Identifying metabolic enzymes with multiple types of association evidence.
BACKGROUND
Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes.


RESULTS
We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases.


CONCLUSIONS
We illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities.
Department of Genetics, New Research Building (NRB) Room 238, 77 Ave, Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. peter.kharchenko@post.harvard.edu
Peter
P
Kharchenko
Lifeng
L
Chen
Yoav
Y
Freund
Dennis
D
Vitkup
George M
GM
Church
D004734:Energy Metabolism / Q000235:genetics; D004798:Enzymes / Q000235:genetics*; D029968:Escherichia coli Proteins / Q000235:genetics*; D016680:Genome, Bacterial; D010802:Phylogeny; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
D004798:Enzymes; D029968:Escherichia coli Proteins; D029701:Saccharomyces cerevisiae Proteins