Systematic Identification of ProteinâMetabolite Interactions in Complex

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Systematic identification of protein-metabolite interactions in complex metabolite mixtures by ligand-detected NMR spectroscopy Yaroslav V. Nikolaev, Karl Kochanowski, Hannes Link, Uwe Sauer, and Frederic H.-T. Allain Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.5b01291 • Publication Date (Web): 11 Apr 2016 Downloaded from http://pubs.acs.org on April 12, 2016

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Biochemistry

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Systematic identification of protein-metabolite

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interactions in complex metabolite mixtures by ligand-

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detected NMR spectroscopy

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Yaroslav V. Nikolaev1,¶,*, Karl Kochanowski2,3,¶, Hannes Link2,4, Uwe Sauer2, Frederic H.-T. Allain1,*

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1

Institute of Molecular Biology & Biophysics, ETH Zurich, Switzerland.

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2

Institute of Molecular Systems Biology, ETH Zurich, Switzerland.

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3

Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland

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4

Max-Planck Institute for Terrestrial Microbiology, Marburg, Germany.

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*

Corresponding authors:

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Yaroslav Nikolaev ([email protected], +41 44 633 0714)

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Frederic Allain ([email protected], +41 44 633 3940)

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¶

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Funding

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This work was in part supported by the Promedica Stiftung, Chur (grant 1300/M to Y.N.).

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These authors contributed equally.

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Abstract

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Protein-metabolite interactions play a vital role in the regulation of numerous cellular processes.

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Consequently, identifying such interactions is a key prerequisite for understanding cellular regulation.

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However, the non-covalent nature of the binding between proteins and metabolites has so far hampered

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the development of methods to systematically map protein-metabolite interactions. The few available,

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largely mass-spectrometry based, approaches are restricted to specific metabolite classes, such as

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lipids. In this study, we address this issue and show the potential of ligand-detected nuclear magnetic

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resonance (NMR) spectroscopy, which is routinely used in drug development, to systematically

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identify protein-metabolite interactions. As a proof-of-concept, we selected four well-characterized

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bacterial and mammalian proteins (AroG, Eno, PfkA, BSA) and identified metabolite binders in

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complex mixes of up to 33 metabolites. Ligand-detected NMR captured all of the reported protein-

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metabolite interactions, spanning full range of physiologically relevant Kds (low-µM to low-mM). We

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also detected a number of novel interactions, such as promiscuous binding of the negatively charged

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metabolites citrate, AMP, and ATP, as well as binding of aromatic amino acids to AroG protein. Using

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in vitro enzyme activity assays, we assessed the functional relevance of these novel interactions in the

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case of AroG and show that L-tryptophan, L-tyrosine and L-histidine act as novel inhibitors of AroG

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activity. Thus, we conclude that ligand-detected NMR is suitable for the systematic identification of

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functionally relevant protein-metabolite interactions.

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Keywords

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allosteric regulation, metabolite-protein interactions, Nuclear Magnetic Resonance spectroscopy

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ACS Paragon Plus Environment

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Biochemistry

Interactions between proteins and metabolites are pivotal for the regulation of diverse cellular

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processes, such as metabolism 1, gene expression

2,3

, and chromatin remodeling 4, allowing cells to

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mount regulatory responses based on their current metabolic state. Therefore, approaches to

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systematically map such interactions are a key prerequisite for understanding cellular regulation 1.

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However, the generally weak affinity of protein-metabolite interactions makes them notoriously

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difficult to detect experimentally. Compared to the plethora of available methods to detect other types

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of biological (i.e. protein-protein or protein-DNA 1) interactions, the development of equivalent

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methods for the detection of protein-metabolite interactions has so far lagged behind

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advances towards this end have led to the development of a few methods to identify such interactions8.

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However, these methods are either restricted to specific metabolite classes, such as lipids 9–12, or rely on

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the indirect identification of protein-metabolite interactions, for example through metabolite-induced

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changes in protein conformation

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protein-metabolite interactions 16. Other indirect methods rely on detecting the sequestration of free

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metabolites through protein binding, but require equimolar amounts of proteins and metabolites,

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restricting their utility to proteins which can be easily purified in large amounts and are stable at high

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concentrations

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metabolite interactions are still largely being identified using laborious in vitro activity assays 7, which

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are often not amenable to non-enzymatic proteins.

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or stability

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5–7

. Recent

which are not necessarily indicative of functional

. Thus, due to the lack of generally applicable systematic approaches, protein-

Complementary to these largely mass-spectrometry based approaches (see also

18,19

), interactions

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between biomolecules can be analyzed using several Nuclear Magnetic Resonance (NMR)-based

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techniques

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(e.g. metabolites) can be directly identified by “ligand-detected NMR” methods such as saturation

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transfer difference (STD) NMR

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20,21

. In particular, interactions between large molecules (e.g. proteins) and small molecules

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, water-ligand observed via gradient spectroscopy (WaterLOGSY)

, diffusion- and relaxation-edited NMR 26. These NMR methods allow the direct detection of ligand


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binding to a purified protein, without any isotope labeling of either component, and without the need to

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perform activity based in vitro assays. Ligand-detected NMR has been primarily developed in the

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context of high-throughput screening of synthetic compound libraries, thereby facilitating drug

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discovery 20,27–30. However, outside of drug discovery applications, such experiments were not used for

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the systematic identification of novel functional interactions of endogenous metabolites with proteins,

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with example studies focusing on few selected proteins or metabolites 31–33, or using the ligand-binding

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profiles to identify functionally related proteins 34.

