
Recent proteomic efforts have created an extensive inventory of the human nucleolar proteome. However, approximately 30% of the identified proteins lack functional annotation. We present an approach of assigning function to uncharacterized nucleolar proteins by data integration coupled to a machine-learning method. By assembling protein complexes, we present a first draft of the human ribosome biogenesis pathway encompassing 74 proteins and hereby assign function to 49 previously uncharacterized proteins. Moreover, the functional diversity of the nucleolus is underlined by the identification of a number of protein complexes with functions beyond ribosome biogenesis. Finally, we were able to obtain experimental evidence of nucleolar localization of 11 proteins, which were predicted by our platform to be associates of nucleolar complexes. We believe other biological organelles or systems could be "wired" in a similar fashion, integrating different types of data with high-throughput proteomics, followed by a detailed biological analysis and experimental validation.
Proteomics, Databases, Factual, Proteome, Genetic Variation, Reproducibility of Results, Cell Biology, Models, Biological, Artificial Intelligence, Software Design, Humans, Molecular Biology, Ribosomes, Cell Nucleolus
Proteomics, Databases, Factual, Proteome, Genetic Variation, Reproducibility of Results, Cell Biology, Models, Biological, Artificial Intelligence, Software Design, Humans, Molecular Biology, Ribosomes, Cell Nucleolus
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