Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins

Yutaka Saito, Misaki Oikawa, Hikaru Nakazawa, Teppei Niide, Tomoshi Kameda, Koji Tsuda, Mitsuo Umetsu

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we conduct two rounds of mutagenesis where an initial library of protein variants is used to train a machine-learning model to guide mutagenesis for the second-round library. This enables us to prepare a small library suited for screening experiments with high enrichment of functional proteins. We demonstrated a proof-of-concept of our approach by altering the reference green fluorescent protein (GFP) so that its fluorescence is changed into yellow. We successfully obtained a number of proteins showing yellow fluorescence, 12 of which had longer wavelengths than the reference yellow fluorescent protein (YFP). These results show the potential of our approach as a powerful method for directed evolution of fluorescent proteins.

Original languageEnglish
Pages (from-to)2014-2022
Number of pages9
JournalACS Synthetic Biology
Volume7
Issue number9
DOIs
Publication statusPublished - 2018 Sep 21

Keywords

  • fluorescent protein
  • machine learning
  • molecular evolution
  • mutagenesis
  • protein engineering

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

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