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In this proof-of-concept study we demonstrate the applicability of ligand-detected NMR for

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systematic identification of functional interactions between proteins and endogenous metabolites in

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vitro. Using two complementary NMR methods and complex mixtures of up to 33 chemically diverse

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metabolites, we recovered all known protein-metabolite interactions for 4 well characterized proteins,

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and identified several new interactions. Furthermore, using enzymatic activity assays, we validated the

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functional relevance of three of these novel interactions, namely between the protein AroG and the

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metabolites L-tryptophan, L-tyrosine and L-histidine. To facilitate comparisons of interactions between

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different protein-metabolite pairs, we established a quantitative metric which provides an estimate for

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the affinity of the interaction.

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Experimental procedures Strains and chemicals Unless stated otherwise, chemicals were obtained from Sigma-Aldrich. Overexpression strains for AroG, PfkA, and Eno proteins from E. coli were obtained from the ASKA library

35

. The GFP

overexpression plasmid pTrc99KK-GFP with an N-terminal His6x-tag was constructed using GFPmut2 from

pUA66

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as

a

template,

and

pTrc99KK

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as

a

vector

(primer

1:

GCCTCGAGATGCATCATCATCATCATCATATGTCTAAAGGTGAAGAATTATTC, primer 2: CGGGATCCTTATTTGTACAATTCATCCATAC), and transformed to the E. coli strain BW25113 38 by electroporation. Protein expression and purification 50 mL LB shake flask cultures were inoculated 1:100 with LB precultures, and expression was induced with 0.2 mM IPTG. Cultures were incubated for 16h at 37°C with shaking (300 rpm). Cells were harvested by centrifugation (5000 g, 4°C, 15 min), washed once with 0.9% NaCl and 10 mM MgSO4 and concentrated tenfold in lysis buffer (20 mM sodiumphosphate buffer, pH 7.4, 500 mM NaCl, 20 mM imidazole, 2 mM dithiothreitol, 1 mM MgCl2, 4 mM phenylmethylsulfonylfluorid). Cells were disrupted by passaging through a French press three times at 4°C, and cell extracts were separated from cell debris by centrifugation (20000 g, 4°C, 30 min). His-tagged proteins were purified from cell extracts using nickel-sepharose gravity flow columns (GE Healthcare), and the elution buffer was replaced by the respective assay buffer (50 mM potassiumphosphate buffer, pH 7.5, 10 mM MgCl2) using filter columns with 10 kD cutoff (Millipore). Lyophilized bovine serum albumin was obtained from Sigma-Aldrich and resuspended in assay buffer (50 mM potassiumphosphate buffer, pH 7.5, 10 mM MgCl2). The purity of all tested proteins was above 90%, as assessed by SDS-PAGE, in agreement with the study in which these overexpression strains had been first described 35 (Supplementary Figure S2).


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NMR measurements and analysis Sample preparation and NMR measurements. Protein concentrations were measured based on their specific extinction coefficients at 280 nm immediately before NMR sample preparation. In experiments with complex metabolite mixes the final protein and individual metabolite concentrations were 20-30 µM (monomer) and 200 µM respectively. In Kd determination experiments protein concentrations were 10 µM. All samples were prepared with total volume 425-500 µl in 5mm-TA tubes (ARMAR Chemicals), and contained 5% D2O and 25 µM DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid). In the presence of BSA protein, DSS signal appeared broad and shifted, suggesting an interaction with the protein, hence alternative chemical shift referencing was used in this case (see below). NMR Measurements were performed on Bruker Avance III-HD 600 MHz spectrometer using a CPTCI cryo-cooled probehead. A set of experiments with GFP protein was recorded on a Bruker Avance III 750 MHz spectrometer with a TXI room-temperature probe. At the beginning of all experimental series the temperature was calibrated to 298K using a 99.8% Methanol-d4 sample. Pulseprograms for WaterLOGSY and T1rho relaxation were adapted from experiments of the Novartis NIBR NMR team (Basel, Switzerland). The Polarization-Optimized (PO)-WaterLOGSY experiment 39 was used. T1rho experiments used 10 and 200 ms relaxation delays. WaterLOGSY mixing time was 0.8 s. Both T1rho and WaterLOGSY used 2 s of recovery time. STD was recorded using excitationsculpting water suppression pulse sequence (stddiffesgp.2 in Bruker library) with 4.8 s total recovery time including 2 s saturation with 10 ms selective pulses at -50 and 0.8 ppm for off- and on-resonance spectra respectively. NMR processing and data analysis. Spectra were processed in TopSpin 3.2 (Bruker) using custombuilt Python scripts to process, calibrate and generate difference spectra. The final difference spectra correspond to: (Imetab+protein – Imetab – Iprotein), where Imetab+protein is the intensity at the position of a given metabolite signal in the spectrum of the metabolite mixture in the presence of the protein, and Imetab and


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Iprotein are the intensity at the same position in spectra of the metabolite mixture and the protein alone respectively. In T1rho method intensities of individual signals in these three samples were taken from the difference of T1rho experiments with short and long spin-lock times (I10ms – I200ms). In STD the difference between off- and on- resonance spectra was taken. The corresponding difference in WaterLOGSY experiment was implemented in the pulse-program itself and therefore did not require additional subtraction during data analysis. The subtraction of the spectra was performed to obtain interaction hits as positive signals in the final difference. Calibration of spectra to DSS was critical to minimize subtraction artifacts in the final difference spectra. In the case of BSA protein, due to its apparent interaction with DSS, the spectra were referenced against a ~1.2 ppm singlet peak from a trace contaminant present in all our water samples. Identification, S/N quantification, assignment and disambiguation of interaction hits was performed using custom-built Matlab scripts. Hit confidence cutoff based on Signal-to-Noise quantification. Only peaks showing a signal-tonoise ratio ≥ 3 after local vs global noise thresholding (see below) were considered for the analysis. Global noise was calculated as standard deviation of intensities in empty regions of the spectrum. Local noise was calculated as the standard deviation of intensities within ± 4 peak widths around the specific signal, minus the median intensity in this region – to separate contribution of other positive signals in this region from the actual noise. Peak width was fixed at 0.005 ppm (3 Hz on 600 MHz spectrometer), linewidth of a singlet proton signal at half-height, after application of a 1Hz-broadened exponential apodization function. The boundaries of a peak were set at the first point with zero or negative intensity to the left and right of the signal region (± 1 peak width around the signal maximum). Local noise was used for S/N calculation in cases when its value exceeded the global noise value by a factor of 3 (indicating that the peak was in a crowded region of the spectrum). Fractional signal intensity. To obtain a measure of how many metabolite molecules interacted with the protein target over experiment time (i.e. to approximate the combination of kinetic and equilibrium


