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

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

SUBMITTER: Inoue K 

PROVIDER: S-EPMC7979833 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature