Journal
COMMUNICATIONS BIOLOGY
Volume 4, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42003-021-01878-9
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Funding
- Japan Society for the Promotion of Science (JSPS) [17H03007, 17H04694, 16H06538, 19H04959, 17H00758, 20H00601]
- Japan Science and Technology Agency (JST), PRESTO, Japan [JPMJPR15P2, JPMJPR15N2]
- CREST, Japan [JPMJCR1502]
- RIKEN AIP
- Louis and Lyra Richmond Memorial Chair in Life Sciences
- Grants-in-Aid for Scientific Research [20H00601] Funding Source: KAKEN
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Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift gains.
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=7.025x10(-5)) actually showed red-shift gains. In addition, four showed red-shift gains >20nm, 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). Inoue, Takeuchi and colleagues propose a machine learning-based protocol to screen rhodopsins for their likelihood to be red-shifted. After experimental verification, their tool shows remarkable success at identifying rhodopsins that showed red-shift gains.
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