TY  - JOUR
AU  - Ostafe, Raluca
AU  - Fontaine, Nicolas
AU  - Frank, David
AU  - Ng Fuk Chong, Matthieu
AU  - Prodanović, Radivoje
AU  - Pandjaitan, Rudy
AU  - Offmann, Bernard
AU  - Cadet, Frederic
AU  - Fischer, Rainer
PY  - 2020
UR  - https://cherry.chem.bg.ac.rs/handle/123456789/3784
AB  - Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure–activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product. Here, we applied our innovative sequence–activity relationship methodology (innov'SAR) to improve glucose oxidase activity in the presence of different mediators across a range of pH values. Using this machine learning approach, a predictive model was developed and the optimal combination of mutations was determined, leading to a glucose oxidase mutant (P1) with greater specificity for the mediators ferrocene–methanol (12-fold) and nitrosoaniline (8-fold), compared to the wild-type enzyme, and better performance in three pH-adjusted buffers. The kcat/KM ratio of P1 increased by up to 121 folds compared to the wild type enzyme at pH 5.5 in the presence of ferrocene methanol.
PB  - Willey
T2  - Biotechnology and Bioengineering
T1  - One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators
VL  - 117
IS  - 1
SP  - 17
EP  - 29
DO  - 10.1002/bit.27169
ER  - 

@article{
author = "Ostafe, Raluca and Fontaine, Nicolas and Frank, David and Ng Fuk Chong, Matthieu and Prodanović, Radivoje and Pandjaitan, Rudy and Offmann, Bernard and Cadet, Frederic and Fischer, Rainer",
year = "2020",
abstract = "Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure–activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product. Here, we applied our innovative sequence–activity relationship methodology (innov'SAR) to improve glucose oxidase activity in the presence of different mediators across a range of pH values. Using this machine learning approach, a predictive model was developed and the optimal combination of mutations was determined, leading to a glucose oxidase mutant (P1) with greater specificity for the mediators ferrocene–methanol (12-fold) and nitrosoaniline (8-fold), compared to the wild-type enzyme, and better performance in three pH-adjusted buffers. The kcat/KM ratio of P1 increased by up to 121 folds compared to the wild type enzyme at pH 5.5 in the presence of ferrocene methanol.",
publisher = "Willey",
journal = "Biotechnology and Bioengineering",
title = "One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators",
volume = "117",
number = "1",
pages = "17-29",
doi = "10.1002/bit.27169"
}

Ostafe, R., Fontaine, N., Frank, D., Ng Fuk Chong, M., Prodanović, R., Pandjaitan, R., Offmann, B., Cadet, F.,& Fischer, R.. (2020). One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators. in Biotechnology and Bioengineering
Willey., 117(1), 17-29.
https://doi.org/10.1002/bit.27169

Ostafe R, Fontaine N, Frank D, Ng Fuk Chong M, Prodanović R, Pandjaitan R, Offmann B, Cadet F, Fischer R. One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators. in Biotechnology and Bioengineering. 2020;117(1):17-29.
doi:10.1002/bit.27169 .

Ostafe, Raluca, Fontaine, Nicolas, Frank, David, Ng Fuk Chong, Matthieu, Prodanović, Radivoje, Pandjaitan, Rudy, Offmann, Bernard, Cadet, Frederic, Fischer, Rainer, "One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators" in Biotechnology and Bioengineering, 117, no. 1 (2020):17-29,
https://doi.org/10.1002/bit.27169 . .