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Targeting plant UBX proteins: AI-enhanced lessons from distant cousins

期刊

TRENDS IN PLANT SCIENCE
卷 27, 期 11, 页码 1099-1108

出版社

CELL PRESS
DOI: 10.1016/j.tplants.2022.05.012

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  1. King Abdullah University of Science and Technology (KAUST) through the baseline fund [URF/1/4039-01]
  2. Off ice of Sponsored Research (OSR)

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This article discusses the lack of research on UBX proteins in plants, and suggests that the AlphaFold algorithm can provide highly accurate protein structure predictions, offering a new approach for research. The combination of cross-kingdom comparison with AF analysis is proposed to inspire and guide experimental research on plant UBX proteins.
Across all eukaryotic kingdoms, ubiquitin regulatory X (UBX) domain-containing adaptor proteins control the segregase cell division control protein 48 (CDC48), and thereby also control cellular proteostasis and adaptation. The structures and biological roles of UBX proteins in animals and fungi have garnered considerable attention. However, their counterparts in plants remain markedly understudied. Since 2021, the artificial intelligence (AI)-based algorithm AlphaFold has provided predictions of protein structural features that can be highly accurate. Predictions of the proteomes of all major model organisms are now freely accessible to the entire research community through user-friendly web interfaces. We propose that the combination of cross-kingdom comparison with AF analysis produces a wealth of testable hypotheses to inspire and guide experimental research on plant UBX domain-containing (PUX) proteins.

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