De novo design of multiple-geometry forming protein scaffolds

Presenter: Anna Backeberg (Anna.Backeberg@campus.lmu.de)

Authors:
Anna Backeberg; Elizaveta Maltseva; Ho Yeung Chim; Prof. Dr. Alena Khmelinskaia

Polyhedral protein assemblies are highly functional materials with applications in areas such as drug-delivery or vaccine development. While the de novo design of static protein nanoparticles with one specific target geometry is well established and exceedingly accurate, this study aims at the design of rigid protein monomers that can simultaneously self-assemble into different geometries. Just like having a versatile ingredient in a kitchen pantry, our goal is to establish "off-the-shelf" programmability of protein assemblies with a set of universal building blocks, facilitating the targeted design of new and functional protein nanoparticles. Combining current Artificial Intelligence (AI)-based design methods with physics-based approaches, we established a pipeline for the design of such multiple-geometry forming protein scaffolds. Our pipeline tackles this challenge on two levels: (i) the generation of backbones matching multiple geometries; and (ii) the design of degenerate sequences compatible with equal formation of the target architectures. Our approach paves the way for a new generation of highly versatile protein building blocks.

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