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Chemoproteomic profiling of protein–metabolite interactions
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Chemoproteomic profiling of protein–metabolite interactions Wei Qin1,2,3,4, Fan Yang1,2,3,5 and Chu Wang1,2,3,4,5 Abstract
Small molecule metabolites play important roles in regulating protein functions, which are acted through either covalent nonenzymatic post-translational modifications or non-covalent binding interactions. Chemical proteomic strategies can help delineate global landscapes of cellular protein–metabolite interactions and provide molecular insights about their mechanisms of action. In this review, we summarized the recent progress in developments and applications of chemoproteomic strategies to profile protein–metabolite interactions. Addresses 1 Synthetic and Functional Biomolecules Center, Peking University, Beijing, China 2 Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China 3 Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China 4 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China 5 College of Chemistry and Molecular Engineering, Peking University, Beijing, China Corresponding author: Wang, Chu ([email protected])
Current Opinion in Chemical Biology 2020, 54:28–36 This review comes from a themed issue on Omics Edited by Raymond Moellering For a complete overview see the Issue and the Editorial
https://doi.org/10.1016/j.cbpa.2019.11.003 1367-5931/© 2019 Elsevier Ltd. All rights reserved.
Keywords Chemoproteomics, Protein-metabolite interactions, Post-translational modifications, Non-covalent binding, Activity-based protein profiling.
Introduction Small molecule metabolites play essential roles in maintaining cell physiology, such as feeding energy production cycles, activating signaling receptors, mediating cellular communications, etc. The diversified functions of metabolites are mainly acted by engagement with proper protein targets in cells, and it is, therefore, crucial to identify such proteinemetabolite interactions (PMIs), to decipher the physiological roles of each metabolite. However, the scale and complexity Current Opinion in Chemical Biology 2020, 54:28–36
of the PMI network in a cell present a formidable challenge for traditional genetic and biochemical strategies. For example, it has been estimated that in a bacterial cell, there are at least one million protein molecules and about 100-fold more metabolites exist, suggesting an extremely intermingled and convoluted PMI network [1,2]. A systematic approach to interrogate the crosstalk between proteins and metabolites will be highly desirable to gain better understanding of diverse biological processes such as transcription, signaling transduction, and metabolism [3]. PMIs could be generally classified into two major types: non-covalent PMIs and covalent post-translational modifications (PTMs) that are not enzymatically mediated, but instead directly induced by reactive metabolites themselves. Non-covalent PMIs include enzyme/substrate complexes, receptor/ligand complexes, and channel/cargo complexes [4]. Some metabolites could also bind to the enzyme away from its active site as allosteric cofactors, which could regulate the protein function in a rapid and reversible manner [4]. On the other hand, covalent PMIs usually occur when metabolites with reactive functional groups encounter nucleophilic residues such as cysteines and lysines, directly forming covalent modifications on the target proteins [5]. Examples of such non-enzymatic modifications include sulfenylation [6], glycation [7**], carbonylation [8], and non-enzymatic acylation [9], all of which have been shown to regulate protein functions. Compared to the traditional metabolite-centric methods that have been widely applied for ‘pulling down’ the interacting proteins of a particular metabolite in cell lysates [10], chemical proteomics offers another attractive approach to systematically map PMIs in an unbiased manner. It usually relies on specific chemical probes that can covalently capture PMI of interest from proteomes of living cells and enrich them for analysis by quantitative proteomic tools. With properly designed probes, the strategy can be flexibly applied to analyze both non-covalent and covalent PMIs. In this review, we will summarize recent advances in chemoproteomic approaches to profile PMIs and discuss perspectives for future method development.
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Direct capture methods based on chemical reactivity
later conjugated with a biotin affinity tag via bioorthogonal reactions such as Cu(I)-catalyzed azidee alkyne cycloaddition (CuAAC). The probe-labeled proteins can be enriched by streptavidin beads and identified by standard shotgun proteomics. To achieve sitespecific analysis of these covalent PMIs, orthogonal cleavable linkers are introduced into the biotin tags [13] and seamlessly integrated with chemical proteomic workflows (Figure 1a). In such a ‘tandem orthogonal proteolysis (TOP)’ workflow, after the streptavidin enrichment and trypsin digestion of the labeled proteins, the probe-adducted peptides could be specifically released by the second round of linker cleavage and analyzed by LC-MS/MS to locate the exact site of modification. Moreover, stable isotope-encoded cleavable linkers have been developed for quantitative analysis [14,15] of metabolite-induced modifications.
Certain metabolite-induced modifications contain unique functional moieties that could be selectively reacted with chemical probes with properly designed warheads (Figure 1a). Such chemoselective probes for direct capture often bear a reporter handle, which can be
Chemoselective probes have been developed for covalently capturing different types of metabolite-induced modifications (Figure 1b). For example, when cells are under oxidative stress, lipid-derived electrophiles
Chemoproteomic methods for mapping metabolite-induced protein modifications Metabolite-induced protein modifications are spontaneous and mostly occur on residues with enhanced chemical reactivity (e.g., cysteines and lysines). Current chemoproteomic methods to profile such nonenzymatic endogenous modifications can be generally classified into two approaches: (1) direct capture methods based on chemical reactivity; (2) competitive profiling of reactive residues. While the ‘bioorthogonal analogues’ probes have also been widely adopted (e.g., for glycosylation [11] and lipidation [12]), they could only be exogenously fed to cells to mimic endogenous metabolite-induced protein modifications and, therefore, will not be extensively discussed here.
Figure 1
Chemical proteomic approaches for direct capture of metabolite-induced modification by chemoselective probes. (a) Workflow for the sitespecific chemoproteomic analysis of metabolite-induced modifications. The structures of representative acid cleavable tags are shown. (b) Representative chemoselective reactions between metabolite-induced modifications and chemical probes.
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(LDEs) containing a,b-unsaturated aldehyde or ketone groups can react with nucleophilic sidechains of cysteines, lysines, and histidines via Michael addition, leaving a free carbonyl moiety on the modified protein [16]. To capture such ‘carbonylations,” several aldehydedirected probes with hydrazide, aminooxy, and aniline warheads have been developed and applied in the chemoproteomic pipeline to profile sites of modification induced by LDEs such as 4-hydroxy-2-nonenal (HNE) and acrolein [17-19**]. Moreover, thiol groups of cysteines can be reversibly oxidized to sulfenic acids or sulfinic acids, which can be captured by probes derived from dimedone analogues and electrophilic nitrogen species (ENS), respectively [20,21]. Clickable dimedone and ENS probes have enabled site-centric proteomic analysis of S-sulfenylations and S-sulfinylations [6,22].
Taking advantage of the site-specific resolution and quantitative power of isoTOP-ABPP, a competitive isoTOP-ABPP strategy was developed for identifying cysteine modifications induced by reactive metabolites such as LDEs [32] (Figure 2). It operates on the rationale that the cysteine modification induced by LDEs could compete the labeling of IA-alkyne, and the competition ratio of each cysteine could be quantified through the isoTOP-ABPP analysis. In this study, the reactivity profile of several LDEs against over 1000 cysteines was accurately quantified in parallel, and a group of ‘hot spot’ residues for LDE modifications were revealed. Moreover, this strategy has been extended to identify the cysteine modifications of microbiotaderived metabolite dipeptide aldehyde and fumarate, which is a covalent oncometabolite accumulated in renal cell carcinoma [33,34].
Chemoselective probes have also been developed for Snitrosylation, S-sulfhydration, and carbamylation, which are induced by gaseous signaling molecules such as nitric oxide (NO), hydrogen sulfide (H2S), and carbon dioxide (CO2) [23e25]. Interestingly, nitrosothiols can react with sulfinic acids to form a stable thiosulfonate bond, and such a ‘reciprocal’ activity has been leveraged to enrich endogenous S-nitrosated and S-sulfinated proteins using the sulfonate- and S-nitrosothiol-linked probes, respectively [26]. More recently, alkynyl thioester probes were developed to label homocysteinylation on lysines, and a hydroxylamine probe was used to capture phosphorylation on aspartic acids [27,28].
