Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

Braun Markus, Gruber Christian C*, Krassnigg Andreas, Kummer Arkadij, Lutz Stefan, Oberdorfer Gustav, Siirola Elina, Snajdrova Radka*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Emerging computational tools promise to revolutionize protein engineering for biocatalytic applications and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization, and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This Perspective aims to showcase the current status of applications in pharmaceutical industry and also to discuss and celebrate the innovative approaches in protein science by highlighting their potential in selected recent developments and offering thoughts on future opportunities for biocatalysis. It also critically assesses the technology’s limitations, unanswered questions, and unmet challenges.

Original languageEnglish
Pages (from-to)14454-14469
Number of pages16
JournalACS Catalysis
Volume13
Issue number21
DOIs
Publication statusPublished - 3 Nov 2023

Keywords

  • biocatalysis
  • enzyme design
  • enzyme engineering
  • enzyme evolution
  • enzyme optimization
  • machine learning

ASJC Scopus subject areas

  • Catalysis
  • General Chemistry

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