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Cross-phyla protein annotation by structural prediction and alignment

Cited 4 time in Web of Science Cited 6 time in Scopus

Ruperti, Fabian; Papadopoulos, Nikolaos; Musser, Jacob M.; Mirdita, Milot; Steinegger, Martin; Arendt, Detlev

Issue Date
BioMed Central
Genome Biology, Vol.24 No.1, p. 113
Background: Protein annotation is a major goal in molecular biology, yet experimen-tally determined knowledge is typically limited to a few model organisms. In non -model species, the sequence-based prediction of gene orthology can be used to infer protein identity; however, this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similar -ity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences.Results: We propose a workflow of openly available tools for the functional annota-tion of proteins via structural similarity (MorF: MorphologFinder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in >90% cases and annotates an additional 50% of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF, and Ephrin signaling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls.Conclusions: Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homolo-gous proteins over long evolutionary distances. We anticipate this will be a powerful approach that boosts discovery in numerous-omics datasets, especially for non-model organisms.
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data


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