Interactions of Bacterial Toxin CNF1 and Host JAK1/2 Driven by Liquid-Liquid Phase Separation Enhance Macrophage Polarization

Sun, Xuan; Yang, Jianming; Deng, Xiaotie; Wei, Yuting; Wang, Changying; Guo, Yaxiu; Yang, Huan; Liu, Yang; Miao, Chunhui; Lv, Junqiang; Xiao, Yuxiu; Zhang, Hong; Yao, Zhi; Wang, Quan

doi:10.1128/mbio.01147-22

Cited by 4 publications

(2 citation statements)

References 55 publications

(61 reference statements)

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“…Nevertheless, the PS of certain ID-PSPs predominantly hinges on modular domains rather than IDRs. A case in point is JAK1, a recognized ID-PSP, which can form a PS-driven CNF1-JAK1-JAK2 complex via its SH2 domain 44 . With PSPire's default features, the PS score of JAK1 is a mere 0.64.…”

Section: Discussionmentioning

confidence: 99%

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

Hou,

Hu,

et al. 2024

Nat Commun

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The burgeoning comprehension of protein phase separation (PS) has ushered in a wealth of bioinformatics tools for the prediction of phase-separating proteins (PSPs). These tools often skew towards PSPs with a high content of intrinsically disordered regions (IDRs), thus frequently undervaluing potential PSPs without IDRs. Nonetheless, PS is not only steered by IDRs but also by the structured modular domains and interactions that aren’t necessarily reflected in amino acid sequences. In this work, we introduce PSPire, a machine learning predictor that incorporates both residue-level and structure-level features for the precise prediction of PSPs. Compared to current PSP predictors, PSPire shows a notable improvement in identifying PSPs without IDRs, which underscores the crucial role of non-IDR, structure-based characteristics in multivalent interactions throughout the PS process. Additionally, our biological validation experiments substantiate the predictive capacity of PSPire, with 9 out of 11 chosen candidate PSPs confirmed to form condensates within cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

Hou,

Hu,

et al. 2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the PS of certain ID-PSPs predominantly hinges on modular domains rather than IDRs. A case in point is JAK1, a recognized ID-PSP, which can form a PS-driven CNF1-JAK1-JAK2 complex via its SH2 domain 40 . With PSPire's default features, the PS score of JAK1 is a mere 0.43.…”

Section: Discussionmentioning

confidence: 99%

PSPire: a machine learning predictor for high-performance screening of phase-separating proteins without intrinsically disordered regions

Hou,

Hu,

et al. 2023

Preprint

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The burgeoning comprehension of protein phase separation (PS) has ushered in a wealth of bioinformatics tools for the prediction of phase-separating proteins (PSPs). These tools often skew towards PSPs with a high content of intrinsically disordered regions (IDRs), thus frequently undervaluing potential PSPs without IDRs. Nonetheless, PS is not only steered by IDRs but also by the structured modular domains and interactions that aren't necessarily reflected in amino acid sequences. In this work, we introduce PSPire, a unique machine learning predictor designed to incorporate both residue-level and structure-level features for the precise prediction of PSPs. Compared to current PSP predictors, PSPire shows a notable improvement in identifying PSPs without IDRs, which underscores the crucial role of non-IDR, structure-based characteristics in multivalent interactions throughout the PS process. Additionally, our biological validation experiments substantiate the predictive capacity of PSPire, with 6 out of the 8 chosen candidate PSPs confirmed to form condensates within cells. This highlights the considerable potential of structure-based models in the accurate prediction and comprehensive understanding of protein PS.

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Resveratrol-loaded sulfated Hericium erinaceus β-glucan-chitosan nanoparticles: Preparation, characterization and synergistic anti-inflammatory effects

Zhang,

Ge,

et al. 2024

Carbohydrate Polymers

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Interactions of Bacterial Toxin CNF1 and Host JAK1/2 Driven by Liquid-Liquid Phase Separation Enhance Macrophage Polarization

Abstract: CNF1 is a key toxin secreted by UPEC, which induces inflammation during UPEC infections. CNF1 is well known to activate Rho GTPases to disturb host cell signaling pathways.

Cited by 4 publications

References 55 publications

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

PSPire: a machine learning predictor for high-performance screening of phase-separating proteins without intrinsically disordered regions

Resveratrol-loaded sulfated Hericium erinaceus β-glucan-chitosan nanoparticles: Preparation, characterization and synergistic anti-inflammatory effects

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