Interaction between phosphofructokinase and aldolase from Saccharomyces cerevisiae studied by aqueous two-phase partitioning

A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling

Protein-protein interactions are operative at almost every level of cell structure and function as, for example, formation of sub-cellular organelles, packaging of chromatin, muscle contraction, signal transduction, and regulation of gene expression. Public databases of reported protein-protein interactions comprise hundreds of thousands interactions, and this number is steadily growing. Elucidating the implications of protein-protein interactions for the regulation of the underlying cellular or extra-cellular reaction network remains a great challenge for computational biochemistry. In this work, we have undertaken a systematic and comprehensive computational analysis of reported enzyme-enzyme interactions in the metabolic networks of the model organisms Escherichia coli and Saccharomyces cerevisiae. We grouped all enzyme pairs according to the topological distance that the catalyzed reactions have in the metabolic network and performed a statistical analysis of reported enzyme-enzyme interactions within these groups. We found a higher frequency of reported enzyme-enzyme interactions within the group of enzymes catalyzing reactions that are adjacent in the network, i.e. sharing at least one metabolite. As some of these interacting enzymes have already been implicated in metabolic channeling our analysis may provide a useful screening for candidates of this phenomenon. To check for a possible regulatory role of interactions between enzymes catalyzing non-neighboring reactions, we determined potentially regulatory enzymes using connectivity in the network and absolute change of Gibbs free energy. Indeed a higher portion of reported interactions pertain to such potentially regulatory enzymes.

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.

Conclusion

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.

Sucrose synthase isoforms in cultured tobacco cells

The plant enzyme sucrose synthase (SuSy; EC 2.4.1.13) catalyzes the reversible conversion of sucrose and UDP into UDP-glucose (UDP-Glc) and fructose. The enzyme exists in different isoforms and is both located in the cytosol, membrane-bound and associated to the actin cytoskeleton. We here investigate sucrose synthase from tobacco (Nicotiana tabacum L.) BY-2 heterotrophic cell suspensions. Two different isoforms of sucrose synthase SuSy1 and SuSy2, could be purified from cytosolic extracts of these cells using a combination of poly(ethylene glycol) (PEG) precipitation, gel filtration, ion-exchange chromatography and affinity chromatography. They were clearly distinct, both with regard to the binding to the ion-exchange column and with regard to their kinetic and regulatory properties. SuSy1, the more abundant species, showed lower V(max) and K(m) for sucrose and UDP compared to the less abundant SuSy2. The activity of SuSy2 in the breakdown direction was stimulated by 60% by actin, in contrast to that of SuSy1, which showed a 17% inhibition. An indication of interaction between SuSy1 and actin was obtained by partitioning in aqueous Dextran-PEG two-phase systems. Furthermore, fructose 2,6-bisphosphate (F26BP) at micromolar concentrations stimulated SuSy2 in the presence of actin while SuSy1 was strongly inhibited by fructose. Possible roles of these two isoforms in the sucrose turnover in BY-2 cells are discussed.