One-shot optimization of multiple enzyme parameters: Tailoring glucose oxidase for pH and electron mediators
Само за регистроване кориснике
2020
Аутори
Ostafe, RalucaFontaine, Nicolas
Frank, David
Ng Fuk Chong, Matthieu
Prodanović, Radivoje
Pandjaitan, Rudy
Offmann, Bernard
Cadet, Frederic
Fischer, Rainer
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
artificial intelligence / directed evolution / multiple parameter improvement / protein sequence activity relationship / protein spectrum / rational screeningИзвор:
Biotechnology and Bioengineering, 2020, 117, 1, 17-29Издавач:
- Willey
DOI: 10.1002/bit.27169
ISSN: 0006-3592
WoS: 000491086600001
Scopus: 2-s2.0-85073966004
Колекције
Институција/група
Hemijski fakultet / Faculty of ChemistryTY - 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 . .