Faculty of Chemistry Repository - Cherry
University of Belgrade - Faculty of Chemistry
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   Cherry
  • Hemijski fakultet
  • Publikacije
  • View Item
  •   Cherry
  • Hemijski fakultet
  • Publikacije
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators

Authorized Users Only
2020
Authors
Ostafe, Raluca
Fontaine, Nicolas
Frank, David
Ng Fuk Chong, Matthieu
Prodanović, Radivoje
Pandjaitan, Rudy
Offmann, Bernard
Cadet, Frederic
Fischer, Rainer
Article (Published version)
Metadata
Show full item record
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 specific...ity 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.

Keywords:
artificial intelligence / directed evolution / multiple parameter improvement / protein sequence activity relationship / protein spectrum / rational screening
Source:
Biotechnology and Bioengineering, 2020, 117, 1, 17-29
Publisher:
  • Willey

DOI: 10.1002/bit.27169

ISSN: 0006-3592

WoS: 000491086600001

Scopus: 2-s2.0-85073966004
[ Google Scholar ]
11
10
URI
https://cherry.chem.bg.ac.rs/handle/123456789/3784
Collections
  • Publikacije
Institution/Community
Hemijski fakultet
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 . .

DSpace software copyright © 2002-2015  DuraSpace
About CHERRY - CHEmistry RepositoRY | Send Feedback

re3dataOpenAIRERCUB
 

 

All of DSpaceInstitutions/communitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About CHERRY - CHEmistry RepositoRY | Send Feedback

re3dataOpenAIRERCUB