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• W2969990740 abstract "Article23 August 2019Open Access Transparent process Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli Maren Diether Maren Diether Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland Search for more papers by this author Yaroslav Nikolaev Corresponding Author Yaroslav Nikolaev [email protected]il.com orcid.org/0000-0002-1479-7474 Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland Search for more papers by this author Frédéric HT Allain Frédéric HT Allain orcid.org/0000-0002-2131-6237 Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland Search for more papers by this author Uwe Sauer Corresponding Author Uwe Sauer [email protected] orcid.org/0000-0002-5923-0770 Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Maren Diether Maren Diether Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland Search for more papers by this author Yaroslav Nikolaev Corresponding Author Yaroslav Nikolaev [email protected] orcid.org/0000-0002-1479-7474 Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland Search for more papers by this author Frédéric HT Allain Frédéric HT Allain orcid.org/0000-0002-2131-6237 Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland Search for more papers by this author Uwe Sauer Corresponding Author Uwe Sauer [email protected] orcid.org/0000-0002-5923-0770 Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Author Information Maren Diether1,2,‡, Yaroslav Nikolaev *,3,‡, Frédéric HT Allain3 and Uwe Sauer *,1 1Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland 2Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland 3Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland ‡These authors contributed equally to this work *Corresponding author. Tel: +41 44 633 0720; E-mail: [email protected] *Corresponding author. Tel: +41 44 633 36 72; E-mail: [email protected] Molecular Systems Biology (2019)15:e9008https://doi.org/10.15252/msb.20199008 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Metabolite binding to proteins regulates nearly all cellular processes, but our knowledge of these interactions originates primarily from empirical in vitro studies. Here, we report the first systematic study of interactions between water-soluble proteins and polar metabolites in an entire biological subnetwork. To test the depth of our current knowledge, we chose to investigate the well-characterized Escherichia coli central metabolism. Using ligand-detected NMR, we assayed 29 enzymes towards binding events with 55 intracellular metabolites. Focusing on high-confidence interactions at a false-positive rate of 5%, we detected 98 interactions, among which purine nucleotides accounted for one-third, while 50% of all metabolites did not interact with any enzyme. In contrast, only five enzymes did not exhibit any metabolite binding and some interacted with up to 11 metabolites. About 40% of the interacting metabolites were predicted to be allosteric effectors based on low chemical similarity to their target's reactants. For five of the eight tested interactions, in vitro assays confirmed novel regulatory functions, including ATP and GTP inhibition of the first pentose phosphate pathway enzyme. With 76 new candidate regulatory interactions that have not been reported previously, we essentially doubled the number of known interactions, indicating that the presently available information about protein–metabolite interactions may only be the tip of the iceberg. Synopsis To probe the depth of our current knowledge on protein-metabolite interactions, the interactions between 29 enzymes from Escherichia coli central metabolism and 55 intracellular metabolites were systematically mapped using ligand-detected NMR. 98 interactions are identified, of which only 22 were previously known. Purine nucleotides account for a third of all interactions. Only five enzymes did not exhibit any metabolite binding. Five out of eight tested newly predicted interactions are functionally validated in vitro. Introduction Robustness and adaptability of cells emerge from the dynamic regulatory interplay of diverse biomolecules. Interactions with metabolites are of particular importance as information input to many regulatory proteins such as transcription factors and kinases (Li et al, 2010; Hahn & Young, 2011; Kochanowski et al, 2013, 2017; Chubukov et al, 2014; Wegner et al, 2015), and also into the metabolic reaction network itself (Gerosa & Sauer, 2011; Ljungdahl & Daignan-Fornier, 2012). An extensive network of protein–metabolite interactions enables coordination of the various activities in each cell. Our knowledge about these functional interactions is largely based on accumulated biochemical evidence from studies on individual proteins that typically yield a few new interactions by testing compounds on the