Systematic Identification of Protein–Metabolite Interactions in Complex

Apr 11, 2016 - Protein–metabolite interactions play a vital role in the regulation of numerous cellular processes. Consequently, identifying such in...
0 downloads 11 Views 4MB Size
Subscriber access provided by GAZI UNIV

Article

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Biochemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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

1

Systematic identification of protein-metabolite

2

interactions in complex metabolite mixtures by ligand-

3

detected NMR spectroscopy

4

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

5

1

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

6

2

Institute of Molecular Systems Biology, ETH Zurich, Switzerland.

7

3

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

8

4

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

10

*

Corresponding authors:

11

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

12

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

13



14

Funding

15

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

9

These authors contributed equally.

16 17 ACS Paragon Plus Environment

Biochemistry

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

Page 2 of 44

18

Abstract

19

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

20

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

21

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

22

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

23

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

24

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

25

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

26

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

27

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

28

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

29

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

30

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

31

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

32

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

33

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

34

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

35

functionally relevant protein-metabolite interactions.

36 37

Keywords

38

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

39

ACS Paragon Plus Environment

Page 3 of 44

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

40

Biochemistry

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

41

processes, such as metabolism 1, gene expression

2,3

, and chromatin remodeling 4, allowing cells to

42

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

43

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

44

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

45

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

46

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

47

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

48

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

49

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

50

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

51

changes in protein conformation

52

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

53

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

54

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

55

concentrations

56

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

57

are often not amenable to non-enzymatic proteins.

58

17

13

or stability

14,15

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

59

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

60

techniques

61

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

62

transfer difference (STD) NMR

63

25

20,21

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

22–24

, water-ligand observed via gradient spectroscopy (WaterLOGSY)

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

ACS Paragon Plus Environment

Biochemistry

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

Page 4 of 44

64

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

65

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

66

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

67

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

68

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

69

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

70

profiles to identify functionally related proteins 34.

71

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

72

systematic identification of functional interactions between proteins and endogenous metabolites in

73

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

74

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

75

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

76

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

77

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

78

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

79

the affinity of the interaction.

80

ACS Paragon Plus Environment

Page 5 of 44

1 2 81 3 4 82 5 6 7 83 8 9 84 10 11 12 85 13 14 86 15 16 87 17 18 19 88 20 21 89 22 23 90 24 25 26 91 27 28 92 29 30 31 93 32 33 94 34 35 95 36 37 38 96 39 40 97 41 42 98 43 44 45 99 46 47 100 48 49 50 101 51 52 102 53 54 103 55 56 57 104 58 59 60

Biochemistry

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

36

as

a

template,

and

pTrc99KK

37

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).

ACS Paragon Plus Environment

Biochemistry

1 2 105 3 4 106 5 6 7 107 8 9 108 10 11 12 109 13 14 110 15 16 17 111 18 19 112 20 21 22 113 23 24 114 25 26 115 27 28 29 116 30 31 117 32 33 118 34 35 36 119 37 38 120 39 40 41 121 42 43 122 44 45 123 46 47 48 124 49 50 125 51 52 126 53 54 55 127 56 57 128 58 59 60

Page 6 of 44

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

ACS Paragon Plus Environment

Page 7 of 44

1 2 129 3 4 130 5 6 7 131 8 9 132 10 11 12 133 13 14 134 15 16 135 17 18 19 136 20 21 137 22 23 138 24 25 26 139 27 28 140 29 30 31 141 32 33 142 34 35 143 36 37 38 144 39 40 145 41 42 146 43 44 45 147 46 47 148 48 49 50 149 51 52 150 53 54 151 55 56 57 152 58 59 60

Biochemistry

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

ACS Paragon Plus Environment

Biochemistry

1 2 153 3 4 154 5 6 7 155 8 9 156 10 11 12 157 13 14 158 15 16 159 17 18 19 160 20 21 161 22 23 24 25 26 27 28 162 29 30 163 31 32 164 33 34 35 165 36 37 38 166 39 40 167 41 42 168 43 44 45 169 46 47 170 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 44

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.

