Abstract 2833 Expanding the β-Arrestin's Interactome: Convolutional Prediction and BRET Validation of the “arreSTick” Motif

The binding and function of β-arrestins in G protein-coupled receptors (GPCRs) are governed by specific phosphorylation motifs, although the precise arrangement of phosphorylated residues underlying a stable association remains elusive. We used a one-dimensional (1D) sequence convolution model, trained on GPCRs with characterized β-arrestin-binding properties, to identify a distinct amino acid pattern - termed “arreSTick” - that mediates stable interactions with β-arrestins. Remarkably, we also detect this arreSTick motif in numerous non-receptor proteins.

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Journal
Journal of Biological Chemistry
Published
2025-05-01
DOI
https://doi.org/10.1016/j.jbc.2025.110132
Primary Topic
Computational Drug Discovery Methods
Type
article
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Abstract 2833 Expanding the β-Arrestin's Interactome: Convolutional Prediction and BRET Validation of the “arreSTick” Motif

László Hunyady, Eszter Soltész‐Katona, Orsolya Kovács, András Dávid Tóth et al.
Journal of Biological Chemistry
Computational Drug Discovery Methods
article

Abstract 2833 Expanding the β-Arrestin's Interactome: Convolutional Prediction and BRET Validation of the “arreSTick” Motif

László Hunyady, Eszter Soltész‐Katona, Orsolya Kovács, András Dávid Tóth, Gábor Turu
article en

Abstract

The binding and function of β-arrestins in G protein-coupled receptors (GPCRs) are governed by specific phosphorylation motifs, although the precise arrangement of phosphorylated residues underlying a stable association remains elusive. We used a one-dimensional (1D) sequence convolution model, trained on GPCRs with characterized β-arrestin-binding properties, to identify a distinct amino acid pattern - termed “arreSTick” - that mediates stable interactions with β-arrestins. Remarkably, we also detect this arreSTick motif in numerous non-receptor proteins.

Journal of Biological ChemistryVol. 301(5)
HUN-REN Research Centre for Natural Sciences (HU)
Openalex Percentile: Top 85%
Computational Drug Discovery Methods
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