basis of existing knowledge (Jones & Fink, 1982; Cherry et al, 2012; Keseler et al, 2017; Placzek et al, 2017). While great strides have been made toward systematic mapping of physical protein–protein and protein–DNA interaction networks (Cai & Huang, 2012; Syafrizayanti et al, 2014; Myers et al, 2015; Smits & Vermeulen, 2016), systematic mapping of protein–metabolite interactions is lagging behind. One challenge is the generally low affinity (mM range) of protein–metabolite interactions (Reznik et al, 2017) and their fleeting nature. Some large-scale discovery approaches reported hundreds of novel protein–metabolite interactions with nearly no overlap between the studies (Gallego et al, 2010; Savitski et al, 2014; Geer & Fitzgerald, 2016; Piazza et al, 2018), providing a glimpse on the size of the interaction space. Given this vast space, it might be more desirable to systematically map protein–metabolite interactions within a defined subnetwork. While this has been achieved for non-polar metabolites (lipids; Gallego et al, 2010; Li et al, 2010), these methods are not transferable to polar metabolites. Alternative methods suitable for polar metabolites suffer from other problems. For example, most available high-throughput methods do not detect metabolite binding itself, but instead measure indirect interaction consequences on the protein (Savitski et al, 2014; Diether & Sauer, 2017; Piazza et al, 2018), thus missing interactions that elicit only weak effects or are condition-dependent. The remaining methods are hampered by chemical limitations, requiring functionalization (Hulce et al, 2013; Höglinger et al, 2017) or radiolabeling (Roelofs et al, 2011) of metabolites, or high protein concentrations (Orsak et al, 2012). To address these limitations, we recently showcased a nuclear magnetic resonance (NMR) spectroscopy approach that permits direct detection of interactions between any set of water-soluble proteins and metabolites (Nikolaev et al, 2016). In a proof-of-concept study with four proteins and 33 metabolites, we recovered all known and detected new interactions, some of which proved to be functional modulators. This approach thus opened a venue to exhaustively search a pre-selected space of proteins and metabolites for potential interactions. To elucidate whether our current knowledge on protein–metabolite interactions is nearing completion, at least for well-characterized subnetworks, we chose to investigate Escherichia coli central carbon metabolism that has been thoroughly investigated over decades. At present, about 100 regulatory (metabolite changes enzyme activity) and 130 catalytic (metabolite is substrate or product) interactions involving the 35 major isoenzymes of central metabolism are reported in the EcoCyc database (Keseler et al, 2017). We systematically generated ligand-detected NMR interaction profiles of 29 purified enzymes from E. coli central metabolism with 55 selected metabolites, between which 72 interactions were already known. Here, we focused our analysis only on high-confidence NMR interactions by choosing a false-positive rate cutoff of 5%, which yielded a dataset encompassing 30% of the 72 known interactions. At the above cutoff, we detected 98 interactions between all tested enzymes and metabolites, including 22 known interactions and 76 interactions that had not been reported previously, and validated five of the newly predicted interactions with in vitro enzyme assays. Among the most striking observations was the highly promiscuous binding of GTP and other purine nucleotides (ATP, AMP, and cAMP), and the lack of interactions with metabolites from amino acid biosynthesis. Results Ligand-detected T1rho NMR assay for a biological subnetwork To probe the depth of our current knowledge on protein–metabolite interactions in E. coli central metabolism, we selected all monomeric and homo-oligomeric enzymes. Hetero-oligomeric and membrane-bound proteins were excluded because of expected difficulties with purification and in vitro reconstitution. For reactions catalyzed by more than one enzyme, the major isoenzyme was chosen. The resulting 35 selected central metabolic enzymes were purified by His-tag affinity purification from clones of the ASKA library (Kitagawa et al, 2005). For six of the enzymes (AceB, GltA, Ppc, PpsA, PrpC, and SthA), we achieved only low yields under high-throughput purification conditions, reducing the final set to 29 enzymes (Dataset EV1). For systematic testing of putative regulators, we selected 59 metabolites from several pathways, including amino acids, nucleotides, cofactors, and central metabolism, several with known regulatory functions (Dataset EV1). From the initial set of 59, four metabolites (coenzyme A, acetyl-coenzyme A, erythrose-4-phosphate, and 5-aminoimidazole-4-carboxamide ribonucleotide) were not tested due to their