次世代創薬技術「標的たんぱく質分解」を加速 ~DCAFたんぱく質群の相互作用ネットワークを解明~

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2026-04-03 愛媛大学

愛媛大学プロテオサイエンスセンターらの研究チームは、標的タンパク質分解(TPD)創薬の鍵となるDCAFタンパク質群の機能と相互作用ネットワークを網羅的に解明した。独自の近接タンパク質標識技術AirIDと大規模プロテオミクス解析により、約60種のDCAF周辺のタンパク質群を同定し、インタラクトームを体系化。さらに、生化学・細胞解析を統合して分解活性の高いDCAFを選定する枠組みを構築した。これにより、標的タンパク質分解薬の設計を加速する基盤が確立され、次世代創薬の発展に大きく貢献する成果となった。

次世代創薬技術「標的たんぱく質分解」を加速 ~DCAFたんぱく質群の相互作用ネットワークを解明~

<関連情報>

標的タンパク質分解におけるDDB1およびCUL4関連因子の優先順位付けのための相互作用ネットワークに基づくフレームワーク An interactome-based framework for DDB1- and CUL4-associated factor prioritization in targeted protein degradation

Satoshi Yamanaka ∙ Koya Nagaoka ∙ Yuki Shoya ∙ … ∙ Atsushi Hijikata ∙ Hidetaka Kosako ∙ Tatsuya Sawasaki
Molecular Cell  Published: April 2, 2026
DOI:https://doi.org/10.1016/j.molcel.2026.03.004

Highlights

  • A proximity interactome resource for the human DCAF family
  • Systematic assessment of CRL4 formation and DCAF autodegradation
  • Functional annotation and substrate inference from DCAF interactomes
  • Prioritization of DCAFs for targeted protein degradation

Summary

The DDB1- and CUL4-associated factor (DCAF) family functions as substrate receptors within Cullin4-really interesting new gene (RING) ubiquitin ligases (CRL4s), facilitating proteasomal degradation of targeted substrates. Although CRL4-based targeted protein degradation (TPD) has emerged as a promising strategy to modulate undruggable proteins, the complex formation, substrates, and functional properties of many DCAFs remain poorly defined. In this study, using proximity biotinylation-based interactome analysis in human HEK293T cells, we systematically annotated interactors and functional associations of individual DCAFs. Furthermore, we identified substrates of the model DCAFs COP1 and DCAF3 using proximity biotinylation coupled with multi-omics approaches. By combining biochemical and cell-based analyses, we establish CRL4 complex formation and DCAF autodegradation as experimentally tractable proxy indicators to evaluate DCAF degradation activity and propose a set of high-activity DCAFs. These datasets establish a resource for functional characterization of DCAFs and provide a framework for their prioritization in TPD.

生物化学工学
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