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

Area of Science:

• Protein engineering and design
• Biophysical characterization
• Enzyme catalysis

Background:

• Computationally designed protein scaffolds offer new functions, including catalysis.
• Understanding scaffold dynamics is crucial for protein design.
• Metalloenzymes are key targets for novel biocatalyst development.

Purpose of the Study:

• To characterize the structure and dynamics of a de novo protein scaffold.
• To investigate the influence of scaffold architecture on conformational dynamics.
• To engineer metalloenzymes with enhanced catalytic activity.

Main Methods:

• X-ray crystallography and NMR spectroscopy for structural and dynamic analysis.
• Molecular dynamics simulations to probe conformational landscapes.
• AI-guided protein sequence optimization using ProteinMPNN.

Main Results:

• A modular de novo scaffold with flexible linkers was characterized.
• A lanthanide-binding variant was initially trapped in an inactive conformation.
• AI-guided redesign stabilized the active conformation, increasing catalytic efficiency 10-fold.

Conclusions:

• Modular de novo scaffolds are promising for metalloenzyme engineering.
• ProteinMPNN can effectively stabilize desired protein conformations.
• Scaffold dynamics significantly impact metalloenzyme function and design.