instability in our buffer and temperature conditions. To detect protein–metabolite interactions, purified proteins were mixed with a subset of metabolites and NMR spectra were recorded. A single one-dimensional (1D) NMR spectrum can resolve few dozens of individual metabolite signals. Due to differences in the NMR properties of small and large molecules, metabolite signals broaden (exhibit reduced intensity) upon protein binding. We exploit this change in signal intensity to detect metabolite–protein interactions. High-throughput NMR analysis relies on availability of isolated compound-specific peaks in the combined NMR spectrum of specific metabolite mixtures. NMR signals start overlapping as the complexity of the metabolite mixture increases, thus limiting the number of metabolites that can be confidently assayed within one mixture. To split the selected metabolites into a minimum number of groups (mixes), the NMRmix tool (Stark et al, 2016) was employed. Three metabolites were explicitly assigned to different mixes to avoid potential enzymatic reactions, yielding four mixes each containing 12, 14, 14, and 15 metabolites in such a way that each metabolite had at least one well-separated signal in the 1D 1H NMR spectrum of the mix (Fig 1A and B, Appendix Figs S1–S4, Dataset EV1). Figure 1. NMR spectra of metabolite mixes 1D 1H-NMR spectra of a metabolite mix and the individual metabolites contained therein. Identification of single compound peaks from 1D 1H-NMR spectra of a metabolite mix. Compound detection is exemplified by showing sections of the 1D 1H-NMR spectra of ATP, IMP, proline (PRO), malate (MAL), shikimate (SKM), and methionine (MET). Download figure Download PowerPoint To automate data acquisition, we developed a set of Python-based TopSpin libraries for sample changing, basic experiment setup, and spectral processing. To increase the measurement throughput compared to the pilot study (Nikolaev et al, 2016), we only measured 1D hydrogen-detected (1H) T1rho relaxation spectra and omitted water-ligand observed via gradient spectroscopy (WaterLOGSY), as the former appeared more robust although slightly less sensitive (Nikolaev et al, 2016). T1rho experiments detect signal decay rates (relaxation rates) in metabolites upon binding to a large macromolecule target. If an interaction between metabolite and target exists, the NMR signals of the metabolite decay (disappear) faster. To minimize contributions of metabolite instability to the signals in the final spectra, T1rho spectra with short relaxation delay (10 ms) were measured as two identical repetitions, before and after the T1rho experiment with long relaxation delay (200 ms), and summed up during processing. Furthermore, the difference spectrum of the two short-delay T1rho spectra provided a quality filter to detect metabolites showing increased chemical instability in the presence of specific proteins. Among other sources of instability, metabolite degradation could be the result of enzymatic conversion, although this is not likely to be a major confounding factor given that the protein–metabolite mixture was incubated for several hours prior to NMR recording. However, differentiating the various sources of metabolite instability is not feasible given our current setup. To identify protein–metabolite interactions, purified proteins were mixed individually with the four metabolite mixes in excess (15 μM of protein monomer and 200 μM of each metabolite) in a buffer optimized for physiological salt concentrations (Fig 2). NMR measurements with pure enzymes and four pure metabolite mixes were used as references for quantification. Figure 2. Workflow of ligand-detected NMR approachTwenty-nine His-tagged enzymes of Escherichia coli central metabolism were mixed individually with four metabolite mixes, and T1rho 1D 1H NMR spectra were recorded for every protein–mix combination. All possible interactions between enzymes and metabolites were quantified using the relaxation factor and are displayed in a protein–metabolite interaction map. Download figure Download PowerPoint Interactions between enzymes and metabolites were analyzed using a fully automated custom-built analysis pipeline, initially by computing both the fractional signal intensity (Nikolaev et al, 2016) and relaxation factor (ΔRF; Gossert & Jahnke, 2016). For the final analyses, the ∆RF metric was selected as it gave slightly better results for the given set of proteins and metabolites (assessed by comparing the “area under the curve” for both metrics; Appendix Fig S5). The ∆RF metric quantifies the difference in the signal relaxation rate of the metabolite alone and in the presence of the protein. If an interaction between metabolite and target exists, the difference ∆RF value increases above zero. Under the NMR setup employed, interactions with dissociation constants (KDs) in the μM-to-mM range were generally detectable, with μM KDs producing the strongest attenuation of metabolite signals in the presence of the protein, i.e., highest ΔRF (Gossert & Jahnke, 2016; Nikolaev et al, 2016). The final analysis used here was restricted to peaks with a signal-to-noise ratio greater than two in the final T1rho NMR spectra of the protein–metabolite mixtures (after subtracting pure protein and metabolite signals). Systematic map of protein–metabolite interactions in Escherichia coli central metabolism To determine the ΔRF values that represent biologically relevant interactions, we investigated the overlap of the interactions detected by our approach with the known interactions reported in the EcoCyc database (Keseler et al, 2017). Among the 29 enzymes and 55 metabolites, 43 catalytic (metabolite is substrate or product) and 40 regulatory (metabolite changes enzyme activity) interactions are known. Since some regulatory metabolites are also substrates or products of their target enzyme, we thus have 72 previously known interactions. Using different ΔRF cutoffs, we calculated the false-positive and true-positive rates of recovering the known interactions, obtaining a receiver-operator characteristics curve (Appendix Fig S5, Materials and Methods: Analysis of the recovery of known interactions). For conservative discovery—to select only high-confidence interactions—we chose a false-positive rate of 5%, corresponding to a ΔRF cutoff of 0.1805 that was applied in all subsequent analyses. By applying the 5% false-positive rate cutoff, we detected 98 distinct protein–metabolite interactions in our dataset. We recovered 22 of the 72 previously reported interactions (30%), thus achieving a twofold higher true-positive rate than a recent MS-based study (Piazza et al, 2018; 16% recovery of known interactions). Half of the 55 tested metabolites did not exhibit any interaction and the other half interacted on average with 3.5 proteins each (Fig 3A, Dataset EV2). Remarkably, only four of the 20 proteinogenic amino acids (Asn, His, Leu, and Trp) exhibited interactions with the tested enzymes in our assays. In contrast, the ten tested nucleotide-related metabolites interacted with 36 enzymes, most prominently GTP with 13 and AMP with nine proteins. Overall, the here-detected interactions were equally distributed between regulatory and catalytic interactions (Fig 3B), indicating that our NMR approach is not biased by interaction type. Likewise, there does not appear to be a bias through chemical structures as the interactions spanned a wide range of metabolites, as expected from NMR T1rho relaxation experiments (Hajduk et al, 1997). Only five tested proteins did not interact with any metabolite, and the remaining 24 proteins interacted with about four metabolites on average (Appendix Fig S6). The most highly connected enzymes were fructose-bisphosphate aldolase (FbaA) and malate dehydrogenase (MaeB) with eleven interacting metabolites each. Projection of the newly detected interactions onto the network of central metabolism revealed significantly fewer interactions in the tricarboxylic acid (TCA) cycle (P-value 0.004, two-tailed t-test assuming unequal variance; on average 1 per TCA cycle enzyme vs. 3.2 for any other enzyme, excluding purely catalytic interactions; Fig 3C). We observed no significant differences between enzymes that catalyze reversible or irreversible reactions (P-value 0.22, two-tailed t-test assuming unequal variance). The number of interacting metabolites per protein did not correlate with protein size (Appendix Fig S7), suggesting that experimental results are not biased by the molecular weight of the targets. Sequence-level analysis (Gasteiger et al, 2005) showed no correlation of protein aliphatic content and hydropathicity with the number of significant hits observed (R2 = 0.0009 and 0.0007; Appendix Fig S8). Similarly, metabolite hydrophobicity did not correlate with the number of detected interactions (R2 = 0.0844; Appendix Fig S8). In total, we discovered 76 new protein–metabolite interactions. Figure 3. Overview of enzyme–metabolite interactions detected with NMR Number of interactions detected per metabolite. Metabolites are grouped according to biological pathways; the height of the bar indicates the total number of interactions. Recovery of interactions reported in EcoCyc database. Metabolites are grouped according to biological pathways, the total height of the bar indicates the number of known interactions that could have been detected, and the height of the colored bar indicates the number of actually recovered interactions. Recovery of catalytic (metabolite is substrate or product of the enzyme) and regulatory interactions is shown. Distribution of known regulatory and newly predicted interactions in Escherichia coli central metabolism. Enzymes included