ACS Paragon Plus Environment

Page 9 of 44

1 2 171 3 4 172 5 6 7 173 8 9 174 10 11 12 175 13 14 176 15 16 177 17 18 19 178 20 21 179 22 23 180 24 25 26 181 27 28 182 29 30 31 183 32 33 184 34 35 185 36 37 38 186 39 40 187 41 42 188 43 44 45 189 46 47 190 48 49 50 191 51 52 192 53 54 193 55 56 57 194 58 59 60

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

ACS Paragon Plus Environment

Biochemistry

1 2 195 3 4 196 5 6 7 197 8 9 198 10 11 12 199 13 14 200 15 16 201 17 18 19 202 20 21 203 22 23 204 24 25 26 205 27 28 206 29 30 31 207 32 33 208 34 35 209 36 37 38 210 39 40 211 41 42 212 43 44 45 213 46 47 214 48 49 50 215 51 52 216 53 54 217 55 56 57 218 58 59 60

Page 10 of 44

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

ACS Paragon Plus Environment

Page 11 of 44

1 2 219 3 4 220 5 6 7 221 8 9 222 10 11 12 223 13 14 224 15 16 225 17 18 19 226 20 21 227 22 23 228 24 25 26 229 27 28 230 29 30 31 231 32 33 232 34 35 233 36 37 38 234 39 40 235 41 42 236 43 44 45 237 46 47 238 48 49 50 239 51 52 240 53 54 241 55 56 57 242 58 59 60

Biochemistry

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

ACS Paragon Plus Environment

Biochemistry

1 2 243 3 4 244 5 6 7 245 8 9 246 10 11 12 247 13 14 248 15 16 249 17 18 19 250 20 21 251 22 23 252 24 25 26 253 27 28 254 29 30 31 255 32 33 256 34 35 257 36 37 38 258 39 40 259 41 42 260 43 44 45 261 46 47 262 48 49 50 263 51 52 264 53 54 265 55 56 57 266 58 59 60

Page 12 of 44

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

ACS Paragon Plus Environment

Page 13 of 44

1 2 267 3 4 268 5 6 7 269 8 9 270 10 11 12 271 13 14 272 15 16 273 17 18 19 274 20 21 275 22 23 276 24 25 26 277 27 28 278 29 30 31 279 32 33 280 34 35 281 36 37 38 282 39 40 283 41 42 284 43 44 45 285 46 47 286 48 49 50 287 51 52 288 53 54 289 55 56 57 290 58 59 60

Biochemistry

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

ACS Paragon Plus Environment

Biochemistry

1 2 291 3 4 292 5 6 7 293 8 9 294 10 11 12 295 13 14 296 15 16 297 17 18 19 298 20 21 299 22 23 300 24 25 26 301 27 28 302 29 30 31 303 32 33 304 34 35 305 36 37 38 306 39 40 307 41 42 308 43 44 45 309 46 47 310 48 49 50 311 51 52 312 53 54 313 55 56 57 314 58 59 60

Page 14 of 44

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

ACS Paragon Plus Environment

Page 15 of 44

1 2 315 3 4 316 5 6 7 317 8 9 318 10 11 12 319 13 14 320 15 16 321 17 18 19 322 20 21 323 22 23 24 324 25 26 325 27 28 326 29 30 31 327 32 33 328 34 35 36 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

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.

ACS Paragon Plus Environment

Biochemistry

1 2 329 3 4 330 5 6 7 331 8 9 332 10 11 12 333 13 14 334 15 16 335 17 18 19 336 20 21 337 22 23 338 24 25 26 339 27 28 340 29 30 31 341 32 33 342 34 35 343 36 37 38 344 39 40 345 41 42 346 43 44 45 347 46 47 348 48 49 50 349 51 52 350 53 54 351 55 56 57 352 58 59 60

Page 16 of 44

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

ACS Paragon Plus Environment

Page 17 of 44

1 2 353 3 4 354 5 6 7 355 8 9 356 10 11 12 357 13 14 358 15 16 359 17 18 19 360 20 21 361 22 23 362 24 25 26 363 27 28 364 29 30 31 365 32 33 366 34 35 367 36 37 38 368 39 40 369 41 42 370 43 44 45 371 46 47 372 48 49 50 373 51 52 374 53 54 375 55 56 57 376 58 59 60

Biochemistry

(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

ACS Paragon Plus Environment

Biochemistry

1 2 377 3 4 378 5 6 7 379 8 9 380 10 11 12 381 13 14 382 15 16 383 17 18 19 384 20 21 385 22 23 386 24 25 26 387 27 28 388 29 30 31 389 32 33 390 34 35 391 36 37 38 392 39 40 393 41 42 394 43 44 45 395 46 47 396 48 49 50 397 51 52 398 53 54 399 55 56 57 400 58 59 60

