On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering
Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel, Píetro Lió, Florian Hollfelder
Abstract
-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.
Topics & Concepts
ThroughputProtein engineeringHigh-throughput screeningDirected evolutionComputer scienceComputationSynthetic biologyMachine learningArtificial intelligenceDirected Molecular EvolutionComputational biologyChemistryBioinformaticsBiologyAlgorithmGeneGeneticsMutantTelecommunicationsWirelessEnzymeBiochemistryInnovative Microfluidic and Catalytic Techniques InnovationEnzyme Catalysis and ImmobilizationChemical Synthesis and Analysis