Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
10515-10529
Series
Applied Microbiology and Biotechnology, Volume 104, issue 24
Abstract
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 asthe 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.
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Keywords
Aldolase, Biocatalysis, Crystal structure determination, C–C bond formation, DERA, Machine learning, Protein engineering
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Citation
Voutilainen , S , Heinonen , M , Andberg , M , Jokinen , E , Maaheimo , H , Pääkkönen , J , Hakulinen , N , Rouvinen , J , Lähdesmäki , H , Kaski , S , Rousu , J , Penttilä , M & Koivula , A 2020 , ' Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods ' , Applied Microbiology and Biotechnology , vol. 104 , no. 24 , pp. 10515-10529 . https://doi.org/10.1007/s00253-020-10960-x