Publications
Structure (London, England : 1993)Nov 2025 DOI:
10.1016/j.str.2025.10.010

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

Wagner Egea, Paula; Delhommel, Florent; Mustafa, Ghulam; Leiss-Maier, Florian; Klimper, Lisa; Badmann, Thomas; Heider, Anna; Wille, Idoia; Groll, Michael; Sattler, Michael; Zeymer, Cathleen
Product Used
Genes
Abstract
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.
Product Used
Genes

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