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ETH Zurich Researchers Build Large-Scale Protein-Metabolite Interactome


NEW YORK (GenomeWeb) – A team led by researchers at the Swiss Federal Institute of Technology (ETH) Zurich has generated a map of protein-metabolite interactions in Escherichia coli.

Detailed in a paper published this month in Cell, the map is one of the largest such resources developed to date and provides insights into the roles of protein-metabolite interactions in cellular function and signaling, said Paola Picotti, professor of biochemistry at ETH Zurich and senior author on the study.

Picotti and her colleagues generated the interaction map using the limited proteolysis (LiP) technology developed in her lab, which uses mass spec-based analyses of protein proteolysis patterns to assess structural changes on a proteome-wide scale.

The LiP approach combines digestion with a broadly specific protease followed by a standard mass spec-based proteomics workflow to assess structural changes on a proteome-wide scale. The basic notion underlying the approach is that in the initial digestion step, the protease will cleave proteins only at sites that are accessible, left exposed by whatever structural conformation it happens to be in at the time of analysis. When a sample is treated with, in this case, a metabolite of interest, the proteins that bind to this molecule will undergo a structural change, and this will be reflected in changes where the protease is able to cleave the protein.

By following this initial digestion step with standard trypsin digestion and mass spec analysis, researchers can compare the peptides generated in treated and untreated samples and, based on changes in the peptides produced, determine which proteins had their structures altered by the treatment in question.

Picotti and her colleagues have used the method in the past to investigate binding between proteins and nutrients in yeast and to assess the thermal stability of proteins on a proteome-wide scale. Last year her lab licensed the technology to targeted proteomics firm Biognosys, which offers the method for applications including studies of drug binding and drug target deconvolution.

In the Cell study, the researchers used the approach to screen 20 metabolites against the E. coli proteome to identify interactions. While large-scale protein-protein interaction work has become increasingly common, Picotti and her co-authors noted that protein-metabolite interaction studies have been more limited due to challenges including the transient nature of the interactions and the low affinity of many such interactions.

Nonetheless, such interactions are known to be important to cellular function and, as the authors wrote, the large number of metabolites present in a cell suggest that "there could be millions of functionally relevant metabolite-protein binding events."

Picotti noted that "most metabolite-proteins interactions we know have been identified via hypothesis-driven experiments involving incubation of metabolites with specific purified enzymes and in vitro enzyme activity assays."

"These assays are tedious and low-throughput, rely on the availability of activity assays for the target proteins, and preclude the systematic identification of unexpected interactions," she said.

More recently, she said, researchers have developed approaches to large-scale characterization of protein-metabolite interactions, using, for instance, chemically modified metabolites that covalently react with proteins upon binding, or using mass spec to track the accumulation or depletion of collections of metabolites when incubated with proteins of interest.

However, Picotti said, many of these methods have focused on a particular class of metabolite (lipids, for instance), or a particular type of protein-metabolite reaction.

Using the LiP approach, the researchers aimed to generate a large-scale interactome covering a range of metabolite types. They selected the 20 metabolites analyzed in the study due either to their "broad biological relevance" or the fact that they sat at key nodes of the E. coli metabolic network, Picotti said.

"Additionally, we chose metabolites that covered a broad range of chemical properties, such as hydrophobicity, charge, and molecular weight, to evaluate whether the approach was really unbiased with respect to nature of the metabolites," she noted. The researchers chose E. coli as their model organism due to the fact that its metabolic network has been extensively characterized, which allowed them to compare their findings to a large set of known interactions.

The analysis identified a total of 1,678 protein-metabolite interactions and 7,345 putative binding sites and found that around 25 percent of the proteome measured interacted with at least one of the 20 metabolites investigated with all known cellular processes being affected by protein-metabolite interactions.

To assess the accuracy of their data, the researchers compared their findings to known protein-metabolite interactions in the BRENDA database, which catalogs such interactions. That analysis suggested a false discovery rate of around 5 percent, Picotti said. They also performed the LiP analysis with the drug cerulenin, which is known to interact only with the protein Fas2. "Of the more than 2,500 proteins identified, only Fas2 had an altered proteolytic pattern in treated relative to untreated extracts," the authors wrote, noting that this further suggested the method's low false-positive rate.

Looking at specific interactions of biological interest, the researchers were able to show, among other things, that fructose-1,6-bisphosphate (FBP) interacts allosterically with the kinase/phosphatse PpsR to regulate PEP synthetase (PpsA) and alter the direction of the glycolytic flux in E. coli, one of the first examples "of allosteric regulation of a protein kinase in E. coli," the authors wrote.

Picotti said she envisioned other researchers similarly validating and following up on the biological implications of different interactions reported in the study.

"Possible scopes include the functional characterization of uncharacterized proteins, the identification of novel enzymes, the identification of allosteric and catalytic sites for the purpose of drug design and structure-activity relationships or synthetic biology, the characterization of signaling functions of metabolites, and the identification and characterization of novel protein complexes and protein aggregates induced by alterations in metabolite levels," she said.

She added that she and her colleagues are now planning to expand the analysis to additional endogenous metabolites as well as to a variety of small molecule drugs.