More recently, we applied the competitive isoTOPABPP strategy to profile cysteine modifications induced by itaconate, an abundant anti-inflammatory metabolite with weak electrophilicity in lipopolysaccharide-stimulated (LPS-stimulated) macrophages [35]. IA-alkyne was first used to label cysteines in proteomes; however, no effective competition was observed by itaconate, probably due to its intrinsically weak electrophilicity. To overcome this problem, we drew inspirations from an ‘off-target’ Sglycosylation reaction of the per-O-acetylated unnatural sugar probe, which has been traditionally used in metabolic glycan labeling, and we developed a cysteine-reactive monosaccharide probe that could be effectively competed by itaconate. Our competitive profiling with the new S-glycosylation probe identified 260 itaconate-reactive cysteines in macrophage proteomes and revealed that itaconate could modify key cysteines of glycolytic enzymes to inhibit glycolysis, which in turn mediates its antiinflammatory effect [36]. The example highlights the importance of using chemical probes for the competitive profiling that have the ‘matching’ reactivity with the metabolite of interest. It also warrants continuous development of new chemical probes with enhanced selectivity to different reactive residues in proteomes.
Competitive profiling of reactive residues
The direct capture approach is suitable only for metabolite-induced modifications with reactive functional moiety. Considering that covalent PMIs often occur on reactive residues such as cysteines and lysines, it is possible to implement a competitive profiling strategy to map metabolite-induced modifications on these residues that are too chemically inert to be directly captured. We here focused on the competitive isoTOP-ABPP profiling of cysteine modifications, as it is one of the most widely implemented strategies for such purposes. Activity-based protein profiling (ABPP) is a versatile platform to monitor the functional states of enzymes by active site-directed chemical probes, which was originally developed for enzyme and drug discovery [29]. The technology was enhanced for systematic identification of reactive cysteines in native proteomes by TOPABPP analysis with a thiol-reactive iodoacetamide (IA)alkyne probe [30]. Moreover, the chemical reactivity of cysteines in proteomes could be quantitatively ranked and correlated with their functional importance by the isotopic TOP-ABPP (‘isoTOP-ABPP’) analysis [31]. Current Opinion in Chemical Biology 2020, 54:28–36
It is noteworthy that competitive isoTOP-ABPP can be applied to profile cysteine modifications not only by reactive metabolites, but also by reactive natural products or covalent drugs [37,38]. It has also been implemented to evaluate the reactivity of a cysteinereactive small-molecule fragment library and to discover unique anchor sites from a large number of proteins that are previously deemed as undruggable [39]. More recently, the pipeline of competitive isoTOP-ABPP was adapted to be compatible with reductive demethylation, as well as different cleavable azide-biotin tags, which not only expands its analytical www.sciencedirect.com
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Figure 2
Workflow of the competitive isoTOP-ABPP analysis for identification of metabolite-induced cysteine modifications. Representative cysteine probes and reactive metabolites are shown.
capacity from duplex to triplex quantification, but also significantly reduces the cost of the method for easy accessibility [40].
Methods for profiling non-covalent protein–metabolite interactions Many metabolites regulate protein functions in a noncovalent manner, which is usually transient and with weak affinity within a complicated cellular environment. An ideal profiling strategy should allow the capture of functional PMIs directly in living cells and then enrichment of them for subsequent identification. In recent years, photo-affinity chemical probes have emerged as powerful tools to study non-covalent interactions between target metabolites and proteomes [41]. A photo-affinity probe typically contains three components: (1) a binding scaffold based on the metabolite structure that can direct the probe to its genuine targets, (2) a photo-reactive group that transforms reversible interactions into covalent adducts upon ultraviolet (UV) irradiation, and (3) a bio-orthogonal
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group (e.g., alkyne and azide) that allows the following conjugation with biotin tags for enrichment (Figure 3a). Different types of photo-reactive groups have been developed including a suite of ‘minimalist’ photocrosslinkers [42]. In combination with quantitative proteomics such as stable-isotope labeling by amino acids in cell culture (SILAC), the protein targets can be identified by LC-MS/MS analysis and the extent of binding engagement can be quantified [43] (Figure 3a). To minimize false identification due to non-specific binding, additional experiments with UV-dependent labeling and/or competition by the native metabolite should be performed to obtain genuine PMIs with high confidence. In certain cases, the binding site of a photoreactive probe on the target protein could be mapped by locating the probe-adducted peptide regions with unique isotopic patterns [44*]. In the context of PMIs, photo-affinity labeling has been successfully applied to capture the interacting proteins of various types of lipid metabolites, including Current Opinion in Chemical Biology 2020, 54:28–36
