Quantitative global studies of reactomes and metabolomes using a vectorial representation of reactions and chemical compounds

BACKGROUND: Global studies of the protein repertories of organisms are providing important information on the characteristics of the protein space. Many of these studies entail classification of the protein repertory on the basis of structure and/or sequence similarities. The situation is different for metabolism. Because there is no good way of measuring similarities between chemical reactions, there is a barrier to the development of global classifications of "metabolic space" and subsequent studies comparable to those done for protein sequences and structures. RESULTS: In this work, we propose a vectorial representation of chemical reactions, which allows them to be compared and classified. In this representation, chemical compounds, reactions and pathways may be represented in the same vectorial space. We show that the representation of chemical compounds reflects their physicochemical properties and can be used for predictive purposes. We use the vectorial representations of reactions to perform a global classification of the reactome of the model organism E. coli. CONCLUSIONS: We show that this unsupervised clustering results in groups of enzymes more coherent in biological terms than equivalent groupings obtained from the EC hierarchy. This hierarchical clustering produces an optimal set of 21 groups which we analyzed for their biological meaning.