Volume 252, 2024

On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering

Abstract

Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on computation from first principles, while the latter is a combinatorial approach based on chance. Advances in ultrahigh throughput (uHT) screening, next generation sequencing and machine learning may create alternative routes to engineered proteins, where functional information linked to specific sequences is interpreted and extrapolated in silico. In particular, the miniaturisation of functional tests in water-in-oil emulsion droplets with picoliter volumes and their rapid generation and analysis (>1 kHz) allows screening of >107-membered libraries in a day. Subsequently, decoding the selected clones by short or long-read sequencing methods leads to large sequence-function datasets that may allow extrapolation from experimental directed evolution to further improved mutants beyond the observed hits. In this work, we explore experimental strategies for how to draw up ‘fitness landscapes’ in sequence space with uHT droplet microfluidics, review the current state of AI/ML in enzyme engineering and discuss how uHT datasets may be combined with AI/ML to make meaningful predictions and accelerate biocatalyst engineering.

Graphical abstract: On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering

  • This article is part of the themed collection: Biocatalysis

Associated articles

Supplementary files

Article information

Article type
Paper
Submitted
25 mar 2024
Accepted
23 apr 2024
First published
23 apr 2024
This article is Open Access
Creative Commons BY-NC license

Faraday Discuss., 2024,252, 89-114

On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering

M. Gantz, S. V. Mathis, F. E. H. Nintzel, P. Lio and F. Hollfelder, Faraday Discuss., 2024, 252, 89 DOI: 10.1039/D4FD00065J

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