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

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...

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Hauptverfasser: Yamada, Takuji (VerfasserIn) , Patil, Kiran Raosaheb (VerfasserIn) , Bork, Peer (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 1 January 2012
In: Molecular systems biology
Year: 2012, Jahrgang: 8
ISSN:1744-4292
DOI:10.1038/msb.2012.13
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1038/msb.2012.13
Verlag, Volltext: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3377989/
Volltext
Verfasserangaben:Takuji Yamada, Alison S Waller, Jeroen Raes, Aleksej Zelezniak, Nadia Perchat, Alain Perret, Marcel Salanoubat, Kiran R Patil, Jean Weissenbach and Peer Bork
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 23.10.2018
Published online 2012 May 8
Beschreibung:Online Resource
ISSN:1744-4292
DOI:10.1038/msb.2012.13