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Nature
Bioinformatics AI
Maren Kirstin Schuhmacher, Christoph Gruber, Christopher M. R. Lang, Ricardo M. W. Ruijpers, Lyupka Mazneykova, Brice Beinsteiner, Ariane Krus, Barbara Tremmel, Friederike Reinhardt, Karoline Kadletz, Zhe Ma, Lucie Casalta, Josep Miquel Cambra Bort, Dina Y. Otify, Iolo Balken, Leon Hetzel, Juliane Merl-Pham, Tatjana Dorn, Marina Luchner, Lea Bauersachs, Karin Ganea, Natascha Wieser, Alexander Emrich, Emirhan Yağmur, Katrin Rager, Gauhar Sagindykova, Niklas Armbrust, Julian Geilenkeuser, Gil G. Westmeyer, Elvir Becirovic, Martin Biel, Rouzanna Istvanffy, Daniela M. Vogt Weisenhorn, Dong-Jiunn Jeffery Truong, Fabian J. Theis, Gregor Ebert, Alessandra Moretti, Ali Ertürk, Andrea Bähr, Christian Kupatt, Marion Jasnin, Nikolai Klymiuk, Florian Giesert, Wolfgang Wurst
2026-09-02
TL;DR - Researchers used artificial intelligence to design synthetic virus-like protein assemblies that deliver RNA into cells more effectively than naturally occurring counterparts. The work suggests bottom-up protein design can bypass evolutionary constraints that limit viral delivery systems.
- Introduces synthetic protein architectures engineered as RNA transfer vehicles.
- AI-designed assemblies showed superior cellular RNA delivery relative to natural analogues.
- The bottom-up approach explores designs unavailable through natural viral evolution.
- The findings could inform more effective RNA delivery platforms.
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