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phospholipids [45], cholesterol [46], glycolipids [47], and sphingolipid [48] (Figure 3b). By synthesizing a panel of photo-reactive lipid probes featuring archidonoyl, oleoyl, palmitoyl, and stearoyl acyl chains, the Cravatt group established a comprehensive map of lipidbinding proteins and surveyed their ligandability in living cells [43]. Recently, our group also developed a number of structurally diverse and photo-reactive bile acid (BA) probes to globally profile BA-protein interactions in mammalian cells and uncovered a large number of novel BA-binding proteins with potential functional implications [49]. Implemented in a
competitive profiling approach, the photo-affinity probes can also be used for mapping thousands of reversible ligandeprotein interactions in human cancer cell, facilitating rapid fragment-based ligand discovery for undruggable protein targets [50]. Despite its robustness, there are still certain drawbacks limiting the wide application of the photo-affinity labeling approach. First, the design of the photo-reactive probes needs to be guided by structureeactivity relationship of the ligand because the introduction of the photo-reactive and bio-orthogonal group may affect its
Figure 3
Identification of non-covalent protein–metabolite interactions by photo-affinity labeling. (a) Workflow of the UV-dependent (upper) and competition (below) experiments using SILAC-ABPP. Structures of the representative photo-crosslinkers are shown. (b) Structures of representative photo-reactive probes with the photo-crosslinkers and bioorthogonal groups shown in yellow and red, respectively.
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binding affinity and selectivity to the target proteins. Second, synthesis of the photo-reactive probes is very technically demanding and cannot be generalized to any metabolites of interest. Last but not the least, it has been shown that photo-crosslinkers may have ‘nonspecific’ binding, too, which could contaminate the validity of mass spectrometry (MS) results [51]. It is, therefore, important that any target that is selected for further functional annotation should be carefully validated by orthogonal biochemical methods.
Label-free strategies for mapping protein–metabolite interactions in proteomes As an alternative to the photo-affinity labeling strategy, label-free methods for detecting endogenous noncovalent PMIs have gained more and more attention recently. Representative approaches include the thermal proteome profiling (TPP) and the limited proteolysis-coupled MS (LiP-MS), both of which rely on determining the change of physical parameters of the target proteins as induced by the metabolite binding at the proteomic scale (Figure 4). TPP integrates multiplex quantitative proteomics with the cellular thermal shift assay (CETSA), enabling identification of proteins with significantly shifted melting temperatures in presence of a small molecule
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[52] (Figure 4a). The cellular targets of endogenous metabolites, such as 20 30 -cGAMP and ATP, have been systematically investigated in living cells by TPP [53,54*]. Very recently, the Moellering group developed a hotspot TPP strategy for high throughput discovery of functional protein modifications by analyzing the impact of site-specific PTMs on protein thermal stability [55]. LiP-MS measures the protein structural changes by a quick treatment of cell extracts with a promiscuous protease to generate confirmation-dependent proteolytic patterns [56] (Figure 4b). The Picotti group has developed a method named ‘LiP-small molecule mapping (LiP-SMap)’ to globally detect proteins that become differentially susceptible to protease cleavage in response to binding of endogenous metabolites, establishing a comprehensive database containing 1678 PMIs as well as 7345 putative binding sites [57**]. Remarkably, false discovery rate was stringently controlled by comparison with previously known PMIs, which suggests the high confidence for a large fraction of novel PMIs detected in this dataset. It should be noted that metabolites may indirectly induce changes on protein stability and conformational changes through a connected proteineprotein interaction network, which could also be captured by the aforementioned label-free profiling strategies. It is also possible that some weak PMIs may fail to induce any
Figure 4
Label-free strategies for proteomic mapping protein–metabolite interactions. (a) Workflow of the thermal proteome profiling for identification of protein–metabolite interactions. (b) Workflow of the limited proteolysis-coupled MS for identification of protein–metabolite interactions.
