.
Elliott S, Pham E, Macdougali IC. Erythropoietins: A common mechanism of action. Exp Heamatol 2008;36:1573-84.  Back to cited text no. 12
    
13.
Bartesaghi S, Marinovich M, Corsini E, Galli CL, Viviani B. Erythropoietin: A novel neuroprotective cytokine. Toxicology 2005;26:923-8.  Back to cited text no. 13
    
14.
Elliott S, Egrie J, Browne J, Lorenzini T, Busse L, Rogers N, et al. Control of rHuEPO biological activity: The role of carbohydrate. Exp Heamatol 2004; 32:1146-55.  Back to cited text no. 14
    
15.
Egrie JC, Dwyer E, Browne JK, Hitz A, Lykos MA. Darbepoetin alfa has a longer circulating half-life and greater in vivo potency than recombinant human erythropoietin. Exp Heamatol 2003;31:290-9.   Back to cited text no. 15
    
16.
Egrie JC, Browne JK. Development and characterization of darbepoetin alfa. Oncology 2002;16:1-11.  Back to cited text no. 16
    
17.
Shakin-Eshleman SH, Spitalnik SL, Kasturi L. The amino acid at the X position of an Asn-X-Ser sequon is an important determinant of N-linked core-glycosylation efficiency. J Biol Chem 1996;271:6363-6.  Back to cited text no. 17
    
18.
Julenius K. NetCGlyc 1.0: Prediction of mammalian C-mannosylation sites. Glycobiology 2007;17:868-76.  Back to cited text no. 18
    
19.
Laskowski R, Macarthur M, Moss D, Thornton J. PROCHECK: A program to check the stereochemical quality of protein structures. G Appl Crys 1993;26:283-91.  Back to cited text no. 19
    
20.
Bohne-Lang A, von der Lieth CW. GlyProt: In silico glycosylation of proteins. Nucleic Acids Res 2005;33:214-9.  Back to cited text no. 20
    
21.
Der Lieth CW, Bohne-Lang A, Lohmann KK, Frank M. Bioinformatics for glycomics: Status, methods, requirements and perspectives. Brief Bioinform 2004;5:164-78.  Back to cited text no. 21
    
22.
Mahal LK. Glycomics: Towards bioinformatic approaches to understanding glycosylation. Anticancer Agents Med Chem 2008;8:37-51.  Back to cited text no. 22
    
23.
Kumar P, Pillay V, Choonara YE, Modi G, Naidoo D, Toit LC. In Silico theoretical molecular modeling for Alzheimer's disease: The nicotine-curcumin paradigm in neuroprotection and neurotherapy. Int J Mol Sci 2011;12:694-724.  Back to cited text no. 23
Advanced Biomedical Research
Volume 2015, july
July 2015
Pages 1-5