タンパク質高性能化研究をロボットで自動化 ―AIが提案した候補を迅速に試せる研究基盤を開発―

2026-08-06 東京大学,科学技術振興機構

遺伝子改変からタンパク質の作製、機能評価までを行える自動実験システム Rhobot-Screenの概略図

＜関連情報＞

• https://www.issp.u-tokyo.ac.jp/maincontents/news2.html?pid=31829
• https://link.springer.com/article/10.1186/s12915-026-02691-8

Rhobot-Screen：ロドプシン変異体の機能スクリーニングのための統合型ロボットプラットフォーム Robots-Screen: an integrated robotic platform for functional screening of rhodopsin variants

Takashi Nagata,Masae Konno,Daisuke R. Hashimoto & Keiichi Inoue
BMC Biology  Published:06 August 2026
DOI:https://doi.org/10.1186/s12915-026-02691-8

Abstract

Background
Rhodopsins are photoreceptive membrane proteins widely used as optogenetic tools in basic research and medical applications, and extensive mutational studies have been performed to improve or modify their functional properties. Recently, in the broader field of protein engineering, data-driven strategies based on machine learning have attracted increasing attention, as they enable efficient exploration of vast mutational spaces with a reduced number of experiments. Such approaches require large, consistent datasets that link predefined mutations to quantitative functional properties, which necessitates systematic construction and characterization of targeted variants rather than random mutagenesis. For rhodopsins, however, generating these datasets remains challenging due to operator-dependent, non-integrated workflows that are difficult to scale and standardize.

Results
To address this limitation, we developed an automated screening platform termed Rhobot-Screen, based on a robotic liquid-handling workstation, which integrates multiple experimental steps into a standardized workflow with reduced dependence on operator-specific expertise. This platform performs site-directed mutagenesis, plasmid preparation, protein expression in bacterial and mammalian cultured cells, and functional characterization in a 96-well format through automated liquid-handling operations. For spectroscopic characterization, we established a 96-well plate-based hydroxylamine bleaching assay that determines absorption maximum wavelengths without protein purification. As a demonstration of the platform, we comprehensively mutated three established color-tuning residues in Gloeobacter rhodopsin, generating 57 single-point variants. Using Rhobot-Screen, the absorption maxima of 46 variants were successfully determined. The resulting dataset revealed position-dependent relationships between spectral shifts and amino acid physicochemical properties, with clear correlations between absorption wavelength and side-chain volume at positions 129 and 256, but not at position 226. The platform was further extended to mammalian cell-based assays for functional characterization of animal rhodopsins.

Conclusions
Rhobot-Screen provides an integrated workflow in which all liquid-handling steps for systematic construction and spectroscopic characterization of rhodopsin variants are automated in a 96-well plate format under standardized conditions. By automating and standardizing multiple operator-dependent steps, the platform provides a reproducible framework for acquiring quantitative sequence–function data from predefined rhodopsin variants. This framework should support both mechanistic studies of rhodopsins and future data-driven engineering of rhodopsin functions.