The lack of annotated sequence homologues in viral proteins spurs the need for innovative strategies to predict protein functions, particularly given the rapid evolution of viruses.
To systematically predict viral protein functions, a new database of predicted structures from 67,715 proteins helps identify functional relationships through structural similarity rather than just sequence.
Viral proteins often exhibit high divergence, indicating that traditional sequence-based similarity searches may fail when amino acid identity is low, necessitating structural approaches.
Our work highlights the crucial role of structural similarity searches in providing insights into the taxonomic diversity and potential functions of previously unannotated viral proteins.
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