Channelrhodopsins (ChRs) are light-gated ion channels. These proteins are widely used in optogenetics to optically manipulate neural activity. However, manipulation using short-wavelength light to activate ChRs causes cell toxicity and is hampered by low tissue penetration. To overcome these difficulties, although several red-shifted ChR variants have been identified, further red-shift is required for more efficient and noninvasive neural control. While molecular screening of ChRs requires high-cost experiments, recent machine-learning-based protein functionality prediction enables more efficient selection of target proteins for characterization. Here, we constructed an elastic-net machine-learning model trained on 1,163 experimental data to predict the maximum absorption wavelength ( λ max ) of uncharacterized ChRs. The model suggested several red-shifted candidates, and we identified a viral rhodopsin channel, ChR024, with the second-longest λ max as a cation-conducting ChR ( λ max = ~578 nm) after Chrimson ( λ max = ~580 nm). This result demonstrates the high impact of ML on reducing the screening costs of functional proteins.
Takaramoto et al. (Wed,) studied this question.
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