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...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 January 2012
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| 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/ 
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| Verfasserangaben: | Takuji Yamada, Alison S Waller, Jeroen Raes, Aleksej Zelezniak, Nadia Perchat, Alain Perret, Marcel Salanoubat, Kiran R Patil, Jean Weissenbach and Peer Bork |
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| 245 | 1 | 0 | |a Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours |c Takuji Yamada, Alison S Waller, Jeroen Raes, Aleksej Zelezniak, Nadia Perchat, Alain Perret, Marcel Salanoubat, Kiran R Patil, Jean Weissenbach and Peer Bork |
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| 520 | |a 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. | ||
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