Page 18 of 44

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

ACS Paragon Plus Environment

Page 19 of 44

1 2 401 3 4 402 5 6 7 403 8 9 404 10 11 12 405 13 14 406 15 16 407 17 18 19 408 20 21 409 22 23 410 24 25 26 411 27 28 412 29 30 31 413 32 33 414 34 35 415 36 37 38 416 39 40 417 41 42 418 43 44 45 419 46 47 420 48 49 50 421 51 52 422 53 54 423 55 56 57 424 58 59 60

Biochemistry

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

ACS Paragon Plus Environment

Biochemistry

1 2 425 3 4 426 5 6 7 427 8 9 428 10 11 12 429 13 14 430 15 16 431 17 18 19 432 20 21 433 22 23 434 24 25 26 435 27 28 436 29 30 31 437 32 33 438 34 35 439 36 37 38 440 39 40 441 41 42 442 43 44 45 443 46 47 444 48 49 50 445 51 52 446 53 54 447 55 56 57 448 58 59 60

Page 20 of 44

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

ACS Paragon Plus Environment

Page 21 of 44

1 2 449 3 4 450 5 6 7 451 8 9 452 10 11 12 453 13 14 454 15 16 455 17 18 19 456 20 21 457 22 23 458 24 25 26 459 27 28 460 29 30 31 461 32 33 462 34 35 463 36 37 38 464 39 40 465 41 42 466 43 44 45 467 46 47 468 48 49 50 469 51 52 53 54 470 55 56 471 57 58 472 59 60

Biochemistry

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

Biochemistry

1 2 473 3 4 5 474 6 7 8 475 9 10 476 11 12 477 13 14 15 478 16 17 479 18 19 20 480 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 44

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.

ACS Paragon Plus Environment

Page 23 of 44

1 2 481 3 4 5 482 6 7 8 483 9 10 11 484 12 13 485 14 15 16 486 17 18 19 487 20 21 488 22 23 24 489 25 26 27 490 28 29 30 491 31 32 492 33 34 35 36 493 37 38 494 39 40 41 495 42 43 44 496 45 46 47 497 48 49 498 50 51 52 499 53 54 500 55 56 57 501 58 59 60

Biochemistry

References (1) Chubukov, V., Gerosa, L., Kochanowski, K., and Sauer, U. (2014) Coordination of microbial metabolism. Nat. Rev. Microbiol. 12, 327–340. (2) You, C., Okano, H., Hui, S., Zhang, Z., Kim, M., Gunderson, C. W., Wang, Y.-P., Lenz, P., Yan, D., and Hwa, T. (2013) Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature 1–6. (3) Ulrich, L. E., Koonin, E. V, and Zhulin, I. B. (2005) One-component systems dominate signal transduction in prokaryotes. Trends Microbiol. 13, 52–56. (4) Grüning, N.-M., Lehrach, H., Ralser, M., and Gru, N. (2010) Regulatory crosstalk of the metabolic network. Trends Biochem. Sci. 35, 220–227. (5) McFedries, A., Schwaid, A., and Saghatelian, A. (2013) Methods for the elucidation of proteinsmall molecule interactions. Chem. Biol. 20, 667–673. (6) Yang, G. X., Li, X., and Snyder, M. (2012) Investigating metabolite-protein interactions: an overview of available techniques. Methods 57, 459–466. (7) Kochanowski, K., Sauer, U., and Noor, E. (2015) Posttranslational regulation of microbial metabolism. Curr. Opin. Microbiol. 27, 10–17. (8) Visser, N. F. C., Scholten, A., van den Heuvel, R. H. H., and Heck, A. J. R. (2007) SurfacePlasmon-Resonance-Based Chemical Proteomics: Efficient Specific Extraction and Semiquantitative Identification of Cyclic Nucleotide-Binding Proteins from Cellular Lysates by Using a Combination of Surface Plasmon Resonance, Sequential Elution and . ChemBioChem 8, 298–305. (9) Gallego, O., Betts, M. J., Gvozdenovic-Jeremic, J., Maeda, K., Matetzki, C., Aguilar-Gurrieri, C.,