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detectable stability and structural changes. Compared to the photo-affinity labeling approaches, these methods lack the step of enrichment and may not be suitable for PMIs with substoichiometric binding engagements. Lastly, successful implementation of TPP and LipSMap requires extensive proteomic runs with demanding sample-to-sample reproducibility, and such experimental setups and expertise may not be readily available to regular labs.
Conclusions and future outlook PMIs play diverse roles in regulating many important biological processes. While traditional biochemical and genetic methods have functionally characterized individual PMIs case-by-case, chemical proteomics aims to establish a global landscape of binding proteins for a given metabolite of interest in a complexed system. Depending on the nature of the interaction (e.g., covalent modification versus non-covalent binding), different strategies have been developed accordingly, as we have summarized above, and a large number of new PMIs have been revealed. However, considering the complexity of life, what have been discovered only constitutes ‘the tip of an iceberg,” and challenges remain significant in the field, which warrant development of new methods with enhanced sensitivity and coverage for PMI profiling. First, new chemical probes are desperately needed to cover reactive proteomes beyond cysteines. A variety of chemical probes have been developed to profile cysteines, with distinct reactivity and substrate selectivity [58]. Although cysteines are functionally critical for many enzymes, they are generally less abundant in proteomes, and it is conceivable that competitive isoTOP-ABPP using reactive probes toward other types of amino acids would significantly expand the coverage of potential PMI landscape. While the probes of lysine and methionine have been implemented for profiling in a similar manner [59,60], residue-specific probes for histidines, tyrosines, arginines, carboxyl sidechains, etc. are still waiting to be developed. Second, the resolution of the profiling methods needs to be improved from the protein level to the site level. It is particularly important for probes with promiscuous reactivity, as the resulting list of target proteins might be otherwise largely contaminated. For example, with a cleavable azide-biotin linker, we discovered an unexpected S-glycosylation reaction for the per-O-acetylated unnatural monosaccharides, which has been widely used imaging and profiling of O-glycosylation [61*]. Such site-specific analysis should be implemented not only for covalent PTMs, but also for photo-affinity probes used for capturing non-covalent PMIs. While isotopically encoded enrichment tags and associated algorithms have been developed to facilitate mapping binding sites Current Opinion in Chemical Biology 2020, 54:28–36
of the photo-affinity probes [44,62], in silico docking simulations could also be considered when structural information is available [63]. Third, chemoproteomic methods should be developed with more compatibility with in situ or in vivo profiling. As most of the direct capture methods and competitive profiling for mapping covalent PMIs are currently performed in cell lysates, cell penetration and toxicity of the probes need further improvement to enable spatial and temporal profiling of modifications in living contexts. While certain photo-affinity probes might be able to cross plasma membranes to get inside cells, label-free methods investigating non-covalent PMIs always record the protein structural information in vitro, which could change when cells are lysed. It is, therefore, highly desired to develop label-free proteomic approaches that can be directly applied in living cells. Lastly, with the rapid development of the metabolomics technology, it is anticipated that more and more metabolites with unique reactivity will be discovered, and investigating their interactions with the proteomes might require new enabling tools, which include, but are not limited to, strategies to search unknown PTMs [64], methods to detect metabolite crosslinking [7], and algorithms to discover residue networks for metabolite binding [65]. Nevertheless, with enhanced sensitivity and easy accessibility, chemoproteomic will become a standard tool to interrogate PMIs and provide valuable clues for studying the mechanisms of action of bioactive metabolites.
Conflict of interest statement Nothing declared.
Acknowledgements We acknowledge the funding support by the National Key Research and Development Projects (No. 2016YFA0501500) and the National Natural Science Foundation of China (No. 21521003, No. 81490740 and No. 21778004).
References Papers of particular interest, published within the period of review, have been highlighted as: * of special interest * * of outstanding interest 1.
Milo R: What is the total number of protein molecules per cell volume? A call to rethink some published values. Bioessays 2013, 35:1050–1055.
2.
Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD: Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol 2009, 5:593–599.
3.
Chubukov V, Gerosa L, Kochanowski K, Sauer U: Coordination of microbial metabolism. Nat Rev Microbiol 2014, 12:327–340.
