Cargando…

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

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....

Descripción completa

Detalles Bibliográficos
Autores principales: Inoue, Keiichi, Karasuyama, Masayuki, Nakamura, Ryoko, Konno, Masae, Yamada, Daichi, Mannen, Kentaro, Nagata, Takashi, Inatsu, Yu, Yawo, Hiromu, Yura, Kei, Béjà, Oded, Kandori, Hideki, Takeuchi, Ichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979833/
https://www.ncbi.nlm.nih.gov/pubmed/33742139
http://dx.doi.org/10.1038/s42003-021-01878-9
Descripción
Sumario: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).