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constants characterizing the given protein-metabolite interaction), the signal intensities in the final difference WaterLOGSY, T1rho and STD spectra were normalized against the corresponding maximum signal intensities derived from reference 1D experiments. For WaterLOGSY and STD the reference 1D was obtained with the excitation sculpting water suppression part of the WaterLOGSY sequence, and for T1rho the experiment with short (10ms) relaxation delay was used as the reference. The reference spectra were also used in the form of a difference spectrum (Imetab+protein – Iprotein), to remove the contribution of protein signals. Taking into account the initial subtractions made for each individual spectrum, the final result is a multi-difference spectrum. The formula for the calculation of the fractional signal intensity can be expressed as follows: !!"#$%!!"#$%&' − !!!"#$ − !!"#$%&'

!"#$%& !" !!!!! !" !"#

!!"#$%!!"#$%&' − !!"#$%&'

!"#"!"$%"

In vitro enzyme assays Enzymatic in vitro assays for AroG were performed as described previously 40. Briefly, assays were performed at room temperature in reaction buffer (50 mM potassiumphosphate buffer, pH 7.5, 10 mM MgCl2) with 100 µM PEP and 100 µM E4P, and 100 µM of the competing metabolites. Reactions were started by addition of purified AroG enzyme (final conc in assay 2.7 µg/mL, corresponding to a monomer concentration of 71 nM), and the decrease in PEP concentration was monitored photometrically at 232 nm every 6 seconds. Initial reaction velocities (within the first 60 to 90 seconds) were then determined by linear regression.


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Biochemistry

Results Selection of target proteins and metabolites. To assess the utility of ligand-detected NMR for the identification of endogenous protein-metabolite interactions, we selected three metabolic enzymes from E. coli with known metabolite binders. Namely, phosphofructokinase I (PfkA) and enolase (Eno), two enzymes of the glycolysis pathway, as well as 2-dehydro-3-deoxyphosphoheptonate aldolase (AroG), an enzyme catalyzing the first step in chorismate biosynthesis. The three enzymes were overexpressed and purified from a library of his-tagged overexpression strains 35. To illustrate that this approach is not restricted to enzymes, and to provide a widely available benchmark protein, we further selected a wellcharacterized non-enzymatic protein of comparable size, namely bovine serum albumin (BSA) (see Table 1 for the full list of proteins tested with molecular sizes and interactions reported in literature). A key challenge for approaches aiming at identifying protein-metabolite interactions resides in the vast chemical diversity of endogenous metabolites. We therefore selected seven chemically diverse metabolites (Table 2, left panel) which include known interactors such as L-phenylalanine, an allosteric inhibitor of AroG, as well as reported regulatory metabolites

1,41

. To avoid artifacts arising due to

covalent modifications of metabolites, when composing metabolite mixtures we made sure that none of the tested enzymes had all substrates present in the same mixture at the same time, and also ensured stability of metabolite mixtures in presence of target proteins over time. Selection of NMR methods for the detection of protein-metabolite interactions. The main NMR methods for ligand-detected primary screening using unlabeled components are STD, diffusion-editing, WaterLOGSY, T1rho and T2 relaxation

20,42

. These NMR methods operate via different physical

mechanisms, but all of them detect signals from small-molecular-weight ligands and changes of their properties upon binding to a large-molecular-weight target (a protein in our case). WaterLOGSY and STD experiments detect changes in dipolar interactions between spins (the nuclear Overhauser enhancement, NOE), T1rho/T2 relaxation detect increased signal decay rates (relaxation rate) in


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metabolites upon their binding to the target, and diffusion-edited techniques detect the decrease in diffusion of small molecules. In contrast to conventional protein-detected NMR, none of the components needs to be labeled, and an increase in the protein size improves the detection sensitivity. All above methods are well established, but diffusion editing and “line broadening” T2 relaxation are considered less sensitive 42, so we initially aimed to compare STD, WaterLOGSY and T1rho. Our tests with BSA protein and 7-metabolite mixture showed that STD is on average less sensitive compared to the other two methods (Supplementary Figure S1A), which correlates with the recent report which compared sensitivity of STD and WaterLOGSY experiments {Antanasijevic, 2014, JBNMR, p37}. Also, in contrast to WaterLOGSY and T1rho, the STD experiment requires optimization of saturation parameters for each protein individually, making it less practical for screens involving multiple different proteins. Thus, because of the above sensitivity and robustness reasons, only PolarizationOptimized-WaterLOGSY 39 and T1rho relaxation 26 were used for the subsequent measurements. Identification of protein-metabolite interactions by two complementary ligand-detected NMR methods. For Kd values in the range of the physiological metabolite concentrations (≥ 1 µM 43), the signal observed in WaterLOGSY and T1rho is roughly proportional to the interaction affinity – and decreases at higher Kd values (42, Figures 1, S3 and results below). Currently there is no general approach to convert single-titration-point WaterLOGSY or T1rho relaxation signals into Kd values, due to the large number of parameters which influence this relationship, including not readily-measurable physico-chemical properties of the ligand molecules in the bound state, such as chemical shifts and relaxation rates of observed nuclei. However, an approximate metric was required for a quantitative comparison of the detected interactions, which would at least correct for peak multiplicity and linewidth. A few affinity ranking methods have been proposed before for specific NMR experiment types 44–46

