9qni
From Proteopedia
NOTCH4 phosphopeptide binding to 14-3-3sigma
Structural highlights
Function1433S_HUMAN Adapter protein implicated in the regulation of a large spectrum of both general and specialized signaling pathways. Binds to a large number of partners, usually by recognition of a phosphoserine or phosphothreonine motif. Binding generally results in the modulation of the activity of the binding partner. When bound to KRT17, regulates protein synthesis and epithelial cell growth by stimulating Akt/mTOR pathway (By similarity). p53-regulated inhibitor of G2/M progression. Publication Abstract from PubMedProtein-protein interactions are at the heart of biological processes. Understanding how proteins interact is key for deciphering their roles in health and disease, and for therapeutic interventions. However, identifying protein interaction sites, especially for intrinsically disordered proteins, is challenging. Here, we developed a deep learning framework to predict potential protein binding sites to 14-3-3 - a 'central hub' protein holding a key role in cellular signaling networks. After systematically testing multiple deep learning approaches to predict sequence binding to 14-3-3, we developed an ensemble model that achieved a 75% balanced accuracy on external sequences. Our approach was applied prospectively to identify putative binding sites across medically relevant proteins (ranging from highly structured to intrinsically disordered) for a total of approximately 300 sequences. The top eight predicted peptide sequences were experimentally validated in the wet-lab, and binding to 14-3-3 was confirmed for five out of eight sequences (K (d) ranging from 1.6 +/- 0.1 muM to 70 +/- 5 muM). The relevance of our results was further confirmed by X-ray crystallography and molecular dynamics simulations. These sequences represent potential new binding sites within the 14-3-3 interactome (e.g., relating to Alzheimer's disease as the binding to tau is not the new part), and provide opportunities to investigate their functional relevance. Our results highlight the ability of deep learning to capture intricate patterns underlying protein-protein interactions, even for challenging cases like intrinsically disordered proteins. To further the understanding and targeting of 14-3-3/protein interactions, our model was provided as a freely accessible web resource at the following URL: https://14-3-3-bindsite.streamlit.app/. Identifying 14-3-3 interactome binding sites with deep learning.,van Weesep L, Ozcelik R, Pennings M, Criscuolo E, Ottmann C, Brunsveld L, Grisoni F Digit Discov. 2025 Aug 8. doi: 10.1039/d5dd00132c. PMID:40837623[1] From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine. References
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