Controlling metal-carbonate phase, form, and function through de novo protein design

Area of Science:

• Biomaterials Science
• Protein Engineering
• Nanotechnology

Background:

• Biomineralization uses proteins to create inorganic materials, but harnessing this in engineered systems is challenging due to protein complexity.
• Native biomineralization proteins are often disordered, heterogeneous, or insoluble, limiting their application in synthetic materials.

Purpose of the Study:

• To design de novo protein interfaces capable of templating inorganic crystal formation.
• To engineer protein architectures for selective polymorph control (e.g., aragonite).
• To explore protein-templated synthesis of novel inorganic materials like cobalt carbonate for catalytic applications.

Main Methods:

• Utilized RFdiffusion2, a deep learning model, to design protein scaffolds and interfaces.
• Assembled designed proteins into two-dimensional arrays and soluble cage structures.
• Investigated templating of calcite, aragonite, and cobalt carbonate nanocrystals.

Main Results:

• Demonstrated de novo designed protein interfaces templating calcite nanocrystals.
• Achieved selective aragonite formation using fine-tuned protein architectures.
• Successfully templated cobalt carbonate formation using protein designs, with cage assemblies yielding homogenous nanocrystals.
• Developed protein-cage cobalt carbonate hybrid materials that function as electrocatalysts for alkaline water splitting.

Conclusions:

• Deep learning-based protein design can create functional protein-mineral composites.
• Engineered protein interfaces offer precise control over inorganic crystal nucleation, orientation, and polymorph.
• These methods open new avenues for designing advanced hybrid materials with tailored properties and functions.