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Systematic mapping of protein–metabolite interactions with mass spectrometry-based techniques
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ScienceDirect Systematic mapping of protein–metabolite interactions with mass spectrometry-based techniques Shanshan Li1 and Wenqing Shui1,2 The recent rapid advance of systematic mapping of protein– metabolite interactions (PMIs) in both prokaryotic and eukaryotic cells has been catalyzed by development of innovative and effective proteomics or metabolomics strategies all based on large-scale mass spectrometry (MS) analysis of biomolecules. Both metabolite-centric and proteincentric approaches have been established to profile PMIs in the native cellular matrix treated by specific metabolites or proteins. Here we will review the development and application of versatile MS-based proteomics and metabolomics techniques for global PMI mapping in different species, which lead to the discovery of numerous uncharacterized PMIs that may reveal new interaction-derived functionality. We further discuss the strengths and limitations of different PMI mapping approaches as well as the key elements in MS quantification and data mining for reliable PMI identification. Addresses 1 iHuman Institute, ShanghaiTech University, 201210, Shanghai, China 2 School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China Corresponding author: Shui, Wenqing ([email protected])
Current Opinion in Biotechnology 2020, 64:24–31 This review comes from a themed issue on Analytical biotechnology Edited by Yinjie Tang and Ludmilla Aristilde
https://doi.org/10.1016/j.copbio.2019.09.002 0958-1669/ã 2019 Published by Elsevier Ltd.
It has been postulated that millions of functionally relevant PMIs may occur in bacterial cells, which composes equivalent complexity, if not more, compared to protein–protein interactions (PPIs) on the proteome scale [3,4]. However, systematic mapping of PMIs in model organisms has substantially lagged behind that of PPIs. Up till now, the most comprehensive PMI analysis resulted in identification of 1678 interaction pairs between 20 designated metabolites and cellular proteins in Escherichia coli whereas a network of more than 56 000 putative PPIs from over 10 000 proteins was constructed in HEK293T cells [4,5]. Technical hurdles in large-scale mapping of intracellular PMIs mainly reside in: 1) difficulty of detecting low-affinity and transient PMIs, 2) difficulty of installing an affinity handle to metabolites without disrupting relevant PMIs, 3) in vitro purification not feasible for certain proteins especially full-length transmembrane proteins, 4) no universal analytical methods available for detection of metabolites of diverse chemical structures. Nevertheless, the rapid advance of mass spectrometry (MS)-based proteomics and metabolomics technology [6,7] has been constantly promoting systematic profiling of PMIs especially in model microorganisms. A recent review presents an excellent overview of MS-based and several other biophysical or computational approaches for regulatory PMI discovery [8]. Given that versatile MSbased methods have been developed for biomolecular interaction analysis, here we will review recent progress in exploiting proteomic or metabolomic strategies for systematic PMI mapping. We will discuss the strengths and limitations of different PMI mapping approaches and the key elements in MS quantification and data mining for reliable PMI identification.
General strategies for PMI mapping Introduction Molecular recognition between proteins and small molecule metabolites plays an essential role in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can be all mediated by protein–metabolite interactions (PMIs), thus connecting cellular metabolism to genetic/epigenetic regulation, environmental sensing and signal transduction [1,2]. Apart from directly binding to the active or orthosteric sites of their native cognate proteins, metabolites are known to interact with different allosteric sites, allowing additional specific tuning of the structure and function of proteins and macromolecular protein assemblies. Current Opinion in Biotechnology 2020, 64:24–31
As pointed out by previous reviews [8,9], PMI mapping strategies are divided into two categories: metabolitecentric and protein-centric (Figure 1). Metabolite-centric PMI mapping aims to identify all protein partners from a cell lysate that potentially associate with a given metabolite ligand, which heavily relies on proteomics techniques. On the other side, the protein-centric strategy identifies endogenous metabolites (usually in specific chemical classes) that potentially bind to a given protein target, which mainly employs metabolomics techniques.
Metabolite-centric PMI mapping Enrich interacting proteins with bifunctional ligands
By dosing a chemically functionalized compound into cell lysates, ligand-binding proteins can be conveniently enriched www.sciencedirect.com
MS-based PMI mapping Li and Shui 25
LC-MS/MS
RT
Limited proteolysis
Intensity
Metabolite
Structural proteomics
LC-MS/MS
Metabolite-centric
UV light
Chemical proteomics
Enrichment Functionalized metabolite
Intensity
Figure 1
Enrichment
RT
Protein-centric
Protein
Untargeted metabolomics
LC-MS/MS
Intensity
RT
Current Opinion in Biotechnology
Systematic mapping of PMIs with metabolite-centric or protein-centric strategies. In metabolite-centric experiments, the cellular proteome extracts are treated with specific functionalized metabolites (upper) or label-free metabolites (middle) and subjected to proteomic analysis. Ligand-bound proteins are identified through affinity enrichment with the bifunctional ligand (chemical proteomics) or monitoring ligand-induced changes of protein structural characteristics (structural proteomics) such as proteolysis susceptibility as shown here. In protein-centric experiments, ligandbound proteins are isolated from the cellular metabolome extracts and associated ligands are typically identified using an untargeted metabolomics workflow (lower). Peptide or metabolite features from the ligand-bound proteins are annotated by red asterisks with signals from the metabolite/protein-treated samples represented by solid lines and controls by dashed lines.
