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Date publication

août 2026

Journal

Angewandte Chemie (International ed. in English)

Auteurs

Membres identifiés du Cancéropôle Est :
Dr KIEFFER Bruno , Dr NOMINE Yves


Tous les auteurs :
Bouzayene AB, Sai M, Jouin A, Nominé Y, Torbeev V, Kieffer B

Résumé

Recently, the application of deep learning to structural data deposited in the Protein Data Bank has enabled the reliable and accurate prediction of 3D folded structures of proteins from their sequences. However, this approach is not applicable to highly dynamic proteins, where multiple structures interconvert. Furthermore, the mechanistic details of protein folding and unfolding remain challenging to study. Herein, we present a set of data highlighting these complexities. By chemical incorporation of stereoisomeric 4-fluoroproline residues at selected sites in the sequence of a folded, multi-conformational protein ("molten-globule"), we were able to modify its structural properties that propagated to highly distinct functional features, such as modulation of ligand binding affinities or misfolding and aggregation into amyloids. Application of NMR methods, notably F NMR spectroscopy, provided detailed molecular insights into the observed phenomena. This study illustrates how subtle residue-localized conformational bias can affect the overall protein conformational dynamics influencing protein-protein interactions that are important for cellular functions and related to diseases.

Mots clés

19F NMR spectroscopy, fluorine, fluoroproline, molten‐globule, protein amyloids, protein folding

Référence

Angew Chem Int Ed Engl. 2026 08 22;:e4052471