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

Inoue, K.; Karasuyama, M.; Nakamura, R.; Konno, M.; Yamada, D.; Mannen, K.; Nagata, T.; Inatsu, Y.; Yura, K.; Beja, O.; Kandori, H.; Takeuchi, I.

doi:10.1101/2020.04.21.052548

bioRxiv · 10.1101/2020.04.21.052548

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

Inoue, K.·Karasuyama, M.·Nakamura, R.·Konno, M.·Yamada, D.·Mannen, K.·Nagata, T.·Inatsu, Y.·Yura, K.·Beja, O.·Kandori, H.·Takeuchi, I.

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Abstract

Microbial rhodopsins are photoreceptive membrane proteins utilized as molecular tools in optogenetics. In this paper, a machine learning (ML)-based model was constructed to approximate the relationship between amino acid sequences and absorption wavelengths using ~800 rhodopsins with known absorption wavelengths. This ML-based model was specifically designed for screening rhodopsins that are red-shifted from representative rhodopsins in the same subfamily. Among 5,558 candidate rhodopsins suggested by a protein BLAST search of several protein databases, 40 were selected by the ML-based model. The wavelengths of these 40 selected candidates were experimentally investigated, and 32 (80%) 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 were potentially useful in optogenetics. These findings suggest that an ML-based model can reduce the cost for exploring new functional proteins.

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Inoue, K., Karasuyama, M., Nakamura, R., Konno, M., Yamada, D., Mannen, K., Nagata, T., Inatsu, Y., Yura, K., Beja, O., Kandori, H., Takeuchi, I.. 2020-04-23. Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design. https://doi.org/10.1101/2020.04.21.052548

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