C-P.42: AI-driven structural modeling reveals hidden functional and evolutionary relationships in divergent dsRNA viruses
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Keywords
virus, protein, structure, capsid, modeling
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Double-stranded RNA (dsRNA) viruses harbor numerous proteins whose functions remain poorly understood due to extreme sequence divergence that defeats conventional annotation methods. Here, we demonstrate that AI-driven structural modeling systematically overcomes this barrier, enabling functional assignment across highly divergent viral proteomes.
Using AlphaFold3 combined with Foldseek structural similarity searches, we predicted three-dimensional structures of proteins from representative Reovirales and Ghabrivirales members. Structure-based analyses identified multiple previously uncharacterized virion components—inner capsid, outer capsid, and capping enzymes (turret proteins)—providing a near-complete structural map despite negligible sequence similarity to known homologs. Critically, structural modeling enabled the first structure-based phylogenetic reconstruction of capsid proteins, circumventing sequence-based limitations.
Remarkably, structural modeling of Micromonas pusilla reovirus (MpRV) revealed that its outer capsid protein adopts a fold closely related to the birnavirus capsid, despite these viruses being deeply divergent. This discovery suggests ancient horizontal gene transfer and demonstrates that capsid modules can be exchanged between distantly related dsRNA viruses.
Our results demonstrate that AI-guided structural modeling not only expands functional annotation of viral proteomes but provides a powerful framework for exploring deep evolutionary relationships among highly divergent proteins. This approach reveals hidden functional and evolutionary connections within the virosphere that remain inaccessible to traditional genomic methods, opening new avenues for understanding viral evolution and diversity.
Co-authors: Edouard De Castro, David Moi, Gerardo Tauriello, Paul Thomas, Jelle Matthijnssens, Houssam Attoui, Fauziah Mohd Jaafar
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