and then identified through a chemical proteomics workflow. Notably, only installing an affinity handle to the ligand structure does not convey sufficient sensitivity and specificity for PMI mapping due to the weak and promiscuous binding of metabolites to many proteins [9]. A more effective way is to introduce bifunctional modifications to the ligand, one photoreactive group that converts weak PMIs to covalent ligand binding, the other clickable group for attachment of a purification tag to the ligand (Figure 1). Such bifunctional probes have been developed for multiple lipid molecules to map cellular protein–lipid interactions. For example, 265 putative cholesterol binding proteins were identified from HeLa cells using a trans-sterol probe [10]. The same group later designed a set of chemical proteomic probes to chart the global landscape of protein–lipid interactions in mammalian cells [11]. A total of 803 and 678 protein targets were identified using two arachidonoyl lipid probes respectively, and large portions of them are enzymes or transporters involved in lipid metabolism or trafficking (Table 1). It should be mentioned that most of the PMI mapping experiments with bifunctional probes adopted several controls including no UV irradiation, treatment with a control probe, and competition with the free ligand to filter out false positive protein hits [10,11]. www.sciencedirect.com
Apart from mapping protein–lipid interactions, application of this chemical proteomics strategy has recently been expanded to identification of proteins targeted by baicalin, a natural product in the flavonoid family (Table 1) [12]. Out of the 142 putative protein targets enriched by a flavonoid probe, an enzyme mediating fatty acid oxidation was found to be activated by the natural product through allosteric modulation. Present limitations for using bifunctional ligands are interference with ligand binding property and function as a result of chemical functionalization and the need of custom synthesis of ligand-derived probes. Monitor ligand-induced effects on protein structural characteristics
In order to address the challenge of mapping PMIs for non-functionalizable ligands, structural proteomics approaches have been developed to eliminate the need of ligand modification [8]. They rely on monitoring the changes of certain protein structural characteristics induced by specific ligand treatment. As ligand binding usually changes the protein structural stability, differential susceptibility to protease cleavage occurs on the Current Opinion in Biotechnology 2020, 64:24–31
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Table 1 List of exemplary PMI mapping studies with different strategies Approach Metabolite-centric Chemical proteomics Sterol probes [10] Lipid probes [11] Flavonoid probe [12]
Species
Human Human, mouse Human
Loaded metabolites or proteins
PMIs identified
MS quan method
Candidate selection cutoffs a
Cholesterol Ararchidonolyl lipids Baicalin
265 678, 803 142
SILAC SILAC SILAC
FC 5.0 FC 3.0 FC 4.0 FC ＞ 2, Q ＜ 0.01
Structural proteomics LiP-SMap [4]
E. coli
20 metabolites
1678 (1447) b
SPROX [15] CETSA/TPP [17]
S. cerevisiae Human
ATP ATP c
139 (102) b 213
Label-free, DDA and DIA SILAC TMT
CETSA/TPP [20]
Human
ATP c
38 d
TMT
FC ＞ 1.7, P ＜ 0.01 Curve fitting R2 ＞ 0.8, delta Tm P ＜ 0.05 The same as above
138 metabolite standards Herbal extracts
5 enzymes
29 (13) b
Label-free, HRMS
FC > 2.0
2 viral proteins
20–30
Label-free, HRMS
FC ＞ 2.0, P < 0.05
S. cerevisiae
21 metabolic enzymes + 103 kinases 1275 proteins NAe
80
Label-free, HRMS
485 (445) b 81
Label-free, HRMS Label-free, HRMS
FC 3.0 or 5.0, P < 0.05 |Z-score| > 5 No quantitative cutofff
Protein-centric Dialysis-GC/LC–MS [27] Affinity MS and metabolomics [28] Affinity isolation and metabolomics [31] Untargeted metabolomics [34] SEC and metabolomics [35]
E. coli Arabidopsis thaliana
a
The major statistical criteria for candidate selection are summarized. FC refers to fold-change measured on proteins or metabolites in sample groups versus controls. b The number of new PMIs reported in that study is indicated in brackets. c These studies also profiled protein targets for several drugs and only results of ATP-binding protein identification are summarized. d Only membrane proteins are counted. e No protein overloading in this study. f Endogenous metabolites bound to native proteins are selected based on identification only in the sample group and not in the control.
protein targets upon binding of a ligand. LiP-small molecule mapping (LiP-SMap) is a workflow that combines limited proteolysis by a broad-specificity protease and quantitative proteomics to detect proteins with altered proteolytic patterns as putative metabolite-binding proteins (Figure 1) [4]. Initially developed for global analysis of protein conformational changes in yeast cells during metabolic transition [13], LiP-SMap has been tailored to systematic PMI mapping in the native cellular matrix of E. coli. Overall, 1678 PMI events were identified from the bacterial total proteome extract incubated with 20 selected metabolites, which represents the largest reported metabolite–protein interactome containing more than 1400 previously unknown PMIs (Table 1). This study also documented more than 7000 putative binding sites in the identified metabolite-binding proteins [4]. The change of protein stability under ligand treatment can be also monitored by proteome-wide thermodynamic analysis with two techniques. SPROX (stability of proteins from rates of oxidation) measures the shift of protein transition midpoints during chemical denaturant-induced unfolding [14]. A representative work is the identification Current Opinion in Biotechnology 2020, 64:24–31
of 139 yeast proteins possibly interacting with ATP in the native cell extract [15] (Table 1). As SPROX requires detection of methionine-containing peptides for monitoring protein-unfolding reactions, the low frequency of methionine in prokaryotic and eukaryotic proteomes (2.5%) has limited the number of proteins that can be assessed with this method. The second thermodynamics-based technique is thermal proteome profiling (TPP) which is a combination of the cellular thermal shift assay (CESTA) and quantitative proteomics [16,17]. In this assay, ligand-binding proteins are distinguished by an increased apparent melting temperature upon thermal denaturation [18]. TPP offers a higher throughput than SPROX by typically assessing 3000–7000 proteins that have their melting curves determined. TPP has been developed for large-scale mapping of both metabolite and drug interactions with the whole-cell proteome [17]. Notably, TPP has been implemented to map protein–drug interactions in not only cell lysates but also live cells by adding the compound to cell culture before performing cell lysis [19,20]. However, live cell treatment may not be necessary for PMI mapping as most metabolites are synthesized and trafficking intracellularly and many of them are not membrane-permeable. www.sciencedirect.com
MS-based PMI mapping Li and Shui 27
ATP-binding proteins were also extensively profiled by TPP which resulted in identification of 213 interacting proteins from whole-cell lysates [17] and 38 interacting membrane proteins from the membrane extracts of human cells [20] (Table 1). It is noteworthy that the above approaches measuring either proteolysis susceptibility or thermodynamic transition could generate false negatives for those PMIs not significantly affecting protein stability, or false positives due to conformational changes of non-target proteins as a downstream functional consequence of specific PMIs. Proteomic techniques used in metabolite-centric PMI mapping