in this study are depicted in bold. Gray and white circles indicate how many known interactions were recovered or not recovered, respectively. Newly detected interactions are depicted using circles that are color-coded according to the origin of the respective metabolite. Data information: Abbreviations of proteins and metabolites are explained in Dataset EV1. Download figure Download PowerPoint Chemical similarities distinguish between potential allosteric and competitive interactions In general, all identified enzyme–metabolite interactions represent potential catalytic and/or regulatory interactions. For catalytic interactions, binding must occur at the enzymes’ active site. For regulatory interactions, binding can occur either at the active site (competitive regulation) or at an alternative binding site (allosteric regulation). Such allosteric regulators often have stronger regulatory potential, as their effect generally does not depend much on the concentrations of the enzymes’ native substrates [e.g., in the case of non-competitive inhibition (Purich, 2010)]. To differentiate binding modes, we investigated the chemical similarity of metabolites with substrates and products of the tested enzymes. We assume that competitive binders will have a higher similarity to substrates or products than allosteric binders. To investigate the underlying distribution of chemical similarities in our biological subnetwork, we calculated the maximum global chemical similarity between all possible regulator–substrate/product pairs using Simcomp2 (Hattori et al, 2010; Fig 4). Simcomp2 identifies the maximal common substructure of two chemical structures using a graph-based method (Hattori et al, 2010). The resulting distribution ranges from zero (no similarity) to one (perfect similarity; regulator is identical to substrate or product) with a mean of 0.34. Computing this metric for the 98 detected interactions results in a distribution with a mean of 0.59 (Fig 4, Dataset EV2), implying that most NMR-detected interactors are similar (> 0.5) to the natural substrates/products of their target. In turn, this indicates that many of the detected interactions are due to binding of the metabolite to the enzyme active site. Nevertheless, 40% of the NMR-detected interactors have a low chemical similarity (< 0.5) to substrates/products of the target enzyme, suggesting an allosteric binding mode (Fig 4B). Since they are more distant from central metabolism, amino acid and nucleotide interactors expectedly dominate among the putative allosteric binders (Fig 4A, black rectangles). Overall, we predict that 36 out of the 76 newly discovered interactions are allosteric while the remaining interactions have a competitive binding mode (Appendix Fig S9). Figure 4. A map of the enzyme–metabolite interactions in Escherichia coli central metabolism Interactions between 29 central enzymes (in rows) and 55 metabolites (in columns), grouped according to metabolic pathways (n = 2, measurement replicates from the same sample). The relaxation factor of every interaction is indicated in green. Previously reported catalytic and regulatory interactions are denoted with “C” and “R”, respectively; black and red letters indicate interactions that were detected and not detected, respectively. Black rectangles indicate potential allosteric interactions (maximum chemical similarity between the interactor and substrates/products of the target is lower than 0.5). Histogram showing the relative occurrences of maximum chemical similarity scores in the protein–metabolite interaction map. Gray bars indicate the distribution of scores considering all possible enzyme–metabolite pairs, and green bars indicate the distribution scores for interactions with ΔRF > 0.1805 (false-positive rate < 5%). Data information: Abbreviations of proteins and metabolites are explained in Dataset EV1. Download figure Download PowerPoint Validation of newly predicted protein–metabolite interactions with in vitro enzyme assays Given the established reliability of NMR in detecting molecular interactions (Dalvit et al, 2006; Pellecchia et al, 2008), we decided to directly validate the newly predicted interactions at the functional level. To validate the functionality of the predictions, we chose eight enzyme–metabolite pairs based on their biological relevance and chemical similarity and tested the enzyme activities by in vitro assays in the presence and absence of the predicted regulator (summarized in Appendix Fig S10, Dataset EV3). First, the NADP+-dependent glucose-6-phosphate dehydrogenase (Zwf) was selected for being at the branch point between glycolysis and pentose phosphate pathway. Our NMR-based approach predicted interactions of Zwf with the nucleotides ATP, GTP, and IMP, with chemical similarities to the natural substrates/products of 0.60, 0.55, and 