Journal of Molecular Modeling

, 25:337 | Cite as

Designing a less immunogenic nattokinase from Bacillus subtilis subsp. natto: a computational mutagenesis

  • Yoanes Maria Vianney
  • Stanley Evander Emeltan Tjoa
  • Reza Aditama
  • Sulisyto Emantoko Dwi PutraEmail author
Original Paper


Nattokinase is an enzyme produced by Bacillus subtilis subsp. natto that contains strong fibrinolytic activity. It has potential to treat cardiovascular diseases. In silico analysis revealed that nattokinase is considered as an antigen, thus hindering its application for injectable therapeutic protein. Various web servers were used to predict B-cell epitopes of nattokinase both continuously and discontinuously to determine which amino acid residues had been responsible for the immunogenicity. With the exclusion of the predicted conserved amino acids, four amino acids such as S18, Q19, T242, and Q245 were allowed for mutation. Substitution mutation was done to lower the immunogenicity of native nattokinase. Through the stability of the mutated protein with the help of Gibbs free energy difference, the proposed mutein was S18D, Q19I, T242Y, and Q245W. The 3D model of the mutated nattokinase was modeled and validated with various tools. Physicochemical properties and stability analysis of the protein indicated that the mutation brought higher stability without causing any changes in the catalytic site of nattokinase. Molecular dynamics simulation implied that the mutation indicated similar stability, conformation, and behavior compared to the native nattokinase. These results are highly likely to contribute to the wet lab experiment to develop safer nattokinase.


B-cell epitopes Bacillus subtilis subsp. natto Bioinformatics Immunogenicity In silico mutagenesis 





European Food Safety Authority


Grand average of hydropathy


Michel Sanner’s molecular surface

MD simulation

Molecular dynamics simulation


Solvent-accesible surface area


Radius of gyration


Root mean square deviation


Root mean square fluctuation


Support vector machine



Thanks to Helen Hendaria Kamandhari, Ph.D. for her proofreading and comments.

Funding information

This study is funded by the Faculty of Biotechnology, University of Surabaya.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

894_2019_4225_MOESM1_ESM.partial (213 kb)
ESM 1 (PARTIAL 213 kb)