Accurate and consistent proteomic quantification is vital for distinguishing true metabolite-binding proteins from the highly complex proteome background. Different MS quantification techniques have been integrated with
specific PMI mapping approaches (Figure 2, Table 1). Chemical proteomic approaches with bifunctional ligands almost all employ Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) for precise measurement of protein abundance changes [10,11,12]. Yet SILAC has limited throughput (analyzing 2–3 samples in one set) and the sample type is mainly restricted to cultured cell lines. Both TPP and SPROX choose an isobaric chemical labeling method (tandem mass tags, TMT) which allows multiplexing analysis of proteomic variation across 10 samples [14,16,17,19,20]. This quantification technique is best suited for acquisition of complete protein melting curves at a series of conditions. However, isobaric chemical labeling-based quantification suffers from a small dynamic range, which makes it difficult to distinguish relatively small magnitude of ligand-induced thermodynamic transition. SPROX was also combined with SILAC to measure the difference of protein folding/
Figure 2
+ Metabolite Control
+UV light LC-MS/MS
Proteolysis
Metabolite
SILAC
Enrichment
Intensity
Metabolite Heavy isotope
m/z
-UV light
Light isotope
Fraction non-denatured
LC-MS/MS
Intensity
TMT labeling
+ Metabolite T1 T2... T10
m/z
Limited proteolysis
500 475 450 425
MS2 Intensity
m/z
DIA Metabolite
DIA Isolation window
1175 . . . 625
Control + Metabolite
400 Cycle time
Retention time
RT
Data-independent acquisition
1200
Temperature
Isobartic chemical labeling
+ Metabolite Control
Current Opinion in Biotechnology
Major MS quantification techniques integrated with different PMI mapping approaches. SILAC is most often coupled to the metabolite-centric chemical proteomics workflows (upper). Ligand-bound proteins are distinguished by significant upregulation in the bifunctional ligand-treated samples (heavy isotope labeled as shown here) versus the control. Isobaric chemical labeling is widely employed in the metabolite-centric structural proteomics workflows (middle). The thermal melting curves of various proteins are derived from multiplexing proteomic quantification with TMT labeling. Ligand-bound proteins are distinguished by increased melting temperature. Data-independent acquisition (DIA) has been employed to quantify peptide abundance changes during limited proteolysis of proteins upon metabolite treatment. Ligand-bound proteins are distinguished by altered proteolytic patterns in the presence of a given metabolite. DIA ion map was adapted from publication Methods Mol Biol. 2017, 1550:289–307. www.sciencedirect.com
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unfolding extent in the absence or presence of a test ligand whereas the full melting curves were determined in a label-free manner [15]. Surprisingly, application of two types of thermodynamics-based approaches, SPROX with isobaric chemical labeling and pulse proteolysis with SILAC, gave rise to distinct sets of protein targets with very small overlap, indicating that different structural proteomics workflows may yield different PMI identifications for the same ligand [21]. The most recent PMI mapping study with the LiP-SMap approach adopted a different and more flexible quantification workflow integrating shotgun proteomic analysis and label-free dataindependent acquisition (DIA) [4]. DIA quantification has combined the strengths of shotgun proteomics and targeted proteomics to achieve high throughput, high sensitivity, high quantification consistency and a broad dynamic range [22]. We expect this quantification technique will gain more prevalence in future MS-based PMI investigation. It is worth mentioning that in the above representative PMI mapping studies, the criteria for selection of potential metabolite-interacting proteins have certain flexibility (Table 1). Even those using the same mapping strategy would define different cut-offs and statistical parameters. It is essential to include positive and negative controls in the experimental design in order to validate specific PMI selection criteria.
Protein-centric PMI mapping Capture interacting metabolites with purified proteins
Small molecule ligands associated with a protein of interest can be captured through protein isolation from a complex matrix and protein-bound ligands are then identified by LC–MS analysis of the compound mixture. This strategy termed affinity MS or affinity selection MS (AS-MS) has been widely implemented to highthroughput ligand screening from synthetic compound libraries towards diverse protein targets [23,24,25,26]. Protein-centric PMI mapping adopts the same strategy in most cases to screen endogenous metabolite ligands from native cellular matrix for a given protein. Multiple biochemical methods have been established to isolate ligand-bound protein complexes either from metabolite standard mixtures or cell extracts. Equilibrium dialysis of individual purified recombinant proteins against a 138metabolite mixture coupled to targeted MS analysis of interacting metabolites identified 29 PMIs for five enzymes in central carbon metabolism [27] (Table 1). Ultrafiltration is equally effective in separating protein complexes from free ligands and it is most often applied to discovery of small molecule ligands from crude extracts of natural products, especially Traditional Chinese Medicine (TCM), for purified protein targets [28,29,30]. Since natural product extracts consist of a large number of small molecule metabolites, an untargeted metabolomics workflow has been Current Opinion in Biotechnology 2020, 64:24–31
combined with affinity MS for efficient discovery of chemical ligands from TCM for viral protein targets [28] (Table 1). Furthermore, virtual screen was integrated with affinity MS-based experimental screen to identify ligands with higher confidence from several TCM crude extracts that target a specific viral nucleoprotein [29]. These studies have demonstrated the great potential of PMI screening in natural products for discovery of functional small molecule ligands. The most comprehensive protein-centric PMI mapping was the identification of extensive hydrophobic metabolite interactions with 16 lipid biosynthetic enzymes and 21 kinases in yeast to reveal the crosstalk between lipid metabolism and kinase activity regulation [31] (Table 1). This work involved overexpression of more than 100 proteins, affinity isolation of each protein from yeast cell lysates, and extraction of protein-bound metabolites for untargeted profiling. As pointed out by the authors, this PMI profiling strategy is biased against proteins that cannot be readily purified, and it is also limited by the scope of metabolites that can be assayed with a defined LC–MS method [31]. Likewise, affinity immobilized protein targets were incubated with lipophilic metabolite extracts from animal tissues for identification of fatty acids interacting with specific nuclear receptors [32,33]. Given that functional PMIs between enzymes and their substrates lead to enzymatic reactions, monitoring the accumulation or depletion of metabolites in a cellular metabolome extract treated with overexpressed or purified proteins would uncover the enzyme-substrate/product connections. Using a untargeted metabolomics approach, a recent study screened 1275 functionally uncharacterized E. coli proteins and discovered 241 potential novel enzymes associated with at least one annotated metabolite [34] (Table 1). Because this approach monitors metabolite abundance changes instead of metabolite binding to the protein, it is not suited for discovery of allosteric modulators or PMIs for non-enzyme proteins. Native PMI identification without overloading
