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.

Protein structural changes induced by external perturbations or internal cues can profoundly influence protein activity and thus modulate cellular physiology. A number of biophysical approaches are available to probe protein structural changes, but these are not applicable to a whole proteome in a biological extract. Limited proteolysis-coupled mass spectrometry (LiP-MS) is a recently developed proteomics approach that enables the identification of protein structural changes directly in their complex biological context on a proteome-wide scale. After perturbations of interest, proteome extracts are subjected to a double-protease digestion step with a nonspecific protease applied under native conditions, followed by complete digestion with the sequence-specific protease trypsin under denaturing conditions. This sequential treatment generates structure-specific peptides amenable to bottom-up MS analysis. Next, a proteomics workflow involving shotgun or targeted MS and label-free quantification is applied to measure structure-dependent proteolytic patterns directly in the proteome extract. Possible applications of LiP-MS include discovery of perturbation-induced protein structural alterations, identification of drug targets, detection of disease-associated protein structural states, and analysis of protein aggregates directly in biological samples. The approach also enables identification of the specific protein regions involved in the structural transition or affected by the binding event. Sample preparation takes approximately 2 d, followed by one to several days of MS and data analysis time, depending on the number of samples analyzed. Scientists with basic biochemistry training can implement the sample preparation steps. MS measurement and data analysis require a background in proteomics.

Condensin complexes have central roles in the three-dimensional organization of chromosomes during cell divisions, but how they interact with chromatin to promote chromosome segregation is largely unknown. Previous work has suggested that condensin, in addition to encircling chromatin fibers topologically within the ring-shaped structure formed by its SMC and kleisin subunits, contacts DNA directly. Here we describe the discovery of a binding domain for double-stranded DNA formed by the two HEAT-repeat subunits of the Saccharomyces cerevisiae condensin complex. From detailed mapping data of the interfaces between the HEAT-repeat and kleisin subunits, we generated condensin complexes that lack one of the HEAT-repeat subunits and consequently fail to associate with chromosomes in yeast and human cells. The finding that DNA binding by condensin's HEAT-repeat subunits stimulates the SMC ATPase activity suggests a multistep mechanism for the loading of condensin onto chromosomes.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.