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

• Published in: ACS catalysis 2023-11, Vol.13 (21), p.14454-14469
• Main Authors: Markus, Braun, C, Gruber Christian, Andreas, Krassnigg, Arkadij, Kummer, Stefan, Lutz, Gustav, Oberdorfer, Elina, Siirola, Radka, Snajdrova
• Format: Article
• Language: English
• ISSN: 2155-5435; 2155-5435
• DOI: 10.1021/acscatal.3c03417

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.