9QNL

BAD pS118 phosphopeptide binding to 14-3-3sigma

9QNL の概要

• エントリーDOI: 10.2210/pdb9qnl/pdb
• 分子名称: 14-3-3 protein sigma, Bcl2-associated agonist of cell death, CALCIUM ION, ... (5 entities in total)
• 機能のキーワード: 14-3-3 protein-protein interactions, peptide binding protein
• 由来する生物種: Homo sapiens (human); 詳細
• タンパク質・核酸の鎖数: 2
• 化学式量合計: 28128.16

構造登録者

Pennings, M.A.M. (登録日: 2025-03-25, 公開日: 2025-09-03, 最終更新日: 2025-09-24)

主引用文献

van Weesep, L.,Ozcelik, R.,Pennings, M.,Criscuolo, E.,Ottmann, C.,Brunsveld, L.,Grisoni, F.
Identifying 14-3-3 interactome binding sites with deep learning.
Digit Discov, 4:2602-2614, 2025

PubMed Abstract: Protein-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 ( ranging from 1.6 ± 0.1 μM to 70 ± 5 μM). 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 (, 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/.

PubMed: 40837623
DOI: 10.1039/d5dd00132c

実験手法

X-RAY DIFFRACTION (1.3 Å)