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

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Official URL:https://doi.org/10.1038/msb.2012.13
PubMed:View item in PubMed
Creators Name:Yamada, T. and Waller, A.S. and Raes, J. and Zelezniak, A. and Perchat, N. and Perret, A. and Salanoubat, M. and Patil, K.R. and Weissenbach, J. and Bork, P.
Journal Title:Molecular Systems Biology
Journal Abbreviation:Mol Syst Biol
Volume:8
Page Range:581
Date:8 May 2012
Keywords:Genomics, Metabolic Pathways, Metagenomics, Neighbourhood Information, Orphan Enzymes
Abstract: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.
ISSN:1744-4292
Publisher:Nature Publishing Group (U.S.A.)
Item Type:Article

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