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Cell. 2018 Jan 11;172(1-2):358-372.e23. doi: 10.1016/j.cell.2017.12.006. Epub 2018 Jan 4.

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication.

Author information

1
Institute of Biochemistry, Department of Biology, ETH Zurich, Zurich, Switzerland; Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
2
Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
3
Institute of Biochemistry, Department of Biology, ETH Zurich, Zurich, Switzerland; Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland. Electronic address: picotti@imsb.biol.ethz.ch.

Abstract

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.

KEYWORDS:

allostery; binding site; chemoproteomics; limited proteolysis; mass spectrometry; metabolite-protein interactions; metabolite-protein interactome; metabolites; structural proteomics; target deconvolution

PMID:
29307493
DOI:
10.1016/j.cell.2017.12.006
[Indexed for MEDLINE]
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