In vitro continuous protein evolution empowered by machine learning and automation

Tianhao Yu, Aashutosh Girish Boob, Nilmani Singh, Yufeng Su, Huimin Zhao

Research output: Contribution to journalReview articlepeer-review

Abstract

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, we propose a closed-loop in vitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

Original languageEnglish (US)
Pages (from-to)633-644
Number of pages12
JournalCell Systems
Volume14
Issue number8
DOIs
StatePublished - Aug 16 2023

Keywords

  • automation
  • closed-loop
  • continuous evolution
  • directed evolution
  • machine learning

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Cell Biology
  • Histology

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