  1. 1.
    Sumi H, Hamada H, Tsushima H, Mihara H, Muraki H (1987) A novel fibrinolytic enzyme (nattokinase) in the vegetable cheese Natto; a typical and popular soybean food in the Japanese diet. Experientia 43(10):1110–1111. CrossRefPubMedGoogle Scholar
  2. 2.
    Fujita M, Nomura K, Hong K, Ito Y, Asada A, Nishimuro S (1993) Purification and characterization of a strong fibrinolytic enzyme (nattokinase) in the vegetable cheese natto, a popular soybean fermented food in Japan. Biochem Biophys Res Commun 197(3):1340–1347. CrossRefPubMedGoogle Scholar
  3. 3.
    Nakamura T, Yamagata Y, Ichishima E (1992) Nucleotide sequence of the subtilisin NAT gene, aprN, of Bacillus subtilis (natto). Biosci Biotechnol Biochem 56(11):1869–1871. CrossRefPubMedGoogle Scholar
  4. 4.
    Yanagisawa Y, Chatake T, Chiba-Kamoshida K, Naito S, Ohsugi T, Sumi H, Yasuda I, Morimoto Y (2010) Purification, crystallization and preliminary X-ray diffraction experiment of nattokinase from Bacillus subtilis natto. Acta Crystallogr Sect F Struct Biol Cryst Commun 66(12):1670–1673. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Murakami K, Yamanaka N, Ohnishi K, Fukayama M, Yoshino M (2012) Inhibition of angiotensin I converting enzyme by subtilisin NAT (nattokinase) in natto, a Japanese traditional fermented food. Food Funct 3(6):674–678. CrossRefPubMedGoogle Scholar
  6. 6.
    Dabbagh F, Negahdaripour M, Berenjian A, Behfar A, Mohammadi F, Zamani M, Irajie C, Ghasemi Y (2014) Nattokinase: production and application. Appl Microbiol Biotechnol 98(22):9199–9206. CrossRefPubMedGoogle Scholar
  7. 7.
    Lee BH, Lai YS, Wu SC (2015) Antioxidation, angiotensin converting enzyme inhibition activity, nattokinase, and antihypertension of Bacillus subtilis (natto)-fermented pigeon pea. J Food Drug Anal 23(4):750–757. CrossRefPubMedGoogle Scholar
  8. 8.
    Chen H, McGowan EM, Ren N, Lal S, Nassif N, Shad-Kaneez F, Qu X, Lin Y (2018) Nattokinase: a promising alternative in prevention and treatment of cardiovascular diseases. Biomark Insights 13:1177271918785130. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Feng R, Li J, Chen J, Duan L, Liu X, Di D, Deng Y, Song Y (2018) Preparation and toxicity evaluation of a novel nattokinase-tauroursodeoxycholate complex. Asian J Pharm Sci 13(2):173–182. CrossRefGoogle Scholar
  10. 10.
    Kurosawa Y, Nirengi S, Homma T, Esaki K, Ohta M, Clark JF, Hamaoka T (2015) A single-dose of oral nattokinase potentiates thrombolysis and anti-coagulation profiles. Sci Rep 5:11601. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Lampe BJ, English JC (2016) Toxicological assessment of nattokinase derived from Bacillus subtilis var. natto. Food Chem Toxicol 88:87–99. CrossRefPubMedGoogle Scholar
  12. 12.
    Weng Y, Yao J, Sparks S, Wang KY (2017) Nattokinase: an oral antithrombotic agent for the prevention of cardiovascular disease. Int J Mol Sci 18(3):523. CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Chitte RR, Deshmukh SV, Kanekar PP (2011) Production, purification, and biochemical characterization of a fibrinolytic enzyme from thermophilic Streptomyces sp. MCMB-379. Appl Biochem Biotechnol 165(5-6):1406–1413. CrossRefPubMedGoogle Scholar
  14. 14.
    EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA) (2016) Safety of fermented soybean extract NSK-SD® as a novel food pursuant to Regulation (EC) No 258/97. EFSA J 14(7):e04541. CrossRefGoogle Scholar
  15. 15.
    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 37(2):523–536. CrossRefPubMedGoogle Scholar
  16. 16.
    Fattahian Y, Riahi-Madvar A, Mirzaee R, Asadikaram G, Rahbar MR (2017) In silico locating the immune-reactive segments of Lepidium draba peroxidase and designing a less immune-reactive enzyme derivative. Comp Biol Chem 70:21–30. CrossRefGoogle Scholar
  17. 17.
    Jespersen MC, Peters B, Nielsen M, Marcatili P (2017) BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 45(W1):W24–W29. CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Potocnakova L, Bhide M, Pulzova LB (2016) An introduction to B-cell epitope mapping and in silico epitope prediction. J Immunol Res 2016(6760830):1–11. CrossRefGoogle Scholar
  19. 19.
    Singh H, Ansari HR, Raghava GP (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PloS One 8(5):e62216. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    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):e1002829. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–48. CrossRefPubMedGoogle Scholar
  22. 22.
    Kolaskar AS, Tongaonkar PC (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276(1-2):172–174. CrossRefGoogle Scholar
  23. 23.
    Weng M, Deng X, Bao W, Zhu L, Wu J, Cai Y, Jia Y, Zheng Z, Zou G (2015) Improving the activity of the subtilisin nattokinase by site-directed mutagenesis and molecular dynamics simulation. Biochem Biophys Res Commun 465(3):580–586. CrossRefPubMedGoogle Scholar
  24. 24.
    Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8(4):1–7. CrossRefGoogle Scholar
  25. 25.
    Yao B, Zhang L, Liang S, Zhang C (2012) SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PloS One. 7(9):e45152. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Ansari HR, Raghava GP (2010) Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res 6(6):1–9. CrossRefGoogle Scholar
  27. 27.
    Saha S, Raghava GPS (2004) BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. International Conference on Artificial Immune Systems. Springer, Berlin, pp 197–204. CrossRefGoogle Scholar
  28. 28.
    Emini EA, Hughes JV, Perlow D, Boger J (1985) Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 55(3):836–839PubMedPubMedCentralGoogle Scholar
  29. 29.
    Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 22(2):195–201. CrossRefPubMedGoogle Scholar
  30. 30.
    Benkert P, Biasini M, Schwede T (2010) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27(3):343–350. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Cassarino TG, Bertoni M, Bordoli L, Schwede T (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42(W1):W252–W258. CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33(suppl_2):W306-W310. CrossRefPubMedCentralGoogle Scholar
