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Decreasing the immunogenicity of Erwinia chrysanthemi asparaginase via protein engineering: computational approach

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Abstract

Immunogenicity of therapeutic proteins is one of the main challenges in disease treatment. l-Asparaginase is an important enzyme in cancer treatment which sometimes leads to undesirable side effects such as immunogenic or allergic responses. Here, to decrease Erwinase (Erwinia chrysanthemil-Asparaginase) immunogenicity, which is the main drawback of the enzyme, firstly conformational B cell epitopes of Erwinase were predicted from three-dimensional structure by three different computational methods. A few residues were defined as candidates for reducing immunogenicity of the protein by point mutation. In addition to immunogenicity and hydrophobicity, stability and binding energy of mutants were also analyzed computationally. In order to evaluate the stability of the best mutant, molecular dynamics simulation was performed. Among mutants, H240A and Q239A presented significant reduction in immunogenicity. In contrast, the immunogenicity scores of D235A slightly decreased according to two servers. Binding affinity of substrate to the active site reduced significantly in K265A and E268A. The final results of molecular dynamics simulation indicated that H240A mutation has not changed the stability, flexibility, and the total structure of desired protein. Overall, point mutation can be used for reducing immunogenicity of therapeutic proteins, in this context, in silico approaches can be used to screen suitable mutants.

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References

  1. Krishna M, Nadler SG (2016) Immunogenicity to biotherapeutics—the role of anti-drug immune complexes. Front Immunol 7:21. https://doi.org/10.3389/fimmu.2016.00021

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Nechansky A, Kircheis R (2010) Immunogenicity of therapeutics: a matter of efficacy and safety. Expert Opin Drug Discov 5:1067–1079

    Article  CAS  Google Scholar 

  3. Baker M, Reynolds HM, Lumicisi B, Bryson CJ (2010) Immunogenicity of protein therapeutics: the key causes, consequences and challenges. Self/nonself 1:314–322

    Article  Google Scholar 

  4. Kintzing JR, Interrante MVF, Cochran JR (2016) Emerging strategies for developing next-generation protein therapeutics for cancer treatment. Trends Pharmacol Sci 37(12):993–1008

    Article  CAS  Google Scholar 

  5. Yari M, Ghoshoon BM, Vakili B, Ghasemi Y (2017) Therapeutic enzymes: applications and approaches to pharmacological improvement. Curr Pharm Biotechnol 18(7):531–540

    Article  CAS  Google Scholar 

  6. Marshall SA, Lazar GA, Chirino AJ, Desjarlais JR (2003) Rational design and engineering of therapeutic proteins. Drug Discovery Today 8(5):212–221

    Article  CAS  Google Scholar 

  7. Yao B, Zheng D, Liang S, Zhang C (2013) Conformational B-cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PLoS ONE 8(4):e62249

    Article  CAS  Google Scholar 

  8. Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S (2015) An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J Biomed Inform 53:405–414

    Article  Google Scholar 

  9. Davids T, Schmidt M, Böttcher D, Bornscheuer UT (2013) Strategies for the discovery and engineering of enzymes for biocatalysis. Curr Opin Chem Biol 17(2):215–220

    Article  CAS  Google Scholar 

  10. Zarei M, Nezafat N, Rahbar MR, Negahdaripour M, Sabetian S, Morowvat MH, Ghasemi Y (2018) Decreasing the immunogenicity of arginine deiminase enzyme via structure-based computational analysis. J Biomol Struct Dyn 1–14

  11. Negahdaripour M, Nezafat N, Eslami M, Ghoshoon MB, Shoolian E, Najafipour S, Morowvat MH, Dehshahri A, Erfani N, Ghasemi Y (2018) Structural vaccinology considerations for in silico designing of a multi-epitope vaccine. Infect Genet Evol 58:96–109

    Article  Google Scholar 

  12. Potocnakova L, Bhide M, Pulzova LB (2016) An introduction to B-Cell epitope mapping and in silico epitope prediction. J Immunol Res. https://doi.org/10.1155/2016/6760830

    Article  PubMed  PubMed Central  Google Scholar 

  13. Liang S, Zheng D, Standley DM, Yao B, Zacharias M, Zhang C (2010) EPSVR and EPMeta: prediction of antigenic epitopes using support vector regression and multiple server results. BMC Bioinform 11(1):381. https://doi.org/10.1186/1471-2105-11-381

    Article  Google Scholar 

  14. Haste Andersen P, Nielsen M, Lund O (2006) Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci 15(11):2558–2567

    Article  Google Scholar 

  15. Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8(12)

    Article  CAS  Google Scholar 

  16. Ponomarenko J, Bui H-H, Li W, Fusseder N, Bourne PE, Sette A, Peters B (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform 9(1):514. https://doi.org/10.1186/1471-2105-9-514

    Article  CAS  Google Scholar 

  17. Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31(13):3381–3385

    Article  CAS  Google Scholar 

  18. Colovos C, Yeates T (1993) ERRAT: an empirical atom-based method for validating protein structures. Protein Sci 2:1511–1519

    Article  CAS  Google Scholar 

  19. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170

    Article  CAS  Google Scholar 

  20. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lovell SC, Davis IW, Arendall WB, De Bakker PI, Word JM, Prisant MG, Richardson JS, Richardson DC (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50(3):437–450

    Article  CAS  Google Scholar 

  22. Pandurangan AP, Ochoa-Montano B, Ascher DB, Blundell TL (2017) SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx439

    Article  PubMed  PubMed Central  Google Scholar 

  23. Worth CL, Preissner R, Blundell TL (2011) SDM–a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res 39:W215–W222. https://doi.org/10.1093/nar/gkr363

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55(3):379-IN374. https://doi.org/10.1016/0022-2836(71)90324-X