, but no experiment-independent metric has been described in the literature. To facilitate a

quantitative comparison of interactions obtained from different metabolites and experiments, we


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defined the metric ‘fractional signal intensity’ (FSI), which reflects of the fractional change in the metabolite signal intensity in a ligand-detected NMR experiment, when normalized to a reference experiment (see Methods and Figure 1). In all our experimental data, this metric shows a fairly good correlation for the same interactions observed independently by WaterLOGSY and T1rho experiments (Supplementary Figure S4A), supporting our choice of the metric for this proof-of-principle study. The FSI metric also showed moderate correlation when comparing between three experiment types recorded under identical conditions, especially between WaterLOGSY and STD hits (Supplementary Figure S1B). Using the ‘fractional signal intensity’ metric to identify metabolite-protein interactions, we tested each protein against the seven-metabolite mix. We found that both methods yielded similar fractional signal-based interaction maps (Figure 2B and 2D), suggesting that the identified interactions are robust against method-dependent variability. Notably, all previously reported protein-metabolite interactions were recovered. Additionally, both methods detected several hitherto unreported interactions, with differences between the tested proteins: BSA and AroG exhibited significant interactions with a greater number of the tested metabolites than Eno and PfkA (5 vs 2 hits in this 7-compound mixture). Notably, citrate (CIT) and adenosine-monophosphate (AMP) showed interactions with three (CIT) and four (AMP) tested proteins, respectively, suggesting that these metabolites show a higher tendency for protein binding than the others. To exclude that these observations reflect unspecific changes of the metabolite properties in the presence of any protein target (e.g. due to nonspecific self-aggregation of metabolites facilitated by any protein interface), we further tested recombinant green fluorescent protein (GFP), which has no reported interactions with metabolites. We found that indeed none of the tested metabolites interacted with GFP, with the exception of a weak interaction with AMP in the WaterLOGSY experiment (Figure S5B). Note that due to its smaller molecular size (Table 1), in the employed NMR experiments GFP


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yields generally weaker interaction signals than the other tested proteins. Consequently, while ruling out nonspecific aggregation of metabolites, this data does not allow direct comparison of the GFPAMP interaction affinity with the other proteins tested. Together with the marked differences in the identity of the detected interactions among the other tested proteins, the GFP results suggest that the detected interactions constitute specific metabolite binding to the respective proteins. Recovering protein-metabolite interactions in complex metabolite mixtures. A key property of many protein-metabolite interactions is that they can be affected by the presence of other metabolites, which directly compete for the same protein binding site, or allosterically alter the protein’s sensitivity to the presence of a particular metabolite 47. To assess the potential impact of such interference, we measured again each protein in the presence of two complex mixtures comprised of 15 and 33 central metabolites (Table 2, Supplementary Figure S6), while maintaining the same buffer, protein and individual metabolite concentrations. Despite the increased overlap of signals in the resulting spectra, we found most of the previously identified interactions to be unambiguously recovered in these more complex metabolite mixtures (example of WaterLOGSY and T1rho spectra for AroG shown in Figure 3A, and interaction maps for all proteins in Figure 3B). In the WaterLOGSY experiment, 5 (38%) of the 13 interactions identified in the 7-metabolite mix (total sum for all four tested proteins), were below the confidence limit for detection (signal/noise ≥ 3) when the mix complexity was increased to 15 metabolites. A further increase in complexity – from 15 to 33 metabolites in the mixture – resulted in the loss of 4 (21%) of 19 interactions observed in the mixture of 15 metabolites. Similar changes were observed in T1rho experiment – with 7/15 (47%) and 5/20 (25%) of the interactions being lost upon increasing the complexity from 7 to 15 and from 15 to 33 metabolites, respectively. This may result from a decrease of the signal intensity due to metabolite competition for the same binding sites, or due to an increase of the noise due to spectral crowding (high density of signals in the given region of the spectrum). Nevertheless, even in the most complex mixtures, ligand-detected NMR identified most of


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the previously reported interactions (Figure 3B, indicated by black and red stars) and suggested a significant number of new interactions. Taken together, these results show that most of the identified interactions are robust against potential interference by other metabolites, allowing us to generate systematic interaction maps between proteins and metabolites even in fairly large metabolite mixtures. Effective affinity range of detectable protein-metabolite interactions. An important criterion to assess the functional relevance of a protein-metabolite interaction in the cellular context is the metabolite affinity for the protein, which allows for a determination of the degree of saturation of the protein assuming that the metabolite’s in vivo concentration is known 43. We therefore wanted to assess the effective affinity range of the protein-metabolite interactions detectable in our assays. First, we compared all of the detected interactions with corresponding Kd/Km values reported in literature (Table S1), which showed that the observed hits cover affinities from 9 µM (AroG-PEP) to 0.75 mM (PfkAPEP). Second, we quantified the Kd values for one of the strongest and one of the weakest signals in our data set based on the fractional signal intensity metric, namely the PfkA-ATP and PfkA-FBP pairs, and found those to be 48(±12) µM and 1150 (±290) µM respectively (Supplementary Figure S3C). Thus, the effective affinity range detectable in our assays covers three orders of magnitude (~1 µM to ~1 mM) which is within the expected sensitivity range of the WaterLOGSY and T1rho relaxation experiments 42. Given the fact that the Km values of enzymes