All the above strategies may cause artificial or biased PMIs due to overloading purified proteins or metabolite standards into the cellular metabolome or proteome extract. An ideal experiment would be to assay native PMIs in the cellular environment without any overloading. Preliminary efforts have been made in this direction by separating native protein complexes with free metabolites meanwhile detecting metabolites co-fractionating with proteins [35]. A total of 81 endogenous polar metabolites were identified to be associated with native proteins in the Arabidopsis cell lysates but these metabolite-binding proteins were not deconvoluted (Table 1). If precisely quantifying both the metabolites and proteins present in each protein complex fraction separated by non-denaturing www.sciencedirect.com
MS-based PMI mapping Li and Shui 29
chromatography, one could delineate numerous native PMIs through correlative analysis akin to protein– protein interactome mapping through protein complex correlation profiling [36,37]. We envision that this proposed new strategy may lead to discovery of novel PMIs not revealed by previous studies under overloading conditions. Metabolomics techniques used in PMI mapping
Small molecule quantification in most PMI mapping studies relies on label-free high-resolution MS (HRMS) analysis (Table 1). The acquired metabolomics dataset is typically processed in an untargeted manner which allows detection and quantification of tens of thousands of small molecule features in the LC-HRMS data [38,39]. Proteinbound metabolites are identified from differential features that are significantly enriched in the protein incubation sample versus control. However, several studies have reported considerable differences in feature detection and differential marker selection from the same metabolomics dataset using different untargeted data processing software [40,41]. Combined usage of multiple software tools is recommended to increase the confidence of differential marker identification [41]. Of note, previous PMI mapping studies employed different data processing packages and different data filtering cutoffs for selection of potential PMI pairs (Table 1). Another important challenge in data mining is structural annotation of the differential metabolic features. Without using standards, most metabolomics studies assign the putative metabolite structures based on accurate mass measurement and matching experimental MSMS spectra with public spectral libraries [42,43]. Because of the limited source of high-quality metabolite spectral libraries and ambiguity of isomeric compound identification by MSMS analysis, the structures of many differential features found in untargeted metabolomics cannot be confidently assigned or even falsely assigned [42]. Taken together, it is highly preferred to validate the identified PMI pairs first using purified proteins and metabolite standards before functional evaluation.
Conclusion Technical breakthroughs in MS-based proteomics and metabolomics have substantially propelled global mapping of biomolecular interaction networks. Although it becomes increasingly easy to identify several thousands of PMIs from one experiment, one has to be cautious in interpreting the data and selecting candidates for functionality assessment. As discussed in this review, false positive and false negative identification of PMIs are almost inevitable in large-scale profiling experiments using different mapping strategies, MS quantification techniques and data mining workflows. Thus, it is necessary to validate novel PMIs with orthogonal approaches such as biophysical methods for protein–ligand interaction characterization [44]. Alternatively, different PMI www.sciencedirect.com
mapping approaches such as those using bifunctional ligands or based on monitoring protein thermodynamic transition can be applied together to validate results and narrow down PMIs of possible functional importance. All previous studies have documented PMI networks in the whole cell at an equilibrium state. One of the future directions would be to explore temporal dynamics and spatial organization of PMIs in different subcellular compartments. With the growing interest in PMI mapping in different organisms, now it is desirable to build a highquality PMI database similar to the CORUM database of experimentally characterized protein complexes [45]. Although the BRENDA database includes thousands of regulatory enzyme–ligand interactions mostly present in E. coli and Saccharomyces cerevisae [46], a comprehensive PMI database for various proteins beyond enzymes in different species is still lacking. This expanding PMI landscape could be integrated with other functional omics data such as PPI networks, protein post-translational modifications or metabolic fluxes to bring new insights into the crosstalk between different layers of cellular regulation. Such comprehensive knowledge would possibly open new avenues for engineering microbial metabolic networks to improve biochemical productivity.
Conflict of interest statement Nothing declared.
Acknowledgements We thank ShanghaiTech University, the National Key Research and Development Program of China (No. 2018YFA0507004) and the National Natural Science Foundation of China (No. 31971362) for funding.
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annotation of reaction pairs for enzymes, leading to the discovery of potential novel enzymes in E. coli. 35. Veyel D, Kierszniowska S, Kosmacz M, Sokolowska EM, Michaelis A, Luzarowski M, Szlachetko J, Willmitzer L, Skirycz A: System-wide detection of protein-small molecule complexes suggests extensive metabolite regulation in plants. Sci Rep 2017, 7:42387. The first report of identification of endogenous metabolites associated with native proteins in plant cells with no overloading, which is based on co-fractionating protein–ligand complexes. 36. Rudashevskaya EL, Sickmann A, Markoutsa S: Global profiling of protein complexes: current approaches and their perspective in biomedical research. Expert Rev Proteomics 2016, 13:951-964. 37. Heusel M, Bludau I, Rosenberger G, Hafen R, Frank M, BanaeiEsfahani A, van Drogen A, Collins BC, Gstaiger M, Aebersold R: Complex-centric proteome profiling by SEC-SWATH-MS. Mol Syst Biol 2019, 15:e8438. 38. Johnson CH, Ivanisevic J, Siuzdak G: Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 2016, 17:451-459. 39. Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, McLean JA: Untargeted metabolomics strategies-challenges and emerging directions. J Am Soc Mass Spectrom 2016, 27:1897-1905. 40. Myers OD, Sumner SJ, Li S, Barnes S, Du X: Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Anal Chem 2017, 89:8689-8695.