ACS Paragon Plus Environment

Biochemistry

1 2 502 3 4 503 5 6 7 504 8 9 10 505 11 12 506 13 14 15 16 507 17 18 508 19 20 509 21 22 23 24 510 25 26 511 27 28 512 29 30 31 32 513 33 34 514 35 36 515 37 38 39 516 40 41 42 517 43 44 518 45 46 47 519 48 49 50 520 51 52 521 53 54 55 522 56 57 523 58 59 60

Page 24 of 44

Beltran-Alvarez, P., Bonn, S., Fernández-Tornero, C., Jensen, L. J., Kuhn, M., Trott, J., Rybin, V., Müller, C. W., Bork, P., Kaksonen, M., Russell, R. B., and Gavin, A.-C. (2010) A systematic screen for protein–lipid interactions in Saccharomyces cerevisiae. Mol. Syst. Biol. 6, 430. (10) Li, X., Gianoulis, T. a., Yip, K. K. Y., Gerstein, M., and Snyder, M. (2010) Extensive In Vivo Metabolite-Protein Interactions Revealed by Large-Scale Systematic Analyses. Cell 143, 639–650. (11) Niphakis, M. J., Lum, K. M., Cognetta, A. B., Correia, B. E., Ichu, T.-A., Olucha, J., Brown, S. J., Kundu, S., Piscitelli, F., Rosen, H., and Cravatt, B. F. (2015) A Global Map of Lipid-Binding Proteins and Their Ligandability in Cells. Cell 161, 1668–1680. (12) Maeda, K., Poletto, M., Chiapparino, A., and Gavin, A.-C. (2014) A generic protocol for the purification and characterization of water-soluble complexes of affinity-tagged proteins and lipids. Nat. Protoc. 9, 2256–2266. (13) Feng, Y., De Franceschi, G., Kahraman, A., Soste, M., Melnik, A., Boersema, P. J., de Laureto, P. P., Nikolaev, Y., Oliveira, A. P., and Picotti, P. (2014) Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044. (14) Lomenick, B., Hao, R., Jonai, N., Chin, R. M., Aghajan, M., Warburton, S., Wang, J., Wu, R. P., Gomez, F., Loo, J. A., Wohlschlegel, J. a, Vondriska, T. M., Pelletier, J., Herschman, H. R., Clardy, J., Clarke, C. F., and Huang, J. (2009) Target identification using drug affinity responsive target stability (DARTS). Proc. Natl. Acad. Sci. U. S. A. 106, 21984–21989. (15) Savitski, M. M., Reinhard, F. B. M., Franken, H., Werner, T., Savitski, M. F., Eberhard, D., Molina, D. M., Jafari, R., Dovega, R. B., Klaeger, S., Kuster, B., Nordlund, P., Bantscheff, M., and Drewes, G. (2014) Tracking cancer drugs in living cells by thermal profiling of the proteome. Science (80-. ). 346, 1255784–1255784.

ACS Paragon Plus Environment

Page 25 of 44

1 2 524 3 4 525 5 6 7 8 526 9 10 527 11 12 13 528 14 15 16 529 17 18 530 19 20 21 531 22 23 24 532 25 26 533 27 28 534 29 30 31 32 535 33 34 536 35 36 537 37 38 39 538 40 41 42 539 43 44 540 45 46 47 541 48 49 50 542 51 52 53 543 54 55 544 56 57 58 59 545 60