48

as well as metabolite concentrations in

bacterial central carbon metabolism 43 are typically between 1 µM and 1 mM (i.e. the concentrations of the metabolites tested here), we conclude that the ligand-detected NMR approach presented here is well suited to detect most physiologically relevant protein-metabolite interactions. Dependence of the fractional signal intensity on the interaction Kd. The full range of WaterLOGSY and T1rho sensitivity covers affinities with Kds from ~0.1 nM to ~10 mM

42

. To

visualize the dependence of the observed signals on the interaction Kd we plotted fractional signal intensities for individual protons of ATP and FBP molecules in the presence of PfkA protein against


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the measured Kd values of these interactions (Supplementary Figure S3B). This data is in agreement with the expectation to observe a bell-shaped dependence – with high intensity signals for interactions with low-µM Kd’s, and low intensity signals for interactions with Kds at the limits of the nM-to-mM affinity range detectable by the methods used. The reason for the bell-shaped dependence of signal intensity on affinity (Supplementary Figure S3B) is that both WaterLOGSY and T1rho use an excess of the metabolite ligand over the protein target (10-100:1), and rely on an amplification effect to alter the NMR properties of multiple ligand molecules using sub-stoichiometric amount of the target. This amplification can be perceived in analogy to enzymatic turnover occurring during a fixed experimental time: interactions with ~µM Kd’s yield multiple protein-metabolite turnover events and hence give a large signal, nM Kd’s (high affinity interactions) yield only a few turnover events (protein-metabolite complex dissociates too slowly), and high-mM Kd’s (low affinity interactions) yield no detectable turnover of metabolite properties during experiment time (illustrated in Supplementary Figure S3A,B). Overall this results in a bell-shaped dependence of the observed signal on the interaction Kd (Supplementary Figure S3B). Detecting ~5% reduction of the final signals in the case of high-affinity (“single-turnover”) events is beyond the sensitivity threshold of a regular screening NMR assay, and therefore high-affinity ligands appear to be non-binding in such screens

49

. However, the physical

constraints of a bacterial cell (in E. coli, 1 nM translates into a single molecule per cell 50) suggest that such high-affinity metabolite-protein interactions are probably rather rare in microbes, at least for intracellular proteins. Overall, the above analysis illustrates the quantitative dependence of the detected NMR signals across the full range of physiologically relevant interaction affinities detectable in the WaterLOGSY and T1rho experiments. Validation of novel protein-metabolite interactions by in vitro activity assays. Finally, we focused on functionally verifying the novel protein-metabolite interactions identified by liganddetected NMR. To check whether these novel interactions also affect the activity of the respective


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proteins, we performed in vitro activity assays in the presence or absence of the potential interactor. In particular, we focused on the proposed interactions with AroG. Using enzymatic in vitro assays, we tested the effect of seven metabolites, which were identified to consistently bind AroG with both NMR methods, on AroG activity at two concentrations and found that several of these metabolites indeed caused a reduction in enzyme activity (Figure 4). The strongest effect on AroG activity was found for L-phenylalanine, its reported allosteric regulator. Moreover, addition of L-tryptophan or L-tyrosine caused a ~20% reduction in AroG activity already at 100 µM effector concentration, and 1 mM Lhistidine caused a ~20% reduction in AroG activity. In all three cases, the effect on AroG activity was concentration dependent, albeit within different concentration ranges, and only L-tryptophan showed clear saturation effect in the tested concentration range (Supplementary Figure S7). In contrast, the promiscuous binding metabolites citrate and AMP did not affect AroG activity. Taken together, these results confirm that ligand-detected NMR is suitable to identify functionally relevant proteinmetabolite interactions.


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Discussion The systematic identification of protein-metabolite interactions remains a key challenge in the investigation of cellular regulatory processes. In this work, we present the use of ligand-detected NMR experiments as a widely applicable approach to observe protein-metabolite interactions in vitro. As a proof-of-concept, we used PO-WaterLOGSY and T1rho relaxation NMR experiments to systematically detect binding between four well-characterized enzymatic and non-enzymatic proteins and several metabolite mixtures comprising up to 33 metabolites. We could recover all reported interactions in the basic (7 metabolites) mixture, and a large majority (9/12=75%, T1rho experiment) of the reported interactions even in the complex (33 metabolites) mixture. Moreover, we identified a number of novel interactions, most notably promiscuous binding of nucleotide mono- and triphosphates as well as citrate, and several interactions involving aromatic amino acids. Finally, using in vitro activity assays, we evaluated the impact of these novel interactions on protein activity and found that, in addition to the already reported L-phenylalanine as an allosteric effector of AroG, AroG activity can also be modulated by three other aromatic amino-acids – L-tryptophan, L-tyrosine and L-histidine. One surprising result of this study is the promiscuous binding of the nucleotides AMP and ATP. These metabolites showed binding to all proteins tested, albeit to a different extent (their signals were affected much more strongly in presence of BSA, AroG and PfkA compared to Eno and GFP). BSA is known to bind AMP and ATP, and PfkA uses ATP as a substrate, but for two other proteins such binding was not anticipated. Notably, a recent study showed that nucleotides regulate the activity of many glycolytic enzymes in yeast 51, and nucleotides may exert similar pleiotropic effects in E. coli. However, in our in vitro assays enzymatic activity of the AroG protein was not affected in presence of these nucleotides, thus we cannot exclude that the detected promiscuous binding of these metabolites may depend on our experimental conditions and not be functionally relevant. Another possibility is that certain allosteric regulators will manifest their effect only in combinations with other effectors