    Article  Google Scholar 

  25. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157(1):105–132

    Article  CAS  Google Scholar 

  26. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    Article  CAS  Google Scholar 

  27. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38

    Article  CAS  Google Scholar 

  28. Hospital A, Goñi JR, Orozco M, Gelpí JL (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 8:37–47. https://doi.org/10.2147/AABC.S70333

    Article  PubMed  PubMed Central  Google Scholar 

  29. Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5(11):789–796. https://doi.org/10.1038/nchembio.232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hansson T, Oostenbrink C, van Gunsteren W (2002) Molecular dynamics simulations. Curr Opin Struct Biol 12(2):190–196

    Article  CAS  Google Scholar 

  31. Abraham M, Van Der Spoel D, Lindahl E, Hess B (2014) The GROMACS development team. GROMACS User Manual Version 5(2):1–298

    Google Scholar 

  32. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78(8):1950–1958

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Nezafat N, Karimi Z, Eslami M, Mohkam M, Zandian S, Ghasemi Y (2016) Designing an efficient multi-epitope peptide vaccine against Vibrio cholerae via combined immunoinformatics and protein interaction based approaches. Comput Biol Chem 62:82–95

    Article  CAS  Google Scholar 

  34. Nezafat N, Eslami M, Negahdaripour M, Rahbar MR, Ghasemi Y (2017) Designing an efficient multi-epitope oral vaccine against Helicobacter pylori using immunoinformatics and structural vaccinology approaches. Mol Biosyst 13(4):699–713

    Article  CAS  Google Scholar 

  35. Eslami M, Nezafat N, Khajeh S, Mostafavi-Pour Z, Bagheri Novir S, Negahdaripour M, Ghasemi Y, Razban V (2018) Deep analysis of N-cadherin/ADH-1 interaction: a computational survey. J Biomol Struct Dyn 1–57

  36. Negahdaripour M, Golkar N, Hajighahramani N, Kianpour S, Nezafat N, Ghasemi Y (2017) Harnessing self-assembled peptide nanoparticles in epitope vaccine design. Biotechnol Adv 35(5):575–596

    Article  CAS  Google Scholar 

  37. Imai K, Mitaku S (2005) Mechanisms of secondary structure breakers in soluble proteins. Biophysics 1:55–65. https://doi.org/10.2142/biophysics.1.55

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  Google Scholar 

  39. Kuriakose A, Chirmule N, Nair P (2016) Immunogenicity of biotherapeutics: causes and association with posttranslational modifications. J Immunol Res 2016:1298473. https://doi.org/10.1155/2016/1298473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Moola ZB, Scawen MD, Atkinson T, Nicholls DJ (1994) Erwinia chrysanthemi L-asparaginase: epitope mapping and production of antigenically modified enzymes. Biochem J 302(Pt 3):921–927

    Article  CAS  Google Scholar 

  41. Van Regenmortel MH (2009) What is a B-cell epitope? Methods Mol Biol 524:3–20. https://doi.org/10.1007/978-1-59745-450-6_1

    Article  CAS  PubMed  Google Scholar 

  42. Ramya LN, Pulicherla KK (2015) Studies on deimmunization of antileukaemic l-asparaginase to have reduced clinical immunogenicity–an in silico approach. Pathol Oncol Res POR 21(4):909–920. https://doi.org/10.1007/s12253-015-9912-0

    Article  CAS  PubMed  Google Scholar 

  43. Meyer DL, Schultz J, Lin Y, Henry A, Sanderson J, Jackson JM, Goshorn S, Rees AR, Graves SS (2001) Reduced antibody response to streptavidin through site-directed mutagenesis. Protein Sci 10(3):491–503. https://doi.org/10.1110/ps.19901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Onda M, Nagata S, FitzGerald DJ, Beers R, Fisher RJ, Vincent JJ, Lee B, Nakamura M, Hwang J, Kreitman RJ, Hassan R, Pastan I (2006) Characterization of the B cell epitopes associated with a truncated form of Pseudomonas exotoxin (PE38) used to make immunotoxins for the treatment of cancer patients. J Immunology 177(12):8822–8834

    Article  CAS  Google Scholar 

  45. Nagata S, Pastan I (2009) Removal of B cell epitopes as a practical approach for reducing the immunogenicity of foreign protein-based therapeutics. Adv Drug Deliv Rev 61(11):977–985. https://doi.org/10.1016/j.addr.2009.07.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Collen D, Bernaerts R, Declerck P, De Cock F, Demarsin E, Jenné S, Laroche Y, Lijnen H, Silence K, Verstreken M (1996) Recombinant staphylokinase variants with altered immunoreactivity: I: construction and characterization. Circulation 94. https://doi.org/10.1161/01.CIR.94.2.197

    Article  CAS  Google Scholar 

  47. Onda M, Beers R, Xiang L, Nagata S, Wang Q-C, Pastan I (2008) An immunotoxin with greatly reduced immunogenicity by identification and removal of B cell epitopes. Proc Natl Acad Sci USA 105(32):11311–11316

    Article  CAS  Google Scholar 

  48. Kodama T, Hamakubo T, Doi H, Sugiyama A, Tsumoto K (2010) Hypo-immunogenic streptavidin and use thereof. International Patent Application WO/2010/095455 (August 26, 2010)

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Acknowledgements

This study was supported by Grant No. 13435 from the Research Council of Shiraz University of Medical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.

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Correspondence to Navid Nezafat or Younes Ghasemi.

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Yari, M., Eslami, M., Ghoshoon, M.B. et al. Decreasing the immunogenicity of Erwinia chrysanthemi asparaginase via protein engineering: computational approach. Mol Biol Rep 46, 4751–4761 (2019). https://doi.org/10.1007/s11033-019-04921-5

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