Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

Publication: Research - peer-reviewJournal article – Annual report year: 2012

  • Author: Yamada, Takuji

    European Molecular Biology Laboratory, Heidelberg, Germany

  • Author: Waller, Alison S.

    European Molecular Biology Laboratory, Heidelberg, Germany

  • Author: Raes, Jeroen

    Molecular and Cellular Interactions Department, VIB, Belgium

  • Author: Zelezniak, Aleksej

    Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark

  • Author: Perchat, Nadia

    Commissariat a` l’Energie Atomique, Evry, France

  • Author: Perret, Alain

    Univ Evry Val dEssonne, Evry, France

  • Author: Salanoubat, Marcel

    Commissariat a` l’Energie Atomique, Evry, France

  • Author: Patil, Kiran R.

    European Molecular Biology Laboratory, Heidelberg, Germany

  • Author: Weissenbach, Jean

    Commissariat a` l’Energie Atomique, Evry, France

  • Author: Bork, Peer

    European Molecular Biology Laboratory, Heidelberg, Germany

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Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta) genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta) genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta) genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence-function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction. Molecular Systems Biology 8: 581; published online 8 May 2012; doi:10.1038/msb.2012.13
Original languageEnglish
JournalMolecular Systems Biology
Publication date2012
Volume8
Number of pages12
ISSN1744-4292
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 5

Keywords

  • BIOCHEMISTRY, METABOLIC NETWORK, PHYLOGENETIC PROFILES, SCALE RECONSTRUCTION, PROTEIN FAMILIES, GENES, ENVIRONMENT, CELL
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