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(metabolites or larger biomolecules) in vivo, thereby appearing non-functional in one-on-one in vitro activity tests 47. Finally, it is possible that nucleotide binding does not affect enzyme activity per se, but rather other features such as complex formation52, or moonlighting functions such as RNA binding, which has recently been found for various highly conserved human metabolic enzymes53. Notably, human enolases which were shown to interact with RNA (i.e. polymeric nucleotides)53 share 52-55% sequence identity with the E.coli enolase shown to interact with monomeric nucleotides in our study. The physiological relevance of potential promiscuous binding of citrate is less clear. One possibility is that citrate, being rich in acidic oxygen moieties similar to phosphate groups, may bind to the same phosphate-binding sites on the protein surface and thereby modulate the effect of nucleotide phosphates on protein activity. This hypothesis is in line with observations that ~20% of mRNA-binding metabolic enzymes share an ability to bind nucleotides and anionic molecules53. Another surprising result is the binding of the aromatic amino acids L-tryptophan, L-tyrosine and Lhistidine to AroG. Although the effect of these amino acids on AroG activity was modest in comparison to the previously reported AroG regulator L-phenylalanine, E. coli AroG has two additional isoenzymes AroH and AroF, which exhibit strong allosteric regulation by L-tryptophan and L-tyrosine, respectively 40,54. Given the high sequence similarity between the three iso-enzymes 54, it is tempting to speculate that aromatic amino acids do indeed bind all three iso-enzymes, but differ in the extent to which they affect each enzyme’s activity due to small differences in the protein structure. Overall, our results suggest that systematic screens based on ligand-detected NMR may shed light on the relationship between a protein sequence and its susceptibility to regulation by a particular allosteric effector, or even by a whole class of metabolites with similar chemical properties. To facilitate quantitative comparison of interaction hits obtained from different metabolites and experiments, we developed the metric ‘fractional signal intensity’ (FSI), which spans values from 1 to 0 and reflects how much the metabolite signal is altered in the presence of a protein. Similar metrics for


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specific NMR experiment types were described before 44–46, but to our knowledge no generalized metric was discussed in the literature. To a rough approximation high FSI value suggests an interaction Kd in the low-µM range, and low FSI – a Kd in the low-mM range. It should be underscored that FSI is an approximate metric which is not meant to replace the true affinity measures like Kd, but merely to compare different interactions observed in the same multi-target and multi-component interaction screen. Most importantly this metric eliminates the dependence of the quantified results on the multiplicities and linewidths of the NMR signals. But it remains dependent on concentrations and relative ratios of the target and ligand molecules, as well as certain physico-chemical properties of the interaction pair (size and shape of the components, kinetics of the interaction (kon, koff, exchange broadening), chemical shifts and relaxation rates of the observed nuclei in the bound and free states of the ligand). For WaterLOGSY also the structural topology of the protein-metabolite interaction interface and properties of exchangeable protons shall have a strong influence. In this work, we have used two complementary ligand-detected NMR methods. We found that both methods showed generally good agreement regarding the detection of protein-metabolite interactions and effective detectable affinity range, suggesting that using only one method should be sufficient to detect such interactions in high-throughput applications. Comparison of fractional signal intensities observed for the same interactions in PO-WaterLOGSY and T1rho relaxation (linear fits in Figure S4A), indicates that WaterLOGSY is slightly more sensitive under our experimental conditions. However, WaterLOGSY to a certain extent depends on properties of structured water molecules, exchangeable protons and dipolar interactions at the binding site 55, making it more sensitive to the exact chemistry and topology of the interaction, especially for non-polar metabolites. In contrast, T1rho relaxation depends primarily on the macroscopic properties of the target-ligand complex and therefore should be less biased towards the chemical properties of the ligand and topology of interaction. Furthermore, since T1rho relaxation experiment does not depend on H2O for magnetization transfer, it


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could be performed in D2O, thereby increasing robustness of the experimental setup (i.e. no need for suppression of the strong H2O solvent signal) and improving sensitivity for signals located near the H2O frequency, which may be masked otherwise. The above considerations suggest that T1rho relaxation experiments may be more suitable for high-throughput applications than WaterLOGSY. Saturation Transfer Difference (STD) experiments were not included in the main assays of this study due to their apparent lower sensitivity (Supplementary Figure S1 and

56

, and the need for protein-

specific optimization of experimental setup (protein saturation parameters). Nevertheless STD experiments remain unique in their ability to give accurate information for structural studies 57,58, and to increase spectral resolution in studies of complex mixtures by employing multi-dimensional STDNMR experiments24,59,60. A key advantage of ligand-detected NMR over classical activity-based assays is the possibility to test the impact of several metabolites simultaneously. The results of our study highlight the trade-off one should make between simplicity of data interpretation and the level of throughput obtained: testing only a few metabolites simultaneously greatly simplifies the data analysis and peak assignment at the expense of limited throughput and increased consumption of often scarce proteins. Conversely, increasing the number of metabolites will inevitably lead to ambiguous assignment of potential proteinmetabolite interactions due to spectral overlap of chemically similar compounds. Also, compounds may compete for the same binding site on the target (e.g. protein), reducing the observed signal intensities of their competitors and increasing the likelihood of false-negatives. This is especially critical if tight binders are present in the mixture, which would block the binding interface(s) of weak binders. As an illustrative example, in our experiments interactions of AMP with the PfkA and AroG proteins were strongly attenuated in the presence of ATP (Figure 3B – compare 7-compound and 15-compound mixtures). Nevertheless, most interactions were recovered even when the complexity of the metabolite mixture was increased. Moreover, given that cells essentially constitute highly complex mixtures of


