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

Junrui Zhang; Alexandra Vancea; Stefan T. Arold

doi:10.1016/j.tplants.2022.05.012

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

Junrui Zhang, Alexandra Vancea, Stefan T. Arold

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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.

Original language	English (US)
Journal	Trends in plant science
DOIs	https://doi.org/10.1016/j.tplants.2022.05.012
State	Published - Jun 16 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-06-22
Acknowledged KAUST grant number(s): URF/1/4039-01
Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline fund and award URF/1/4039-01 from the Office of Sponsored Research (OSR). We thank U.S. Farook Hameed for his contributions to the initial stages of this project, and we thank Ł. Jaremko, S. Al-Babili, H. Hirt, B. Zahodnik-Huntington, and A. Sandholu for their constructive comments on the manuscript.

ASJC Scopus subject areas

Plant Science

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10.1016/j.tplants.2022.05.012

https://repository.kaust.edu.sa/bitstream/10754/679229/1/1-s2.0-S1360138522001522-main%20%281%29.pdf

Cite this

@article{6582448e74a141fea6027e62acaa9216,

title = "Targeting plant UBX proteins: AI-enhanced lessons from distant cousins",

abstract = "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.",

author = "Junrui Zhang and Alexandra Vancea and Arold, {Stefan T.}",

note = "KAUST Repository Item: Exported on 2022-06-22 Acknowledged KAUST grant number(s): URF/1/4039-01 Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline fund and award URF/1/4039-01 from the Office of Sponsored Research (OSR). We thank U.S. Farook Hameed for his contributions to the initial stages of this project, and we thank {\L}. Jaremko, S. Al-Babili, H. Hirt, B. Zahodnik-Huntington, and A. Sandholu for their constructive comments on the manuscript.",

year = "2022",

month = jun,

day = "16",

doi = "10.1016/j.tplants.2022.05.012",

language = "English (US)",

journal = "Trends in plant science",

issn = "1360-1385",

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T1 - Targeting plant UBX proteins: AI-enhanced lessons from distant cousins

AU - Zhang, Junrui

AU - Vancea, Alexandra

AU - Arold, Stefan T.

N1 - KAUST Repository Item: Exported on 2022-06-22 Acknowledged KAUST grant number(s): URF/1/4039-01 Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline fund and award URF/1/4039-01 from the Office of Sponsored Research (OSR). We thank U.S. Farook Hameed for his contributions to the initial stages of this project, and we thank Ł. Jaremko, S. Al-Babili, H. Hirt, B. Zahodnik-Huntington, and A. Sandholu for their constructive comments on the manuscript.

PY - 2022/6/16

Y1 - 2022/6/16

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/679229

UR - https://linkinghub.elsevier.com/retrieve/pii/S1360138522001522

U2 - 10.1016/j.tplants.2022.05.012

DO - 10.1016/j.tplants.2022.05.012

M3 - Article

C2 - 35718708

SN - 1360-1385

JO - Trends in plant science

JF - Trends in plant science

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

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