Journal Article

DZNE-2026-01069

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Creating bottom-up RNA transfer vehicles from synthetic protein assemblies.

Schuhmacher, M. K. ; Gruber, C. ; Lang, C. M. R. ; Ruijpers, R. M. W. ; Mazneykova, L. ; Beinsteiner, B. ; Krus, A. ; Tremmel, B. ; Reinhardt, F. ; Kadletz, K. ; Ma, Z. ; Casalta, L. ; Cambra Bort, J. M. ; Otify, D. Y. ; Balken, I. ; Hetzel, L. ; Merl-Pham, J. ; Dorn, T. ; Luchner, M. ; Bauersachs, L. ; Ganea, K. ; Wieser, N. ; Emrich, A. ; Yağmur, E. ; Rager, K. ; Sagindykova, G. ; Armbrust, N. ; Geilenkeuser, J. ; Westmeyer, G. G. ; Becirovic, E. ; Biel, M. ; Istvanffy, R. ; Vogt Weisenhorn, D. M. ; Truong, D. J. ; Theis, F. J. ; Ebert, G. ; Moretti, A. ; Ertürk, A. ; Bähr, A. ; Kupatt, C. ; Jasnin, M. ; Klymiuk, N. ; Giesert, F. ; Wurst, W. (Last author)DZNE*

2026
Nature Publ. Group London [u.a.]

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Please use a persistent id in citations: doi:10.1038/s41586-026-10952-3

Abstract: Evolution guides biological systems to populate ecological niches, with viruses among the most successful examples of this principle. Viruses evolved over billions of years to efficiently transfer genetic information. Although viruses are highly diverse, most have converged towards remarkable similarity in the size and shape of their capsids1,2. By contrast, generative models for protein design enable the creation of protein architectures that are absent from nature3-5. Here we investigate whether protein assemblies designed by artificial intelligence can be functionalized to construct nucleic acid transport vehicles that are independent of evolutionary trajectories. By combining natural protein domains with synthetic protein assemblies, we create more than 100 bottom-up RNA transfer vehicles with unique sizes and shapes. These vehicles surpass the RNA transfer efficiency of widely used delivery vehicles by several orders of magnitude. In addition, we demonstrate that their tropism can be programmed by incorporation of computationally designed peptide binders and use them to deliver therapeutically relevant cargo RNAs into a wide range of cellular models. We show the in vivo biodistribution of one of these vehicles in a mouse at near-single-cell resolution, confirm its safety, and use it to perform a gene-editing treatment strategy for Duchenne muscular dystrophy in patient-derived cells and a pig. Our work demonstrates how proteins created by generative artificial intelligence can be harnessed for the rational engineering of RNA transport systems with the desired properties by overcoming the limitations of natural protein diversity.

Keyword(s): Animals (MeSH) ; Mice (MeSH) ; Humans (MeSH) ; Swine (MeSH) ; RNA: genetics (MeSH) ; RNA: metabolism (MeSH) ; RNA: administration & dosage (MeSH) ; Generative Artificial Intelligence (MeSH) ; Gene Transfer Techniques (MeSH) ; Proteins: chemistry (MeSH) ; Proteins: metabolism (MeSH) ; Peptides: chemistry (MeSH) ; Peptides: metabolism (MeSH) ; Tissue Distribution (MeSH) ; Protein Engineering (MeSH) ; Protein Domains (MeSH) ; RNA ; Proteins ; Peptides

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Contributing Institute(s):

1. Genome Engineering (AG Wurst)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)

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; BIOSIS Previews ; Biological Abstracts ; Chemical Reactions ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; DEAL Nature ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 60 ; Index Chemicus ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record

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