Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.
Commun Biol
; 4(1): 362, 2021 03 19.
Article
en En
| MEDLINE
| ID: mdl-33742139
ABSTRACT
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).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Rodopsinas Microbianas
/
Optogenética
/
Aprendizaje Automático
/
Canales Iónicos
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Commun Biol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Japón