Publication

Genomics-based discovery and engineering of biocatalysts for conversion of amines

Heberling, M. M. 2017 [Groningen]: University of Groningen. 222 p.

Research output: ScientificDoctoral Thesis

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  • Title and contents

    Final publisher's version, 212 KB, PDF-document

  • Chapter 1

    Final publisher's version, 2 MB, PDF-document

    Embargo ends: 13/10/2018

  • Chapter 2

    Final publisher's version, 3 MB, PDF-document

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  • Chapter 3

    Final publisher's version, 1 MB, PDF-document

  • Chapter 4

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  • Chapter 5

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  • Chapter 6

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  • Chapter 7

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  • English summary

    Final publisher's version, 153 KB, PDF-document

  • Nederlandse samenvatting

    Final publisher's version, 165 KB, PDF-document

  • Curriculum vitae

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  • Publications

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  • Acknowledgements

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  • Complete thesis

    Final publisher's version, 17 MB, PDF-document

    Embargo ends: 13/10/2018

  • Propositions

    Final publisher's version, 143 KB, PDF-document

  • Matthew Michael Heberling
Enzymes have emerged as eco-friendly catalysts in synthetic routes of industrial chemicals and therapeutics because of their exquisite enhancement of chemical reactions. In particular, amine-forming enzymes have received a lot of attention for their ability to synthesize β-amino acids and primary amines, which are prevalent components in pharmaceuticals. However, enhancing the industrial potential of enzymes requires novel enzyme discoveries or engineering known enzymes to acquire the desired features to fit an industrial process. This thesis begins with a review of industrial biocatalysis and the barriers to fully exploit enzymes for industrial catalysis. Shifting from a ‘data producing’ to a ‘data processing’ mindset will be paramount in advancing industrial biocatalysis. Examples of the discovery and engineering of amine-forming enzymes are reported in this thesis, along with computational approaches integrated throughout in order to accelerate discoveries or understand and predict targeted features of the enzymes. A final outlook concludes that pattern recognition using advanced computer algorithms (machine learning) may likely advance our fundamental understanding of various enzyme features in order to augment their industrial potential.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
Award date13-Oct-2017
Place of Publication[Groningen]
Publisher
Print ISBNs978-94-034-0132-4
Electronic ISBNs978-94-034-0131-7
StatePublished - 2017

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