Biochemistry

(16) Tsai, C.-J., del Sol, A., and Nussinov, R. (2008) Allostery: absence of a change in shape does not imply that allostery is not at play. J. Mol. Biol. 378, 1–11. (17) Orsak, T., Smith, T. L., Eckert, D., Lindsley, J. E., Borges, C. R., and Rutter, J. (2012) Revealing the allosterome: systematic identification of metabolite-protein interactions. Biochemistry 51, 225–232. (18) Corradini, E., Klaasse, G., Leurs, U., Heck, A. J. R., Martin, N. I., and Scholten, A. (2015) Charting the interactome of PDE3A in human cells using an IBMX based chemical proteomics approach. Mol. Biosyst. 11, 2786–2797. (19) Scholten, A., Poh, M. K., Van Veen, T. a B., Van Breukelen, B., Vos, M. a., and Heck, A. J. R. (2006) Analysis of the cGMP/cAMP interactome using a chemical proteomics approach in mammalian heart tissue validates sphingosine kinase type 1-interacting protein as a genuine and highly abundant AKAP. J. Proteome Res. 5, 1435–1447. (20) Pellecchia, M., Bertini, I., Cowburn, D., Dalvit, C., Giralt, E., Jahnke, W., James, T. L., Homans, S. W., Kessler, H., Luchinat, C., Meyer, B., Oschkinat, H., Peng, J., Schwalbe, H., and Siegal, G. (2008) Perspectives on NMR in drug discovery: a technique comes of age. Nat. Rev. Drug Discov. 7, 738–745. (21) Grutsch, S., Brüschweiler, S., and Tollinger, M. (2016) NMR Methods to Study Dynamic Allostery. PLOS Comput. Biol. 12, e1004620. (22) Mayer, M., and Meyer, B. (1999) Characterization of ligand binding by saturation transfer difference NMR spectroscopy. Angew. Chemie - Int. Ed. 38, 1784–1788. (23) Bhunia, A., Bhattacharjya, S., and Chatterjee, S. (2012) Applications of saturation transfer difference NMR in biological systems. Drug Discov. Today 17, 505–513. (24) Wagstaff, J. L., Taylor, S. L., and Howard, M. J. (2013) Recent developments and applications of ACS Paragon Plus Environment

Biochemistry

1 2 546 3 4 547 5 6 7 8 548 9 10 549 11 12 550 13 14 15 16 551 17 18 552 19 20 553 21 22 23 24 554 25 26 555 27 28 29 556 30 31 32 557 33 34 35 558 36 37 559 38 39 40 560 41 42 43 561 44 45 562 46 47 48 563 49 50 51 564 52 53 54 565 55 56 566 57 58 59 567 60

Page 26 of 44

saturation transfer difference nuclear magnetic resonance (STD NMR) spectroscopy. Mol. BioSyst. 9, 571–577. (25) Dalvit, C., Pevarello, P., Tato, M., Veronesi, M., Vulpetti, A., and Sundstrom, M. (2000) Identification of compounds with binding affinity to proteins via magnetization transfer from bulk water. J. Biomol. NMR 18, 65–68. (26) Hajduk, P. J., Olejniczak, E. T., and Fesik, S. W. (1997) One-Dimensional Relaxation- and Diffusion-Edited NMR Methods for Screening Compounds That Bind to Macromolecules. J. Am. Chem. Soc. 119, 12257–12261. (27) Fernández, C., and Jahnke, W. (2004) New approaches for NMR screening in drug discovery. Drug Discov. Today. Technol. 1, 277–283. (28) Hajduk, P. J., and Greer, J. (2007) A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discov. 6, 211–219. (29) Cala, O., Guillière, F., and Krimm, I. (2014) NMR-based analysis of protein-ligand interactions. Anal. Bioanal. Chem. 406, 943–956. (30) Silvestre, H. L., Blundell, T. L., Abell, C., and Ciulli, A. (2013) Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery. Proc. Natl. Acad. Sci. U. S. A. 110, 12984–12989. (31) Blume, A., Berger, M., Benie, A. J., Peters, T., and Hinderlich, S. (2008) Characterization of ligand binding to N-acetylglucosamine kinase studied by STD NMR. Biochemistry 47, 13138–13146. (32) Jupin, M., Michiels, P. J., Girard, F. C., Spraul, M., and Wijmenga, S. S. (2013) NMR identification of endogenous metabolites interacting with fatted and non-fatted human serum albumin in blood plasma: Fatty acids influence the HSA-metabolite interaction. J. Magn. Reson. 228, 81–94. ACS Paragon Plus Environment

Page 27 of 44

1 2 568 3 4 569 5 6 7 570 8 9 10 571 11 12 572 13 14 15 16 573 17 18 574 19 20 575 21 22 23 24 576 25 26 577 27 28 578 29 30 31 32 579 33 34 580 35 36 37 581 38 39 582 40 41 42 583 43 44 45 584 46 47 585 48 49 50 586 51 52 53 587 54 55 588 56 57 58 589 59 60

