Functional characterization of red-shifted rhodopsin channels from giant viruses explored by a machine-learning model for long-wavelength optogenetics
Shunki Takaramoto, Chenxiang Zhao, Masayuki Karasuyama, Frederik Schulz, Masaya Watanabe, Yuma Kawasaki, Takashi Nagata, Masae Konno, Naoya Morimoto, Masahiro Fukuda, Yu Inatsu, Hiromu Yawo, Oded Béjà, Hideaki E. KATO, Tanja Woyke, , Keiichi Inoue
The paper uses a machine-learning model to predict red-shifted channelrhodopsin variants, identifying ChR024 with a maximum absorption wavelength of ~578 nm for optogenetics.
bioRxiv · Sep 16, 2025 · PreprintBiophysics