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Wagner Egea, P.* ; Delhommel, F. ; Mustafa, G.* ; Leiss-Maier, F.* ; Klimper, L.* ; Badmann, T.* ; Heider, A.* ; Wille, I.* ; Groll, M.* ; Sattler, M. ; Zeymer, C.*

Modular protein scaffold architecture and AI-guided sequence optimization facilitate de novo metalloenzyme engineering.

Structure, DOI: 10.1016/j.str.2025.10.010 (2025)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Incorporating metal cofactors into computationally designed protein scaffolds provides a versatile route to novel protein functions, including the potential for new-to-nature enzyme catalysis. However, a major challenge in protein design is to understand how the scaffold architecture influences conformational dynamics. Here, we characterized structure and dynamics of a modular de novo scaffold with flexible inter-domain linkers. Three rationally engineered variants with different metal specificity were studied by combining X-ray crystallography, NMR spectroscopy, and molecular dynamics simulations. The lanthanide-binding variant was initially trapped in an inactive conformational state, which impaired efficient metal coordination and cerium-dependent photocatalytic activity. Stabilization of the active conformation by AI-guided sequence optimization using ProteinMPNN led to accelerated lanthanide binding and a 10-fold increase in kcat/Km for a photoenzymatic model reaction. Our results suggest that modular scaffold architectures provide an attractive starting point for de novo metalloenzyme engineering and that ProteinMPNN-based sequence redesign can stabilize desired conformational states.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Md Simulations ; Nmr Spectroscopy ; Proteinmpnn ; X-ray Crystallography ; Conformational Dynamics ; De Novo Protein Scaffold ; Enzyme Engineering ; Metalloprotein ; Photoenzyme ; Protein Design
ISSN (print) / ISBN 0969-2126
e-ISSN 1878-4186
Journal Structure
Publisher Cell Press
Reviewing status Peer reviewed