4.8 Article

Illuminating structural proteins in viral dark matter with metaproteomics

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1525139113

Keywords

viruses; marine; proteins

Funding

  1. University Information Technology Services Research Computing Group
  2. Arizona Research Laboratories Biotechnology Computing
  3. Ford Foundation Postdoctoral Fellowship
  4. Gordon and Betty Moore Foundation [GBMF2631, GBMF3790]
  5. UA Ecosystem Genomics Institute through the UA Technology and Research Initiative Fund
  6. Water, Environmental and Energy Solutions Initiative

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Viruses are ecologically important, yet environmental virology is limited by dominance of unannotated genomic sequences representing taxonomic and functional viral dark matter. Although recent analytical advances are rapidly improving taxonomic annotations, identifying functional darkmatter remains problematic. Here, we apply paired metaproteomics and dsDNA-targeted metagenomics to identify 1,875 virion-associated proteins from the ocean. Over one-half of these proteins were newly functionally annotated and represent abundant and widespread viral metagenome-derived protein clusters (PCs). One primarily unannotated PC dominated the dataset, but structural modeling and genomic context identified this PC as a previously unidentified capsid protein from multiple uncultivated tailed virus families. Furthermore, four of the five most abundant PCs in the metaproteome represent capsid proteins containing the HK97-like protein fold previously found in many viruses that infect all three domains of life. The dominance of these proteins within our dataset, as well as their global distribution throughout the world's oceans and seas, supports prior hypotheses that this HK97-like protein fold is the most abundant biological structure on Earth. Together, these culture-independent analyses improve virion-associated protein annotations, facilitate the investigation of proteins within natural viral communities, and offer a high-throughput means of illuminating functional viral dark matter.

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