4.
Gerosa L, Sauer U: Regulation and control of metabolic fluxes in microbes. Curr Opin Biotechnol 2011, 22:566–575.
www.sciencedirect.com
PMI profiling by chemoproteomics Qin et al.
5.
Harmel R, Fiedler D: Features and regulation of non-enzymatic post-translational modifications. Nat Chem Biol 2018, 14: 244–252.
6.
Yang J, Gupta V, Carroll KS, Liebler DC: Site-specific mapping and quantification of protein S-sulphenylation in cells. Nat Commun 2014, 5:4776.
7. **
Bollong MJ, Lee G, Coukos JS, Yun H, Zambaldo C, Chang JW, Chin EN, Ahmad I, Chatterjee AK, Lairson LL, et al.: A metabolite-derived protein modification integrates glycolysis with KEAP1-NRF2 signalling. Nature 2018, 562:600–604. Authors revealed a direct link between glycolysis and NRF2 signaling. Inhibition of the glycolytic enzyme PGK1 results in accumulation of the methylglyoxal, which modifies KEAP1 to form crosslinking between proximal cysteine and arginine residues. 8.
Hauck AK, Huang Y, Hertzel AV, Bernlohr DA: Adipose oxidative stress and protein carbonylation. J Biol Chem 2019, 294: 1083–1088.
9.
Trub AG, Hirschey MD: Reactive acyl-CoA species modify proteins and induce carbon stress. Trends Biochem Sci 2018, 43:369–379.
10. Guo H, Peng H, Emili A: Mass spectrometry methods to study protein-metabolite interactions. Expert Opin Drug Discov 2017, 12:1271–1280. 11. Hao Y, Fan X, Shi Y, Zhang C, Sun DE, Qin K, Qin W, Zhou W, Chen X: Next-generation unnatural monosaccharides reveal that ESRRB O-GlcNAcylation regulates pluripotency of mouse embryonic stem cells. Nat Commun 2019, 10:4065. 12. Storck EM, Morales-Sanfrutos J, Serwa RA, Panyain N, LanyonHogg T, Tolmachova T, Ventimiglia LN, Martin-Serrano J, Seabra MC, Wojciak-stothard B, et al.: Dual chemical probes enable quantitative system-wide analysis of protein prenylation and prenylation dynamics. Nat Chem 2019, 11:552–561. 13. Szychowski J, Mahdavi A, Hodas JJ, Bagert JD, Ngo JT, Landgraf P, Dieterich DC, Schuman EM, Tirrell DA: Cleavable biotin probes for labeling of biomolecules via azide-alkyne cycloaddition. J Am Chem Soc 2010, 132:18351–18360. 14. Yang J, Tallman KA, Porter NA, Liebler DC: Quantitative chemoproteomics for site-specific analysis of protein alkylation by 4-hydroxy-2-nonenal in cells. Anal Chem 2015, 87: 2535–2541. 15. Qin K, Zhu Y, Qin W, Gao J, Shao X, Wang YL, Zhou W, Wang C, Chen X: Quantitative profiling of protein O-GlcNAcylation sites by an isotope-tagged cleavable linker. ACS Chem Biol 2018, 13:1983–1989. 16. Chen Y, Qin W, Wang C: Chemoproteomic profiling of protein modifications by lipid-derived electrophiles. Curr Opin Chem Biol 2016, 30:37–45. 17. Chen Y, Cong Y, Quan B, Lan T, Chu X, Ye Z, Hou X, Wang C: Chemoproteomic profiling of targets of lipid-derived electrophiles by bioorthogonal aminooxy probe. Redox Biol 2017, 12: 712–718. 18. Chen Y, Liu Y, Lan T, Qin W, Zhu Y, Qin K, Gao J, Wang H, * * Hou X, Chen N, et al.: Quantitative profiling of protein carbonylations in ferroptosis by an aniline-derived probe. J Am Chem Soc 2018, 140:4712–4720. Authors used a quantitative chemoproteomic method to detecting endogenous target proteins and residue sites of carbonylation by a new aniline-based probe during ferroptosis, and validated that a functional cysteine C210 in the channel protein VDAC2 might play an important role in mediating ferroptosis. 19. Chen Y, Liu Y, Hou X, Ye Z, Wang C: Quantitative and sitespecific chemoproteomic profiling of targets of acrolein. Chem Res Toxicol 2019, 32:467–473. 20. Paulsen CE, Truong TH, Garcia FJ, Homann A, Gupta V, Leonard SE, Carroll KS: Peroxide-dependent sulfenylation of the EGFR catalytic site enhances kinase activity. Nat Chem Biol 2011, 8:57–64. 21. Lo Conte M, Carroll KS: Chemoselective ligation of sulfinic acids with aryl-nitroso compounds. Angew Chem Int Ed Engl 2012, 51:6502–6505.