Biochemistry

(33) Chen, Y., Apolinario, E., Brachova, L., Kelman, Z., Li, Z., Nikolau, B. J., Showman, L., Sowers, K., and Orban, J. (2011) A nuclear magnetic resonance based approach to accurate functional annotation of putative enzymes in the methanogen Methanosarcina acetivorans. BMC Genomics 12, S7. (34) Shortridge, M. D., Bokemper, M., Copeland, J. C., Stark, J. L., and Powers, R. (2011) Correlation between protein function and ligand binding profiles. J. Proteome Res. 10, 2538–2545. (35) Kitagawa, M., Ara, T., Arifuzzaman, M., Ioka-Nakamichi, T., Inamoto, E., Toyonaga, H., and Mori, H. (2006) Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research. DNA Res. 12, 291–299. (36) Zaslaver, A., Bren, A., Ronen, M., Itzkovitz, S., Kikoin, I., Shavit, S., Liebermeister, W., Surette, M. G., and Alon, U. (2006) A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat. Methods 3, 623–628. (37) Link, H., Kochanowski, K., and Sauer, U. (2013) Systematic identification of allosteric proteinmetabolite interactions that control enzyme activity in vivo. Nat. Biotechnol. 31, 357–361. (38) Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., Datsenko, K. a, Tomita, M., Wanner, B. L., and Mori, H. (2006) Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008. (39) Gossert, A. D., Henry, C., Blommers, M. J. J., Jahnke, W., and Fernández, C. (2009) Time efficient detection of protein-ligand interactions with the polarization optimized PO-WaterLOGSY NMR experiment. J. Biomol. NMR 43, 211–217. (40) Schoner, R., and Herrmann, K. M. (1976) 3-Deoxy-D-arabino-heptulosonate 7-phosphate synthase. Purification, properties, and kinetics of the tyrosine-sensitive isoenzyme from Escherichia coli. J. Biol. Chem. 251, 5440–5447.

ACS Paragon Plus Environment

Biochemistry

1 2 590 3 4 591 5 6 7 592 8 9 593 10 11 12 594 13 14 15 595 16 17 596 18 19 20 597 21 22 23 598 24 25 599 26 27 28 600 29 30 31 601 32 33 602 34 35 36 603 37 38 39 604 40 41 42 605 43 44 606 45 46 47 607 48 49 50 608 51 52 609 53 54 55 610 56 57 58 611 59 60

Page 28 of 44

(41) Keseler, I. M., Collado-Vides, J., Santos-Zavaleta, A., Peralta-Gil, M., Gama-Castro, S., MuñizRascado, L., Bonavides-Martinez, C., Paley, S., Krummenacker, M., Altman, T., Kaipa, P., Spaulding, A., Pacheco, J., Latendresse, M., Fulcher, C., Sarker, M., Shearer, A. G., Mackie, A., Paulsen, I., Gunsalus, R. P., and Karp, P. D. (2011) EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res. 39, D583–D590. (42) Meyer, B., and Peters, T. (2003) NMR spectroscopy techniques for screening and identifying ligand binding to protein receptors. Angew. Chemie - Int. Ed. 42, 864–890. (43) Bennett, B. D., Kimball, E. H., Gao, M., Osterhout, R., Van Dien, S. J., and Rabinowitz, J. D. (2009) Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599. (44) van Dongen, M. J. P., Uppenberg, J., Svensson, S., Lundbäck, T., Åkerud, T., Wikström, M., and Schultz, J. (2002) Structure-Based Screening As Applied to Human FABP4: A Highly Efficient Alternative to HTS for Hit Generation. J. Am. Chem. Soc. 124, 11874–11880. (45) Shortridge, M. D., Hage, D. S., Harbison, G. S., and Powers, R. (2008) Estimating protein-ligand binding affinity using high-throughput screening by NMR. J. Comb. Chem. 10, 948–958. (46) Mayer, M., and Meyer, B. (2001) Group Epitope Mapping by Saturation Transfer Difference NMR To Identify Segments of a Ligand in Direct Contact with a Protein Receptor. J. Am. Chem. Soc. 123, 6108–6117. (47) Xu, Y.-F., Amador-Noguez, D., Reaves, M. L., Feng, X.-J., and Rabinowitz, J. D. (2012) Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase. Nat. Chem. Biol. 8, 562–568. (48) Bar-Even, A., Noor, E., Savir, Y., Liebermeister, W., Davidi, D., Tawfik, D. S., and Milo, R.

