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

Maximilian Gantz1, Simon V Mathis2, Friederike E H Nintzel1

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Ultrahigh throughput (uHT) screening using droplet microfluidics enables rapid, large-scale protein engineering. Combining this with artificial intelligence (AI) and machine learning (ML) accelerates the discovery of improved biocatalysts.

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Area of Science:

  • Biotechnology
  • Protein Engineering
  • Computational Biology

Background:

  • Protein engineering traditionally uses protein design and directed evolution.
  • Advances in screening technologies and machine learning offer new avenues for protein engineering.
  • Ultrahigh throughput (uHT) screening methods are revolutionizing the field.

Purpose of the Study:

  • To explore experimental strategies for creating sequence-space fitness landscapes using uHT droplet microfluidics.
  • To review the current applications of AI/ML in enzyme engineering.
  • To discuss the integration of uHT datasets with AI/ML for predictive biocatalyst engineering.

Main Methods:

  • Utilizing droplet microfluidics for picoliter-volume functional tests at high throughput (>1 kHz).
  • Screening of libraries exceeding 10^7 members within a single day.
  • Employing next-generation sequencing to decode selected clones and generate sequence-function datasets.

Main Results:

  • Demonstrated feasibility of generating comprehensive fitness landscapes in sequence space.
  • Established large sequence-function datasets from experimental evolution.
  • Identified potential for AI/ML to extrapolate beyond observed experimental hits.

Conclusions:

  • uHT droplet microfluidics combined with AI/ML offers a powerful approach to accelerate biocatalyst engineering.
  • Integration of experimental data and computational prediction can lead to novel protein designs.
  • This integrated strategy promises to significantly advance the field of protein engineering.