  33. 33.
    Yang Y, Zhou Y (2008) Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions. Protein Sci 17(7):1212–1219. CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Chen VB, Arendall III WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66(1):12–21. CrossRefPubMedGoogle Scholar
  36. 36.
    Lovell SC, Davis IW, Arendall III WB, de Bakker PIW, Word JM, Prisant MG, Richardson JS, Richardson DC (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation. Proteins 50(3):437–450. CrossRefGoogle Scholar
  37. 37.
    Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A (2017) ProQ3D: improved model quality assessments using deep learning. Bioinformatics. 33(10):1578–1580. CrossRefPubMedGoogle Scholar
  38. 38.
    Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83. CrossRefPubMedGoogle Scholar
  39. 39.
    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. CrossRefPubMedGoogle Scholar
  40. 40.
    Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Porter CT, Bartlett GJ, Thornton JM (2004) The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res 32(suppl_1):D129–D133. CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Schmidtke P, Le Guilloux V, Maupetit J, Tuffery P (2010) Fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38(suppl_2):W582-W589. CrossRefPubMedCentralGoogle Scholar
  43. 43.
    Olsson MHM, Sondergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7(2):525–537. CrossRefPubMedGoogle Scholar
  44. 44.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. CrossRefGoogle Scholar
  45. 45.
    van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible and free. J Comp Chem 26:1701–1718. CrossRefGoogle Scholar
  46. 46.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105(28):6474–6487. CrossRefGoogle Scholar
  47. 47.
    Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126(1):014101. CrossRefPubMedGoogle Scholar
  48. 48.
    Humphrey W, Dalke A, Schulten K (1996) VMD - visual molecular dynamics. J Mol Graph 14(1):33–38. CrossRefPubMedGoogle Scholar
  49. 49.
    Bazmara H, Rasooli I, Jahangiri A, Sefid F, Astaneh SDA, Payandeh Z (2019) Antigenic properties of iron regulated proteins in Acinetobacter baumannii: an in silico approach. Int J Pept Res Ther 25(1):205–213. CrossRefGoogle Scholar
  50. 50.
    Alam A, Ali S, Ahamad S, Malik MZ, Ishrat R (2016) From ZikV genome to vaccine: in silico approach for the epitope-based peptide vaccine against Zika virus envelope glycoprotein. Immunology 149(4):386–399. CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    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. Comp Biol Chem 62:82–95. CrossRefGoogle Scholar
  52. 52.
    Yasmin T, Akter S, Debnath M, Ebihara A, Nakagawa T, Nabi AN (2016) In silico proposition to predict cluster of B-and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane associated proteins of C. jejuni. In Silico Pharmacol 4(5):1–10. CrossRefGoogle Scholar
  53. 53.
    Shi J, Zhang J, Li S, Sun J, Teng Y, Wu M, Li J, Li Y, Hu N, Wang H, Hu Y (2015) Epitope-based vaccine target screening against highly pathogenic MERS-CoV: an in silico approach applied to emerging infectious diseases. PloS One 10(12):e0144475. CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Sefid F, Rasooli I, Jahangiri A, Bazmara H (2015) Functional exposed amino acids of BauA as potential immunogen against Acinetobacter baumannii. Acta Biotheor 63(2):129–149. CrossRefPubMedGoogle Scholar
  55. 55.
    Pahil S, Taneja N, Ansari HR, Raghava GPS (2017) In silico analysis to identify vaccine candidates common to multiple serotypes of Shigella and evaluation of their immunogenicity. PloS One 12(8):e0180505. CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Talukdar S, Bayan U, Saikia KK (2017) In silico identification of vaccine candidates against Klebsiella oxytoca. Comp Biol Chem 69:48–54. CrossRefGoogle Scholar
  57. 57.
    Ramya LN, Pulicherla KK (2015) Studies on deimmunization of antileukaemic L-asparaginase to have reduced clinical immunogenicity-an in silico approach. Pathol Oncol Res 21(4):909–920. CrossRefPubMedGoogle Scholar
  58. 58.
    Beezhold DH, Hickey VL, Sussman GL (2001) Mutational analysis of the IgE epitopes in the latex allergen Hev b 5. J. Allergy Clin Immunol 107(6):1069–1076. CrossRefPubMedGoogle Scholar
  59. 59.
    Eisenberg D, Lüthy R, Bowie JU (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 277:396–404. CrossRefPubMedGoogle Scholar
  60. 60.
    Khor BY, Tye GJ, Lim TS, Noordin R, Choong YS (2014) The structure and dynamics of BmR1 protein from Brugia malayi: In silico approaches. Int J Mol Sci 15(6):11082–11099. CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Messaoudi A, Belguith H, Hamida JB (2013) Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase. Theor Biol Med Model 10(1):22. CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Guruprasad K, Reddy BB, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng Des Sel 4(2):155–161. CrossRefGoogle Scholar
  63. 63.
    Ikai A (1980) Thermostability and aliphatic index of globular proteins. J Biochem 88(6):1895–1898. CrossRefPubMedGoogle Scholar
  64. 64.
    Pellequer JL, Westhof E, Van Regenmortel MH (1993) Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol Lett 36(1):83–99. CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Rajendran V, Purohit R, Sethumadhavan R (2012) In silico investigation of molecular mechanism of laminopathy caused by a point mutation (R482W) in lamin A/C protein. Amino acids 43(2):603–615. CrossRefPubMedGoogle Scholar
  66. 66.
    Kamaraj B, Purohit R (2013) In silico screening and molecular dynamics simulation of disease-associated nsSNP in TYRP1 gene and its structural consequences in OCA3. Biomed Res Int 2013:697051. CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Sharp PM, Li WH (1987) The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281–1295. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of BiotechnologyUniversity of SurabayaSurabayaIndonesia
  2. 2.Biochemistry Research Group, Department of Chemistry, Faculty of Mathematics and Natural SciencesBandung Institute of TechnologyBandungIndonesia

Personalised recommendations