Biochemistry

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thousands of different metabolites, using more complex/realistic metabolite mixtures may actually allow to distinguish robust interactions which are likely to be relevant in vivo from interactions that only occur in vitro. In vivo concentrations of the metabolites tested here are dependent on the cell culturing conditions, but for most of the tested metabolites the 200 µM concentration used in our NMR experiments is within their physiological concentration range. It should be pointed out that the competition for binding sites or indirect allosteric modulation of affinity of one ligand by another represent an important network of interactions regulating metabolism and other cellular processes. Although we represent the influence of ligands on each other’s binding signal as an interfering effect, the methods described here could be adapted to decipher such cross talk between ligands by comparing data obtained with different mixtures representing subsets which include or exclude the pairs which influence one another. A first limitation of the presented ligand-detected NMR methods is their restriction to detecting protein-metabolite binding, which does not directly translate into changes in protein activity. In drugscreening, NMR assays employing a competitor molecule are often performed to detect ligand interactions at specific functional sites61, but this is not possible when trying to detect new allosteric effectors for which binding sites are unknown. NMR can also be used to directly determine a protein’s activity, but without additional labeling of substrate molecules

62,63

the sensitivity of such NMR-based

enzymatic assays is likely to be lower than the sensitivity of common colorimetric, mass-spectrometric or fluorescence-based assays. Therefore, similar to the drug screening process, we envision that liganddetected NMR could be used to narrow down the number of potential interactions that are subsequently tested using targeted activity assays. A second limitation of the presented methods is the requirement for moderate amounts of metabolites and proteins – with common NMR instrumentation requiring minimum ~10 nmol of a compound at a concentration of around 30-40 µM (with a sample volume of 300 µl) and at least ~1 nmol of protein at minimum concentration of ~1 µM to stay within reasonable


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experimental durations (around 1 hour per sample). This makes the investigation of protein complexes, which can often only be purified in tiny amounts, more challenging. However, recent advances in genome editing, which allow the simultaneous purification of multiple proteins by addition of affinity tags

64

, may enable the larger-scale purification of protein complexes, or proteins for which no

overexpression library is currently available. Moreover, since NMR is non-destructive, the samples can be analyzed multiple times and stable sample components can be recovered. A third limitation of the ligand-detected NMR methods used here is the minimum size of the protein target, which should be at least 10-30 kDa

42

. This is due to the fact that the key characteristic differentiating free vs bound

metabolites in these methods is the rate of tumbling of the molecule in solution – the protein-metabolite complex needs to tumble substantially slower than the free metabolite. Fortunately, bacterial and eukaryotic enzymes are typically larger than 10 kDa 65. Moreover, for proteins with sizes below ~30 kDa a range of alternative, target-detected, NMR methods is available 20 (e.g. SAR by NMR 66), which probe the same binding events by looking at changes in the protein’s NMR properties, but require isotope labeling of the target. Taken together, the results presented in this study show that ligand-detected NMR is a suitable complementary approach to map protein-metabolite interactions on a system-level scale, and can further guide the biochemical and functional characterization of proteins. For example, ligand-detected NMR may be used to systematically identify interactions between endogenous metabolites and regulatory proteins, such as eukaryotic protein kinases/phosphatases, to unravel potential cross-talk between metabolism and cellular regulatory networks 67. Acknowledgements Y.N. acknowledges funding by the Promedica Stiftung, Chur (grant 1300/M). Authors thank the Novartis NIBR team for providing pulse-programs for PO-WaterLOGSY and T1rho experiments. We are grateful to Navratna Vajpai and Fred F. Damberger for critical comments on the manuscript. ACS Paragon Plus Environment

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Supporting Information Available Figure S1. Performance comparison for three most commonly employed ligand-detected NMR experiments. Figure S2. SDS-PAGE of all tested proteins. Figure S3. Effective affinity range and signal intensity for detectable protein-metabolite interactions. Figure S4. Comparison of hit signal intensities in WaterLOGSY and T1rho data. Figure S5. Test of GFP as a benchmark for moderatesized proteins in ligand-detected NMR. Figure S6. Reference spectra for 15-compound and 33compound mixtures. Table S1. Affinities of previously reported protein-metabolite interactions tested in this study. Supporting materials may be accessed free of charge online at http://pubs.acs.org.


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Tables Table 1. Overview of known protein-metabolite interactions in this study.

Protein name phosphofructokinase I, (PfkA, P0A796) enolase (Eno, P0A6P9)

Monomer size [kDa]

140

35

(tetramer) 92

46

(dimer)

2-dehydro-3deoxyphosphoheptonate 38 aldolase

152 (tetramer)

(AroG, P0AB91) Green fluorescent protein (GFP, P42212)

bovine serum albumin (BSA, P02769)

27.6

27.6

(monomer)

ATP, FBP, F6P, ADP PEP, phosphoglycerate

PEP, ADP, GDP

2-

PEP, erythrose-4- L-phenylalanine, phosphate, 3-deoxy-7L-alanine phosphoheptulonate

n/a

69-138 69

Known allosteric effectors/binding metabolites

Native size Substrate/product [kDa]

(monomer- n/a dimer)

ATP, AMP, citrate, L-histidine, pyruvate, L-phenylalanine, lactate

For PfkA, Eno, and AroG, information was obtained from ECOCYC 41 as well as BRENDA data bases. For BSA, known metabolite binders were obtained by literature research: ATP, AMP 68. Moreover, we included metabolites known to bind human serum albumin (HSA): histidine, pyruvate, phenylalanine, citrate 32. Italics: interaction not tested.