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41. Li Z, Lu Y, Guo Y, Cao H, Wang Q, Shui W: Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection. Anal Chim Acta 2018, 1029:50-57. This study acquired a benchmark dataset to evaluate five software packages widely used in untargeted metabolomics in regard to feature detection, quantification accuracy and selection of markers of differential abundances. 42. Chaleckis R, Meister I, Zhang P, Wheelock CE: Challenges, progress and promises of metabolite annotation for LC-MSbased metabolomics. Curr Opin Biotechnol 2019, 55:44-50. 43. Tsugawa H: Advances in computational metabolomics and databases deepen the understanding of metabolisms. Curr Opin Biotechnol 2018, 54:10-17. 44. Yang GX, Li X, Snyder M: Investigating metabolite-protein interactions: an overview of available techniques. Methods 2012, 57:459-466. A good review of the available in vitro biophysical methods, in silico and in vivo technologies for mapping or characterizing small molecule–protein interactions. 45. Giurgiu M, Reinhard J, Brauner B, Dunger-Kaltenbach I, Fobo G, Frishman G, Montrone C, Ruepp A: CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res 2019, 47:D559-D563. 46. Jeske L, Placzek S, Schomburg I, Chang A, Schomburg D: BRENDA in 2019: a European ELIXIR core data resource. Nucleic Acids Res 2019, 47:D542-D549.
Current Opinion in Biotechnology 2020, 64:24–31
ScienceDirect Systematic mapping of protein–metabolite interactions with mass spectrometry-based techniques Shanshan Li1 and Wenqing Shui1,2 The recent rapid advance of systematic mapping of protein– metabolite interactions (PMIs) in both prokaryotic and eukaryotic cells has been catalyzed by development of innovative and effective proteomics or metabolomics strategies all based on large-scale mass spectrometry (MS) analysis of biomolecules. Both metabolite-centric and proteincentric approaches have been established to profile PMIs in the native cellular matrix treated by specific metabolites or proteins. Here we will review the development and application of versatile MS-based proteomics and metabolomics techniques for global PMI mapping in different species, which lead to the discovery of numerous uncharacterized PMIs that may reveal new interaction-derived functionality. We further discuss the strengths and limitations of different PMI mapping approaches as well as the key elements in MS quantification and data mining for reliable PMI identification. Addresses 1 iHuman Institute, ShanghaiTech University, 201210, Shanghai, China 2 School of Life Science and Technology, ShanghaiTech University, 201210, Shanghai, China Corresponding author: Shui, Wenqing ([email protected])
Current Opinion in Biotechnology 2020, 64:24–31 This review comes from a themed issue on Analytical biotechnology Edited by Yinjie Tang and Ludmilla Aristilde
https://doi.org/10.1016/j.copbio.2019.09.002 0958-1669/ã 2019 Published by Elsevier Ltd.
It has been postulated that millions of functionally relevant PMIs may occur in bacterial cells, which composes equivalent complexity, if not more, compared to protein–protein interactions (PPIs) on the proteome scale [3,4]. However, systematic mapping of PMIs in model organisms has substantially lagged behind that of PPIs. Up till now, the most comprehensive PMI analysis resulted in identification of 1678 interaction pairs between 20 designated metabolites and cellular proteins in Escherichia coli whereas a network of more than 56 000 putative PPIs from over 10 000 proteins was constructed in HEK293T cells [4,5]. Technical hurdles in large-scale mapping of intracellular PMIs mainly reside in: 1) difficulty of detecting low-affinity and transient PMIs, 2) difficulty of installing an affinity handle to metabolites without disrupting relevant PMIs, 3) in vitro purification not feasible for certain proteins especially full-length transmembrane proteins, 4) no universal analytical methods available for detection of metabolites of diverse chemical structures. Nevertheless, the rapid advance of mass spectrometry (MS)-based proteomics and metabolomics technology [6,7] has been constantly promoting systematic profiling of PMIs especially in model microorganisms. A recent review presents an excellent overview of MS-based and several other biophysical or computational approaches for regulatory PMI discovery [8]. Given that versatile MSbased methods have been developed for biomolecular interaction analysis, here we will review recent progress in exploiting proteomic or metabolomic strategies for systematic PMI mapping. We will discuss the strengths and limitations of different PMI mapping approaches and the key elements in MS quantification and data mining for reliable PMI identification.
General strategies for PMI mapping Introduction Molecular recognition between proteins and small molecule metabolites plays an essential role in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can be all mediated by protein–metabolite interactions (PMIs), thus connecting cellular metabolism to genetic/epigenetic regulation, environmental sensing and signal transduction [1,2]. Apart from directly binding to the active or orthosteric sites of their native cognate proteins, metabolites are known to interact with different allosteric sites, allowing additional specific tuning of the structure and function of proteins and macromolecular protein assemblies. Current Opinion in Biotechnology 2020, 64:24–31
As pointed out by previous reviews [8,9], PMI mapping strategies are divided into two categories: metabolitecentric and protein-centric (Figure 1). Metabolite-centric PMI mapping aims to identify all protein partners from a cell lysate that potentially associate with a given metabolite ligand, which heavily relies on proteomics techniques. On the other side, the protein-centric strategy identifies endogenous metabolites (usually in specific chemical classes) that potentially bind to a given protein target, which mainly employs metabolomics techniques.