ACS Paragon Plus Environment

Page 29 of 44

1 2 612 3 4 613 5 6 7 8 614 9 10 615 11 12 616 13 14 15 16 617 17 18 19 618 20 21 619 22 23 24 620 25 26 621 27 28 29 622 30 31 32 623 33 34 624 35 36 37 625 38 39 626 40 41 42 627 43 44 45 628 46 47 629 48 49 50 630 51 52 53 631 54 55 632 56 57 58 633 59 60

Biochemistry

(2011) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410. (49) Jahnke, W., Floersheim, P., Ostermeier, C., Zhang, X., Hemmig, R., Hurth, K., and Uzunov, D. P. (2002) NMR reporter screening for the detection of high-affinity ligands. Angew. Chem. Int. Ed. Engl. 41, 3420–3423. (50) Moran, U., Phillips, R., and Milo, R. (2010) SnapShot: key numbers in biology. Cell 141, 1–2. (51) Mensonides, F. I. C., Bakker, B. M., Cremazy, F., Messiha, H. L., Mendes, P., Boogerd, F. C., and Westerhoff, H. V. (2013) A new regulatory principle for in vivo biochemistry: Pleiotropic low affinity regulation by the adenine nucleotides - Illustrated for the glycolytic enzymes of Saccharomyces cerevisiae. FEBS Lett. 587, 2860–2867. (52) Lasserre, J. P., Beyne, E., Pyndiah, S., Lapaillerie, D., Claverol, S., and Bonneu, M. (2006) A complexomic study of Escherichia coli using two-dimensional blue native/SDS polyacrylamide gel electrophoresis. Electrophoresis 27, 3306–3321. (53) Castello, A., Fischer, B., Eichelbaum, K., Horos, R., Beckmann, B. M., Strein, C., Davey, N. E., Humphreys, D. T., Preiss, T., Steinmetz, L. M., Krijgsveld, J., and Hentze, M. W. (2012) Insights into RNA Biology from an Atlas of Mammalian mRNA-Binding Proteins. Cell 149, 1393–1406. (54) Ray, J. M., Yanofsky, C., and Bauerle, R. (1988) Mutational analysis of the catalytic and feedback sites of the tryptophan-sensitive 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase of Escherichia coli. J. Bacteriol. 170, 5500–5506. (55) Dalvit, C., Fogliatto, G., Stewart, A., Veronesi, M., and Stockman, B. (2001) WaterLOGSY as a method for primary NMR screening: Practical aspects and range of applicability. J. Biomol. NMR 21, 349–359.

ACS Paragon Plus Environment

Biochemistry

1 2 634 3 4 635 5 6 7 8 636 9 10 637 11 12 638 13 14 15 16 639 17 18 640 19 20 641 21 22 23 642 24 25 26 643 27 28 644 29 30 31 32 645 33 34 646 35 36 647 37 38 39 648 40 41 42 649 43 44 650 45 46 47 651 48 49 50 652 51 52 53 653 54 55 654 56 57 58 59 655 60

Page 30 of 44

(56) Antanasijevic, A., Ramirez, B., and Caffrey, M. (2014) Comparison of the sensitivities of WaterLOGSY and saturation transfer difference NMR experiments. J. Biomol. NMR 60, 37–44. (57) Jayalakshmi, V., and Krishna, N. R. (2005) Determination of the conformation of trimethoprim in the binding pocket of bovine dihydrofolate reductase from a STD-NMR intensity-restrained CORCEMA-ST optimization. J. Am. Chem. Soc. 127, 14080–14084. (58) Kemper, S., Patel, M. K., Errey, J. C., Davis, B. G., Jones, J. A., and Claridge, T. D. W. (2010) Group epitope mapping considering relaxation of the ligand (GEM-CRL): including longitudinal relaxation rates in the analysis of saturation transfer difference (STD) experiments. J. Magn. Reson. 203, 1–10. (59) Vogtherr, M., and Peters, T. (2000) Application of NMR Based Binding Assays to Identify Key Hydroxy Groups for Intermolecular Recognition. J. Am. Chem. Soc. 122, 6093–6099. (60) Wagstaff, J. L., Vallath, S., Marshall, J. F., Williamson, R. A., and Howard, M. J. (2010) Twodimensional heteronuclear saturation transfer difference NMR reveals detailed integrin αvβ6 proteinpeptide interactions. Chem. Commun. (Camb). 46, 7533–7535. (61) Dalvit, C., Flocco, M., Stockman, B. J., and Veronesi, M. (2002) Competition Binding Experiments for Rapidly Ranking Lead Molecules for their Binding Affinity to Human Serum Albumin. Comb. Chem. High Throughput Screen. 5, 645–650. (62) Dalvit, C., Ardini, E., Fogliatto, G. P., Mongelli, N., and Veronesi, M. (2004) Reliable highthroughput functional screening with 3-FABS. Drug Discov. Today 9, 595–602. (63) Manzenrieder, F., Frank, A. O., and Kessler, H. (2008) Phosphorus NMR spectroscopy as a versatile tool for compound library screening. Angew. Chem. Int. Ed. Engl. 47, 2608–2611. (64) Wang, H. H., Huang, P. Y., Xu, G., Haas, W., Marblestone, A., Li, J., Gygi, S. P., Forster, A. C., ACS Paragon Plus Environment

