Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

Keiichi Inoue*, Masayuki Karasuyama, Ryoko Nakamura, Masae Konno, Daichi Yamada, Kentaro Mannen, Takashi Nagata, Yu Inatsu, Hiromu Yawo, Kei Yura, Oded Béjà, Hideki Kandori, Ichiro Takeuchi

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from representative rhodopsins in the same subfamily. Among 3,022 ion-pumping rhodopsins that were suggested by a protein BLAST search in several protein databases, the ML-based method selected 65 candidate rhodopsins. The wavelengths of 39 of them were able to be experimentally determined by expressing proteins with the Escherichia coli system, and 32 (82%, p = 7.025 × 10−5) actually showed red-shift gains. In addition, four showed red-shift gains >20 nm, and two were found to have desirable ion-transporting properties, indicating that they would be potentially useful in optogenetics. These findings suggest that data-driven ML-based approaches play effective roles in the experimental design of rhodopsin and other photobiological studies. (141/150 words).

本文言語English
論文番号362
ジャーナルCommunications Biology
4
1
DOI
出版ステータスPublished - 2021 12月

ASJC Scopus subject areas

  • 医学(その他)
  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(全般)

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