Our Cell Paper on Protein-Metabolite Interactions Recommended in F1000, twice

Drs. Kuroda and Papp rated the paper as 'external page exceptional'.

Dr. Kuroda:
"Although this paper was published as a resource in Cell, it provides not only a comprehensive map of protein-metabolite interaction but also valuable biological insights into the prevalence and mechanisms of enzyme promiscuity, and quantitative parameters of metabolite binding on a proteome-wide scale. One of the key points of this paper is that they compared their data from a LiP analysis, a proteome analysis for the identification of protein-metabolite interaction, with previous knowledge, including known protein-metabolite interactions, genetic information, and crystal structures of proteins. In particular, they calculated Euclidean distances between binding sites and catalytic sites by using information of crystal structures of proteins, and found that enzyme promiscuity is a wide-spread property that mostly derives from binding clefts able to host diverse natural compounds. The authors also showed that their LiP analysis can be useful to derive the quantitative parameters of metabolite binding in the cellular environment. By calculating Kd values for ATP-protein interactions, they discovered new low-affinity ATP binding proteins, suggesting that ATP is exclusively an allosteric effectors for these proteins.

The concept of the proteome analysis and the validation experiments were well-designed and very sophisticated. They provided a great example for how to design proteome experiments and verify the novel findings from large-scale data properly."

Dr. Papp:
"Interactions between metabolites and proteins underlie various cellular processes, yet our knowledge of the in vivo metabolite-protein interaction network remains rudimentary. This study takes a substantial step towards filling this gap in our knowledge. In brief, it proposes a workflow to directly map protein-metabolite interactions in their native cellular environment by detecting ligand-induced structural alterations on a proteome-wide scale. Applying the workflow to identifying the interaction partners of 20 metabolites in the E. coli proteome revealed novel regulatory interactions, enzyme-substrate relationships and instances of metabolite-induced assembly and disassembly of protein complexes. Strikingly, more than 80% of the reported interactions were novel and about one quarter of the measured proteome interacted with at least one of the 20 tested metabolites, indicating that the metabolite-protein interaction network is vast and largely uncharted. Finally, the data hint at a new ‘design principle’ of regulation: proteins expressed in a condition-specific manner are depleted in metabolic interactions, suggesting that gene expression and metabolite-mediated regulation might be mutually exclusive strategies to modulate gene activity."