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Biochemistry

Table 2. Metabolite mixes used in this study. 7-metabolite mix

15-metabolite mix

33-metabolite mix

pyruvate

pyruvate

pyruvate

citrate

citrate

citrate

2-oxoglutarate

2-oxoglutarate

2-oxoglutarate

FBP

FBP

FBP

shikimate

shikimate

shikimate

L-phenylalanine

L-phenylalanine

L-phenylalanine

AMP

AMP

AMP

L-glutamine

L-glutamine

malate

malate

6PG

6PG

G6P

G6P

PEP

PEP

phenylpyruvate

phenylpyruvate

ATP

ATP

NAD+

NAD+ L-tryptophan L-tyrosine L-histidine L-arginine L-lysine L-glutamate L-aspartate L-asparagine L-serine L-threonine


Biochemistry

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L-valine glycine L-alanine L-leucine L-isoleucine L-proline L-methionine L-cysteine


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Biochemistry

Figures

Figure 1. Illustration of results from ligand-detected WaterLOGSY and T1rho relaxation experiments, and our quantification metric for the interaction affinities. The two NMR methods identify proteinmetabolite interactions by detecting changes in the NMR properties of a metabolite (M) when bound to a protein (P) (spectra on the left sides of the two middle panels). The magnitude of these changes depends on the interaction affinity and is reflected in the intensity of the difference signal (M+P minus M alone). Normalizing this difference signal to the reference spectrum of the metabolite (red) yields the fractional signal intensity – a proxy measure for the affinity between metabolite and protein. Fractional signal intensities reach maximum value for interactions with Kds in the low-µM range. See text for more details.


Biochemistry

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Figure 2. Ligand-detected NMR results for interaction of four proteins with a mixture of seven chemically diverse metabolites. A) 1D-1H-NMR reference spectrum of the full 7-metabolite mixture, and spectra of individual metabolite components. B,C) WaterLOGSY and T1rho relaxation spectra of 7-metabolite mixture in presence of four proteins (BSA, AroG, Eno, PfkA). Spectra were obtained by subtraction of the spectra of free protein and free metabolites from the combined protein+metabolite sample spectrum (see Methods). Metabolites interacting with the protein are seen as positive peaks at the chemical shifts (position in the frequency spectrum) characteristic for each metabolite. D,E) Interaction maps derived from WaterLOGSY and T1rho relaxation experiments. Intensity of color in the boxes corresponds to the fractional signal intensity (see Methods). All previously reported proteinmetabolite interactions (designated by black stars) were identified in this experiment. Abbreviations: PYR: pyruvate, CIT: citrate, AKG: 2-oxoglutarate, FBP: fructose-1,6-bisphosphate, SKM: shikimate, PHE: L-phenylalanine, AMP: adenosine-5-monophosphate.


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Biochemistry

Figure 3. Ligand-detected NMR with metabolite mixtures of increasing complexity. A) Example of WaterLOGSY and T1rho relaxation NMR spectra of AroG with three different metabolite mixtures (mixture compositions indicated as subscripts in (B), and listed in Table 2). Light red stripes show peaks used for generation of interaction maps for 33-compound mixture in (B). B) Interaction maps resulting from WaterLOGSY and T1rho experiments, for all four tested proteins and individual mixtures, based on unambiguously assigned metabolite peaks. Intensity of color in the boxes denotes the Fractional Signal Intensity (see Methods). Stars designate previously reported protein-metabolite interactions which were identified (black stars) or not identified (red stars) in the corresponding experiment. Abbreviations: PYR: pyruvate, CIT: citrate, AKG: 2-oxoglutarate, FBP: fructose-1,6-


Biochemistry

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bisphosphate, SKM: shikimate, PHE: L-phenylalanine, AMP: adenosine-5-monophosphate, GLU: Lglutamate,

MAL:

malate,

6PG:

6-phosphogluconate,

G6P:

glucose-6-phosphate,

PEP:

phosphoenolpyruvate, PhePyr: phenylpyruvate, ATP: adenosine-5-triphosphate, NAD: nicotinamide adenine dinucleotide, TYR: L-tyrosine, TRP: L-tryptophan, HIS: L-histidine, ARG: L-arginine, ASP: L-aspartate, LYS: L-lysine, ASN: L-asparagine, GLN: L-glutamine, SER: L-serine, THR: L-threonine, ALA: L-alanine, ILE: L-isoleucine, LEU: L-leucine, MET: L-methionine, VAL:L-valine, CYS: Lcysteine, GLY: glycine, PRO: L-proline.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 725 23 726 24 25 26 727 27 28 728 29 30 31 729 32 33 730 34 35 36 731 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Biochemistry

Figure 4. Effect of identified binding metabolites on AroG enzyme activity. In vitro enzyme activity of AroG as determined by photometric assays in presence of the respective binding metabolite at two concentrations (low: 100 µM; high: 1 mM for citrate, phenylpyruvate, L-histidine. 500 µM for AMP. 300 µM for L-phenylalanine, L-tryptophan, L-tyrosine). All experiments were performed in triplicate, and error bars denote the corresponding standard deviations. Metabolites denoted with *: significant change in enzyme activity (p-value < 0.02 as determined by two-tailed Student’s T-test).


Biochemistry

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For Table of Contents Use Only:

Systematic identification of protein-metabolite interactions in complex metabolite mixtures by ligand-detected NMR spectroscopy Yaroslav V. Nikolaev, Karl Kochanowski, Hannes Link, Uwe Sauer, Frederic H.-T. Allain


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Biochemistry

Illustration of results from ligand-detected WaterLOGSY and T1rho relaxation experiments. 167x73mm (300 x 300 DPI)


Biochemistry

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Ligand-detected NMR results for interaction of four proteins with a mixture of seven chemically diverse metabolites. 460x194mm (300 x 300 DPI)


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Biochemistry

Ligand-detected NMR with metabolite mixtures of increasing complexity. 540x444mm (300 x 300 DPI)


Biochemistry

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Effect of identified binding metabolites on AroG enzyme activity. 89x84mm (299 x 299 DPI)


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Systematic Identification of ProteinâMetabolite Interactions in Complex

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