Metabolite-centric PMI mapping Enrich interacting proteins with bifunctional ligands
By dosing a chemically functionalized compound into cell lysates, ligand-binding proteins can be conveniently enriched www.sciencedirect.com
MS-based PMI mapping Li and Shui 25
LC-MS/MS
RT
Limited proteolysis
Intensity
Metabolite
Structural proteomics
LC-MS/MS
Metabolite-centric
UV light
Chemical proteomics
Enrichment Functionalized metabolite
Intensity
Figure 1
Enrichment
RT
Protein-centric
Protein
Untargeted metabolomics
LC-MS/MS
Intensity
RT
Current Opinion in Biotechnology
Systematic mapping of PMIs with metabolite-centric or protein-centric strategies. In metabolite-centric experiments, the cellular proteome extracts are treated with specific functionalized metabolites (upper) or label-free metabolites (middle) and subjected to proteomic analysis. Ligand-bound proteins are identified through affinity enrichment with the bifunctional ligand (chemical proteomics) or monitoring ligand-induced changes of protein structural characteristics (structural proteomics) such as proteolysis susceptibility as shown here. In protein-centric experiments, ligandbound proteins are isolated from the cellular metabolome extracts and associated ligands are typically identified using an untargeted metabolomics workflow (lower). Peptide or metabolite features from the ligand-bound proteins are annotated by red asterisks with signals from the metabolite/protein-treated samples represented by solid lines and controls by dashed lines.
and then identified through a chemical proteomics workflow. Notably, only installing an affinity handle to the ligand structure does not convey sufficient sensitivity and specificity for PMI mapping due to the weak and promiscuous binding of metabolites to many proteins [9]. A more effective way is to introduce bifunctional modifications to the ligand, one photoreactive group that converts weak PMIs to covalent ligand binding, the other clickable group for attachment of a purification tag to the ligand (Figure 1). Such bifunctional probes have been developed for multiple lipid molecules to map cellular protein–lipid interactions. For example, 265 putative cholesterol binding proteins were identified from HeLa cells using a trans-sterol probe [10]. The same group later designed a set of chemical proteomic probes to chart the global landscape of protein–lipid interactions in mammalian cells [11]. A total of 803 and 678 protein targets were identified using two arachidonoyl lipid probes respectively, and large portions of them are enzymes or transporters involved in lipid metabolism or trafficking (Table 1). It should be mentioned that most of the PMI mapping experiments with bifunctional probes adopted several controls including no UV irradiation, treatment with a control probe, and competition with the free ligand to filter out false positive protein hits [10,11]. www.sciencedirect.com
Apart from mapping protein–lipid interactions, application of this chemical proteomics strategy has recently been expanded to identification of proteins targeted by baicalin, a natural product in the flavonoid family (Table 1) [12]. Out of the 142 putative protein targets enriched by a flavonoid probe, an enzyme mediating fatty acid oxidation was found to be activated by the natural product through allosteric modulation. Present limitations for using bifunctional ligands are interference with ligand binding property and function as a result of chemical functionalization and the need of custom synthesis of ligand-derived probes. Monitor ligand-induced effects on protein structural characteristics
In order to address the challenge of mapping PMIs for non-functionalizable ligands, structural proteomics approaches have been developed to eliminate the need of ligand modification [8]. They rely on monitoring the changes of certain protein structural characteristics induced by specific ligand treatment. As ligand binding usually changes the protein structural stability, differential susceptibility to protease cleavage occurs on the Current Opinion in Biotechnology 2020, 64:24–31
26 Analytical biotechnology
Table 1 List of exemplary PMI mapping studies with different strategies Approach Metabolite-centric Chemical proteomics Sterol probes [10] Lipid probes [11] Flavonoid probe [12]
Species
Human Human, mouse Human
Loaded metabolites or proteins
PMIs identified
MS quan method
Candidate selection cutoffs a
Cholesterol Ararchidonolyl lipids Baicalin
265 678, 803 142
SILAC SILAC SILAC
FC 5.0 FC 3.0 FC 4.0 FC ＞ 2, Q ＜ 0.01
Structural proteomics LiP-SMap [4]
E. coli
20 metabolites
1678 (1447) b
SPROX [15] CETSA/TPP [17]
S. cerevisiae Human
ATP ATP c
139 (102) b 213
Label-free, DDA and DIA SILAC TMT
CETSA/TPP [20]
Human
ATP c
38 d
TMT
FC ＞ 1.7, P ＜ 0.01 Curve fitting R2 ＞ 0.8, delta Tm P ＜ 0.05 The same as above
138 metabolite standards Herbal extracts
5 enzymes
29 (13) b
Label-free, HRMS
FC > 2.0
2 viral proteins
20–30
Label-free, HRMS
FC ＞ 2.0, P < 0.05
S. cerevisiae
21 metabolic enzymes + 103 kinases 1275 proteins NAe
80
Label-free, HRMS
485 (445) b 81
Label-free, HRMS Label-free, HRMS
FC 3.0 or 5.0, P < 0.05 |Z-score| > 5 No quantitative cutofff
Protein-centric Dialysis-GC/LC–MS [27] Affinity MS and metabolomics [28] Affinity isolation and metabolomics [31] Untargeted metabolomics [34] SEC and metabolomics [35]
E. coli Arabidopsis thaliana
a
The major statistical criteria for candidate selection are summarized. FC refers to fold-change measured on proteins or metabolites in sample groups versus controls. b The number of new PMIs reported in that study is indicated in brackets. c These studies also profiled protein targets for several drugs and only results of ATP-binding protein identification are summarized. d Only membrane proteins are counted. e No protein overloading in this study. f Endogenous metabolites bound to native proteins are selected based on identification only in the sample group and not in the control.