0.44, respectively. These interactions were not reported in EcoCyc or BRENDA, except for ATP inhibition of Zwf in the absence of stabilizing Mg2+ ions (Santimoy Banerjee & Fraenkel, 1972). In our plate-reader-based in vitro enzyme assay, ATP and GTP indeed inhibited Zwf even at physiological concentrations of MgCl2. IMP did not affect Zwf activity, similar to the negative control AMP (Fig 5A). Second, we investigated the predicted regulation of phosphate acetyltransferase (Pta) by l-tryptophan and phenylpyruvate. These interactions were selected due to the very low similarity between the regulators and the natural substrates and products of the enzyme (0.06 and 0.05), indicating possible allosteric interactions. Mass spectrometry-based in vitro assays showed a small, but insignificant concentration-dependent inhibitory effect of phenylpyruvate and no inhibitory effect of l-tryptophan on phosphate acetyltransferase activity (Fig 5B). Third, the glycolytic fructose-bisphosphate aldolase class II (FbaA) has no reported metabolite regulators in EcoCyc but was found to interact with eleven metabolites. We selected 3-phosphoglycerate, ATP, and phosphoenolpyruvate for validation, as well as hypoxanthine and IMP as negative controls. Mass spectrometry-based in vitro assays showed that 3-phosphoglycerate, ATP, and phosphoenolpyruvate had an activating effect on the enzyme, whereas hypoxanthine and IMP had no measurable impact (Fig 5C). Previously, 3-phosphoglycerate has been reported to inhibit fructose-bisphosphate aldolase (Szwergold et al, 1995) and the activating effect of PEP was observed with the mechanistically distinct fructose-bisphosphate aldolase class I (Baldwin & Perham, 1978). Overall, we could validate the regulatory function of five out of eight newly observed enzyme–metabolite interactions. Figure 5. In vitro enzyme assays Relative activity of NADP+-dependent glucose-6-phosphate dehydrogenase in the presence of potential regulators; error bars represent the s.e.m. All regulators were tested at two concentrations in four distinct replicates (n = 4, ATP: 5 mM, 18 mM; GTP: 2 mM, 10 mM; IMP and AMP: 1 mM, 5 mM), and the asterisk denotes significant inhibition (18 mM ATP: P-value = 0.029; 10 mM GTP: P-value = 0.025, one-tailed t-test). Relative activity of phosphate acetyltransferase in the presence of phenylpyruvate and tryptophan; error bars represent the s.e.m. All regulators were tested at three concentrations in three distinct replicates (n = 3, phenylpyruvate: 0.1, 1, 5 mM; l-tryptophan: 0.1, 1, 4.4 mM). Relative activity of fructose-bisphosphate aldolase class II in the presence of 5 mM of potential regulators, except hypoxanthine, which was tested at 3.22 mM. All regulators were tested in three distinct replicates (n = 3), and error bars represent the s.e.m. The asterisk denotes significant activation (5 mM 3PG: P-value = 0.025; 5 mM ATP: P-value = 0.024; 5 mM PEP: P-value = 0.046, one-tailed t-test). Data information: The raw data for all in vitro enzyme assays can be found in Dataset EV3. Download figure Download PowerPoint Discussion To probe the depth of our present knowledge on protein–metabolite interactions, we systematically mapped protein–(polar) metabolite interactions in the arguably best-characterized molecular network: E. coli central metabolism (Keseler et al, 2013; Placzek et al, 2017). For this purpose, we developed a higher throughput version of a ligand-detected NMR assay that was recently showcased to provide direct readout of binding events at high sensitivity for weak interactions (Nikolaev et al, 2016). Even with very conservative cutoffs at a 5% false-positive rate of the known interactions, the metabolite-binding profiles of 29 central enzymes with 55 metabolites identified 76 novel interactions. Detected interactions were spread across most enzymes, rather than focusing on few regulatory hubs. Although our NMR assays do not provide direct functionality evidence, we estimate that over 60% of the newly detected binding events are functional, based on the recovery of positive controls from protein–metabolite interaction databases and functional in vitro assays. By constructing the first near-comprehensive map of protein–(polar) metabolite interactions in a defined biological subnetwork, we could essentially double the number of known interactions. Given that central metabolism was already heavily investigated, our results suggest that the presently available information about protein–metabolite interactions may only be the tip of the iceberg. Most striking was the large number of interactions with purine nucleotides, most" @default.
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• W2969990740 title "Systematic mapping of protein‐metabolite interactions in central metabolism of <i>Escherichia coli</i>" @default.
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