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This research leverages artificial intelligence to design synthetic protein assemblies that outperform existing viral vectors for RNA delivery. By utilizing non-natural symmetries and AI-driven architecture, the study creates programmable vehicles capable of efficient in vivo transport without relying on evolutionary constraints. The findings highlight a significant advancement in gene-editing therapeutics through the fusion of computational design and bottom-up nanotechnology.
Bottom-up protein assemblies form efficient RNA transport systems
Researchers created over 100 synthetic protein assemblies, a “bottom-up” approach, to efficiently deliver RNA—mimicking viral strategies for genetic transfer.
Researchers have built functional RNA transfer systems from scratch using synthetic protein assemblies, mimicking the efficiency viruses developed over billions of years. The team created more than 100 bottom-up RNA vehicles with unique shapes and sizes, surpassing the RNA transfer efficiency of widely used delivery vehicles by several orders of magnitude. This approach combines natural protein domains with artificial intelligence-designed protein architectures, offering a new way to construct nucleic acid transport independent of evolutionary constraints. The work demonstrates the in vivo biodistribution of one of these vehicles in cleared mice and their potential for gene-editing treatment strategies in patient-derived cells and a pig. Artificial intelligence designed…
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