protein targets upon binding of a ligand. LiP-small molecule mapping (LiP-SMap) is a workflow that combines limited proteolysis by a broad-specificity protease and quantitative proteomics to detect proteins with altered proteolytic patterns as putative metabolite-binding proteins (Figure 1) [4]. Initially developed for global analysis of protein conformational changes in yeast cells during metabolic transition [13], LiP-SMap has been tailored to systematic PMI mapping in the native cellular matrix of E. coli. Overall, 1678 PMI events were identified from the bacterial total proteome extract incubated with 20 selected metabolites, which represents the largest reported metabolite–protein interactome containing more than 1400 previously unknown PMIs (Table 1). This study also documented more than 7000 putative binding sites in the identified metabolite-binding proteins [4]. The change of protein stability under ligand treatment can be also monitored by proteome-wide thermodynamic analysis with two techniques. SPROX (stability of proteins from rates of oxidation) measures the shift of protein transition midpoints during chemical denaturant-induced unfolding [14]. A representative work is the identification Current Opinion in Biotechnology 2020, 64:24–31
of 139 yeast proteins possibly interacting with ATP in the native cell extract [15] (Table 1). As SPROX requires detection of methionine-containing peptides for monitoring protein-unfolding reactions, the low frequency of methionine in prokaryotic and eukaryotic proteomes (2.5%) has limited the number of proteins that can be assessed with this method. The second thermodynamics-based technique is thermal proteome profiling (TPP) which is a combination of the cellular thermal shift assay (CESTA) and quantitative proteomics [16,17]. In this assay, ligand-binding proteins are distinguished by an increased apparent melting temperature upon thermal denaturation [18]. TPP offers a higher throughput than SPROX by typically assessing 3000–7000 proteins that have their melting curves determined. TPP has been developed for large-scale mapping of both metabolite and drug interactions with the whole-cell proteome [17]. Notably, TPP has been implemented to map protein–drug interactions in not only cell lysates but also live cells by adding the compound to cell culture before performing cell lysis [19,20]. However, live cell treatment may not be necessary for PMI mapping as most metabolites are synthesized and trafficking intracellularly and many of them are not membrane-permeable. www.sciencedirect.com
MS-based PMI mapping Li and Shui 27
ATP-binding proteins were also extensively profiled by TPP which resulted in identification of 213 interacting proteins from whole-cell lysates [17] and 38 interacting membrane proteins from the membrane extracts of human cells [20] (Table 1). It is noteworthy that the above approaches measuring either proteolysis susceptibility or thermodynamic transition could generate false negatives for those PMIs not significantly affecting protein stability, or false positives due to conformational changes of non-target proteins as a downstream functional consequence of specific PMIs. Proteomic techniques used in metabolite-centric PMI mapping
Accurate and consistent proteomic quantification is vital for distinguishing true metabolite-binding proteins from the highly complex proteome background. Different MS quantification techniques have been integrated with
specific PMI mapping approaches (Figure 2, Table 1). Chemical proteomic approaches with bifunctional ligands almost all employ Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) for precise measurement of protein abundance changes [10,11,12]. Yet SILAC has limited throughput (analyzing 2–3 samples in one set) and the sample type is mainly restricted to cultured cell lines. Both TPP and SPROX choose an isobaric chemical labeling method (tandem mass tags, TMT) which allows multiplexing analysis of proteomic variation across 10 samples [14,16,17,19,20]. This quantification technique is best suited for acquisition of complete protein melting curves at a series of conditions. However, isobaric chemical labeling-based quantification suffers from a small dynamic range, which makes it difficult to distinguish relatively small magnitude of ligand-induced thermodynamic transition. SPROX was also combined with SILAC to measure the difference of protein folding/
Figure 2
+ Metabolite Control
+UV light LC-MS/MS
Proteolysis
Metabolite
SILAC
Enrichment
Intensity
Metabolite Heavy isotope
m/z
-UV light
Light isotope
Fraction non-denatured
LC-MS/MS
Intensity
TMT labeling
+ Metabolite T1 T2... T10
m/z
Limited proteolysis
500 475 450 425
MS2 Intensity
m/z
DIA Metabolite
DIA Isolation window
1175 . . . 625
Control + Metabolite
400 Cycle time
Retention time
RT
Data-independent acquisition
1200
Temperature
Isobartic chemical labeling
+ Metabolite Control
Current Opinion in Biotechnology
Major MS quantification techniques integrated with different PMI mapping approaches. SILAC is most often coupled to the metabolite-centric chemical proteomics workflows (upper). Ligand-bound proteins are distinguished by significant upregulation in the bifunctional ligand-treated samples (heavy isotope labeled as shown here) versus the control. Isobaric chemical labeling is widely employed in the metabolite-centric structural proteomics workflows (middle). The thermal melting curves of various proteins are derived from multiplexing proteomic quantification with TMT labeling. Ligand-bound proteins are distinguished by increased melting temperature. Data-independent acquisition (DIA) has been employed to quantify peptide abundance changes during limited proteolysis of proteins upon metabolite treatment. Ligand-bound proteins are distinguished by altered proteolytic patterns in the presence of a given metabolite. DIA ion map was adapted from publication Methods Mol Biol. 2017, 1550:289–307. www.sciencedirect.com
Current Opinion in Biotechnology 2020, 64:24–31
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unfolding extent in the absence or presence of a test ligand whereas the full melting curves were determined in a label-free manner [15]. Surprisingly, application of two types of thermodynamics-based approaches, SPROX with isobaric chemical labeling and pulse proteolysis with SILAC, gave rise to distinct sets of protein targets with very small overlap, indicating that different structural proteomics workflows may yield different PMI identifications for the same ligand [21]. The most recent PMI mapping study with the LiP-SMap approach adopted a different and more flexible quantification workflow integrating shotgun proteomic analysis and label-free dataindependent acquisition (DIA) [4]. DIA quantification has combined the strengths of shotgun proteomics and targeted proteomics to achieve high throughput, high sensitivity, high quantification consistency and a broad dynamic range [22]. We expect this quantification technique will gain more prevalence in future MS-based PMI investigation. It is worth mentioning that in the above representative PMI mapping studies, the criteria for selection of potential metabolite-interacting proteins have certain flexibility (Table 1). Even those using the same mapping strategy would define different cut-offs and statistical parameters. It is essential to include positive and negative controls in the experimental design in order to validate specific PMI selection criteria.
