Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design
| Autor: | Yu Inatsu, Takashi Nagata, Kei Yura, Hideki Kandori, Oded Béjà, Daichi Yamada, Kentaro Mannen, Ichiro Takeuchi, Masayuki Karasuyama, Masae Konno, Hiromu Yawo, Keiichi Inoue, Ryoko Nakamura |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
genetic structures QH301-705.5 Computer science Biophysics Medicine (miscellaneous) Optogenetics 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Biochemistry General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences Biology (General) biology business.industry Protein database A protein 0104 chemical sciences Computational biology and bioinformatics 030104 developmental biology Rhodopsin Bayesian experimental design biology.protein Artificial intelligence sense organs General Agricultural and Biological Sciences business computer |
| Zdroj: | Communications Biology Communications Biology, Vol 4, Iss 1, Pp 1-11 (2021) |
| ISSN: | 2399-3642 |
| Popis: | 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). 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. |
| Databáze: | OpenAIRE |
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