Page 31 of 44

1 2 656 3 4 657 5 6 7 8 658 9 10 659 11 12 13 660 14 15 16 661 17 18 19 662 20 21 663 22 23 24 664 25 26 27 665 28 29 30 666 31 32 33 667 34 35 668 36 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

Jewett, M. C., and Church, G. M. (2012) Multiplexed in vivo his-tagging of enzyme pathways for in vitro single-pot multienzyme catalysis. ACS Synth. Biol. 1, 43–52. (65) Brocchieri, L., and Karlin, S. (2005) Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res. 33, 3390–3400. (66) Shuker, S. B., Hajduk, P. J., Meadows, R. P., and Fesik, S. W. (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274, 1531–1534. (67) Oliveira, A. P., and Sauer, U. (2012) The importance of post-translational modifications in regulating Saccharomyces cerevisiae metabolism. FEMS Yeast Res. 12, 104–117. (68) Takeda, S., Miyauchi, S., Nakayama, H., and Kamo, N. (1997) Adenosine 5′-triphosphate binding to bovine serum albumin. Biophys. Chem. 69, 175–183.

ACS Paragon Plus Environment

Biochemistry

1 2 669 3 4 5 670 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 671 40 41 672 42 43 44 673 45 46 674 47 48 675 49 50 51 52 676 53 54 55 56 57 58 59 60

Page 32 of 44

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.

ACS Paragon Plus Environment

Page 33 of 44

1 2 677 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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

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

ACS Paragon Plus Environment

Biochemistry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 678 34 35 679 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 44

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

ACS Paragon Plus Environment

Page 35 of 44

1 2 680 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 681 23 24 25 682 26 27 28 683 29 30 684 31 32 685 33 34 35 686 36 37 687 38 39 688 40 41 42 689 43 44 690 45 46 47 48 691 49 50 51 52 53 54 55 56 57 58 59 60

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.

ACS Paragon Plus Environment

Biochemistry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 692 20 21 693 22 23 694 24 25 26 695 27 28 696 29 30 697 31 32 33 698 34 35 699 36 37 38 700 39 40 701 41 42 702 43 44 45 703 46 47 704 48 49 50 705 51 52 53 54 55 56 57 58 59 60

Page 36 of 44

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.

ACS Paragon Plus Environment

Page 37 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 706 38 707 39 40 41 708 42 43 709 44 45 46 710 47 48 711 49 50 712 51 52 53 713 54 55 714 56 57 715 58 59 60

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-

ACS Paragon Plus Environment

Biochemistry

1 2 716 3 4 717 5 6 7 718 8 9 719 10 11 12 720 13 14 721 15 16 722 17 18 19 20 723 21 22 724 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 44

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.

ACS Paragon Plus Environment

Page 39 of 44

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).

ACS Paragon Plus Environment

Biochemistry

1 2 732 3 4 5 733 6 7 734 8 9 10 735 11 12 13 14 15 16 17 18 19 20 736 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 44

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

ACS Paragon Plus Environment

Page 41 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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

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

ACS Paragon Plus Environment

Biochemistry

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

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

ACS Paragon Plus Environment

Page 42 of 44

Page 43 of 44

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 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

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

ACS Paragon Plus Environment

Biochemistry

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

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

ACS Paragon Plus Environment

Page 44 of 44