www.sciencedirect.com
35
22. Akter S, Fu L, Jung Y, Conte ML, Lawson JR, Lowther WT, Sun R, Liu K, Yang J, Carroll KS: Chemical proteomics reveals new targets of cysteine sulfinic acid reductase. Nat Chem Biol 2018, 14:995–1004. 23. Doulias PT, Tenopoulou M, Greene JL, Raju K, Ischiropoulos H: Nitric oxide regulates mitochondrial fatty acid metabolism through reversible protein S-nitrosylation. Sci Signal 2013, 6. rs1. 24. Zhang D, Macinkovic I, Devarie-Baez NO, Pan J, Park CM, Carroll KS, Filipovic MR, Xian M: Detection of protein S-sulfhydration by a tag-switch technique. Angew Chem Int Ed Engl 2014, 53:575–581. 25. Linthwaite VL, Janus JM, Brown AP, Wong-Pascua D, O’Donoghue AC, Porter A, Treumann A, Hodgson DRW, Cann MJ: The identification of carbon dioxide mediated protein post-translational modifications. Nat Commun 2018, 9: 3092. 26. Majmudar JD, Konopko AM, Labby KJ, Tom CT, Crellin JE, Prakash A, Martin BR: Harnessing redox cross-reactivity to profile distinct cysteine modifications. J Am Chem Soc 2016, 138:1852–1859. 27. Chen N, Liu J, Qiao Z, Liu Y, Yang Y, Jiang C, Wang X, Wang C: Chemical proteomic profiling of protein N-homocysteinylation with a thioester probe. Chem Sci 2018, 9: 2826–2830. 28. Chang JW, Montgomery JE, Lee G, Moellering RE: Chemoproteomic profiling of phosphoaspartate modifications in prokaryotes. Angew Chem Int Ed Engl 2018, 57:15712–15716. 29. Niphakis MJ, Cravatt BF: Enzyme inhibitor discovery by activity-based protein profiling. Annu Rev Biochem 2014, 83: 341–377. 30. Weerapana E, Simon GM, Cravatt BF: Disparate proteome reactivity profiles of carbon electrophiles. Nat Chem Biol 2008, 4:405–407. 31. Weerapana E, Wang C, Simon GM, Richter F, Khare S, Dillon MB, Bachovchin DA, Mowen K, Baker D, Cravatt BF: Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 2010, 468:790–795. 32. Wang C, Weerapana E, Blewett MM, Cravatt BF: A chemoproteomic platform to quantitatively map targets of lipid-derived electrophiles. Nat Methods 2014, 11:79–85. 33. Kulkarni RA, Bak DW, Wei D, Bergholtz SE, Briney CA, Shrimp JH, Alpsoy A, Thorpe AL, Bavari AE, Crooks DR, et al.: A chemoproteomic portrait of the oncometabolite fumarate. Nat Chem Biol 2019, 15:391–400. 34. Guo CJ, Chang FY, Wyche TP, Backus KM, Acker TM, Funabashi M, Taketani M, Donia MS, Nayfach S, Pollard KS, et al.: Discovery of reactive microbiota-derived metabolites that inhibit host proteases. Cell 2017, 168:517 – 526. e518. 35. Michelucci A, Cordes T, Ghelfi J, Pailot A, Reiling N, Goldmann O, Binz T, Wegner A, Tallam A, Rausell A, et al.: Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production. Proc Natl Acad Sci U S A 2013, 110:7820–7825. 36. Qin W, