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Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods
ABSTRACT: In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broa...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671976/ https://www.ncbi.nlm.nih.gov/pubmed/33147349 http://dx.doi.org/10.1007/s00253-020-10960-x |
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author | Voutilainen, Sanni Heinonen, Markus Andberg, Martina Jokinen, Emmi Maaheimo, Hannu Pääkkönen, Johan Hakulinen, Nina Rouvinen, Juha Lähdesmäki, Harri Kaski, Samuel Rousu, Juho Penttilä, Merja Koivula, Anu |
author_facet | Voutilainen, Sanni Heinonen, Markus Andberg, Martina Jokinen, Emmi Maaheimo, Hannu Pääkkönen, Johan Hakulinen, Nina Rouvinen, Juha Lähdesmäki, Harri Kaski, Samuel Rousu, Juho Penttilä, Merja Koivula, Anu |
author_sort | Voutilainen, Sanni |
collection | PubMed |
description | ABSTRACT: In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde. KEY POINTS: • DERA aldolases are promiscuous enzymes. • Synthetic utility of DERA aldolase was improved by protein engineering approaches. • Machine learning methods aid the protein engineering of DERA. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00253-020-10960-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7671976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-76719762020-11-20 Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods Voutilainen, Sanni Heinonen, Markus Andberg, Martina Jokinen, Emmi Maaheimo, Hannu Pääkkönen, Johan Hakulinen, Nina Rouvinen, Juha Lähdesmäki, Harri Kaski, Samuel Rousu, Juho Penttilä, Merja Koivula, Anu Appl Microbiol Biotechnol Biotechnologically Relevant Enzymes and Proteins ABSTRACT: In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde. KEY POINTS: • DERA aldolases are promiscuous enzymes. • Synthetic utility of DERA aldolase was improved by protein engineering approaches. • Machine learning methods aid the protein engineering of DERA. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s00253-020-10960-x) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-11-04 2020 /pmc/articles/PMC7671976/ /pubmed/33147349 http://dx.doi.org/10.1007/s00253-020-10960-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Biotechnologically Relevant Enzymes and Proteins Voutilainen, Sanni Heinonen, Markus Andberg, Martina Jokinen, Emmi Maaheimo, Hannu Pääkkönen, Johan Hakulinen, Nina Rouvinen, Juha Lähdesmäki, Harri Kaski, Samuel Rousu, Juho Penttilä, Merja Koivula, Anu Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title | Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title_full | Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title_fullStr | Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title_full_unstemmed | Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title_short | Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods |
title_sort | substrate specificity of 2-deoxy-d-ribose 5-phosphate aldolase (dera) assessed by different protein engineering and machine learning methods |
topic | Biotechnologically Relevant Enzymes and Proteins |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671976/ https://www.ncbi.nlm.nih.gov/pubmed/33147349 http://dx.doi.org/10.1007/s00253-020-10960-x |
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