Protein-centric PMI mapping Capture interacting metabolites with purified proteins
Small molecule ligands associated with a protein of interest can be captured through protein isolation from a complex matrix and protein-bound ligands are then identified by LC–MS analysis of the compound mixture. This strategy termed affinity MS or affinity selection MS (AS-MS) has been widely implemented to highthroughput ligand screening from synthetic compound libraries towards diverse protein targets [23,24,25,26]. Protein-centric PMI mapping adopts the same strategy in most cases to screen endogenous metabolite ligands from native cellular matrix for a given protein. Multiple biochemical methods have been established to isolate ligand-bound protein complexes either from metabolite standard mixtures or cell extracts. Equilibrium dialysis of individual purified recombinant proteins against a 138metabolite mixture coupled to targeted MS analysis of interacting metabolites identified 29 PMIs for five enzymes in central carbon metabolism [27] (Table 1). Ultrafiltration is equally effective in separating protein complexes from free ligands and it is most often applied to discovery of small molecule ligands from crude extracts of natural products, especially Traditional Chinese Medicine (TCM), for purified protein targets [28,29,30]. Since natural product extracts consist of a large number of small molecule metabolites, an untargeted metabolomics workflow has been Current Opinion in Biotechnology 2020, 64:24–31
combined with affinity MS for efficient discovery of chemical ligands from TCM for viral protein targets [28] (Table 1). Furthermore, virtual screen was integrated with affinity MS-based experimental screen to identify ligands with higher confidence from several TCM crude extracts that target a specific viral nucleoprotein [29]. These studies have demonstrated the great potential of PMI screening in natural products for discovery of functional small molecule ligands. The most comprehensive protein-centric PMI mapping was the identification of extensive hydrophobic metabolite interactions with 16 lipid biosynthetic enzymes and 21 kinases in yeast to reveal the crosstalk between lipid metabolism and kinase activity regulation [31] (Table 1). This work involved overexpression of more than 100 proteins, affinity isolation of each protein from yeast cell lysates, and extraction of protein-bound metabolites for untargeted profiling. As pointed out by the authors, this PMI profiling strategy is biased against proteins that cannot be readily purified, and it is also limited by the scope of metabolites that can be assayed with a defined LC–MS method [31]. Likewise, affinity immobilized protein targets were incubated with lipophilic metabolite extracts from animal tissues for identification of fatty acids interacting with specific nuclear receptors [32,33]. Given that functional PMIs between enzymes and their substrates lead to enzymatic reactions, monitoring the accumulation or depletion of metabolites in a cellular metabolome extract treated with overexpressed or purified proteins would uncover the enzyme-substrate/product connections. Using a untargeted metabolomics approach, a recent study screened 1275 functionally uncharacterized E. coli proteins and discovered 241 potential novel enzymes associated with at least one annotated metabolite [34] (Table 1). Because this approach monitors metabolite abundance changes instead of metabolite binding to the protein, it is not suited for discovery of allosteric modulators or PMIs for non-enzyme proteins. Native PMI identification without overloading
All the above strategies may cause artificial or biased PMIs due to overloading purified proteins or metabolite standards into the cellular metabolome or proteome extract. An ideal experiment would be to assay native PMIs in the cellular environment without any overloading. Preliminary efforts have been made in this direction by separating native protein complexes with free metabolites meanwhile detecting metabolites co-fractionating with proteins [35]. A total of 81 endogenous polar metabolites were identified to be associated with native proteins in the Arabidopsis cell lysates but these metabolite-binding proteins were not deconvoluted (Table 1). If precisely quantifying both the metabolites and proteins present in each protein complex fraction separated by non-denaturing www.sciencedirect.com
MS-based PMI mapping Li and Shui 29
chromatography, one could delineate numerous native PMIs through correlative analysis akin to protein– protein interactome mapping through protein complex correlation profiling [36,37]. We envision that this proposed new strategy may lead to discovery of novel PMIs not revealed by previous studies under overloading conditions. Metabolomics techniques used in PMI mapping
Small molecule quantification in most PMI mapping studies relies on label-free high-resolution MS (HRMS) analysis (Table 1). The acquired metabolomics dataset is typically processed in an untargeted manner which allows detection and quantification of tens of thousands of small molecule features in the LC-HRMS data [38,39]. Proteinbound metabolites are identified from differential features that are significantly enriched in the protein incubation sample versus control. However, several studies have reported considerable differences in feature detection and differential marker selection from the same metabolomics dataset using different untargeted data processing software [40,41]. Combined usage of multiple software tools is recommended to increase the confidence of differential marker identification [41]. Of note, previous PMI mapping studies employed different data processing packages and different data filtering cutoffs for selection of potential PMI pairs (Table 1). Another important challenge in data mining is structural annotation of the differential metabolic features. Without using standards, most metabolomics studies assign the putative metabolite structures based on accurate mass measurement and matching experimental MSMS spectra with public spectral libraries [42,43]. Because of the limited source of high-quality metabolite spectral libraries and ambiguity of isomeric compound identification by MSMS analysis, the structures of many differential features found in untargeted metabolomics cannot be confidently assigned or even falsely assigned [42]. Taken together, it is highly preferred to validate the identified PMI pairs first using purified proteins and metabolite standards before functional evaluation.
Conclusion Technical breakthroughs in MS-based proteomics and metabolomics have substantially propelled global mapping of biomolecular interaction networks. Although it becomes increasingly easy to identify several thousands of PMIs from one experiment, one has to be cautious in interpreting the data and selecting candidates for functionality assessment. As discussed in this review, false positive and false negative identification of PMIs are almost inevitable in large-scale profiling experiments using different mapping strategies, MS quantification techniques and data mining workflows. Thus, it is necessary to validate novel PMIs with orthogonal approaches such as biophysical methods for protein–ligand interaction characterization [44]. Alternatively, different PMI www.sciencedirect.com
mapping approaches such as those using bifunctional ligands or based on monitoring protein thermodynamic transition can be applied together to validate results and narrow down PMIs of possible functional importance. All previous studies have documented PMI networks in the whole cell at an equilibrium state. One of the future directions would be to explore temporal dynamics and spatial organization of PMIs in different subcellular compartments. With the growing interest in PMI mapping in different organisms, now it is desirable to build a highquality PMI database similar to the CORUM database of experimentally characterized protein complexes [45]. Although the BRENDA database includes thousands of regulatory enzyme–ligand interactions mostly present in E. coli and Saccharomyces cerevisae [46], a comprehensive PMI database for various proteins beyond enzymes in different species is still lacking. This expanding PMI landscape could be integrated with other functional omics data such as PPI networks, protein post-translational modifications or metabolic fluxes to bring new insights into the crosstalk between different layers of cellular regulation. Such comprehensive knowledge would possibly open new avenues for engineering microbial metabolic networks to improve biochemical productivity.
Conflict of interest statement Nothing declared.
Acknowledgements We thank ShanghaiTech University, the National Key Research and Development Program of China (No. 2018YFA0507004) and the National Natural Science Foundation of China (No. 31971362) for funding.
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