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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

Abstract

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.

Keywords

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

Abbreviation

3D

Three-dimensional

EFSA

European Food Safety Authority

GRAVY

Grand average of hydropathy

MSMS

Michel Sanner’s molecular surface

MD simulation

Molecular dynamics simulation

SASA

Solvent-accesible surface area

Rg

Radius of gyration

RMSD

Root mean square deviation

RMSF

Root mean square fluctuation

SVM

Support vector machine

Notes

Acknowledgments

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)

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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

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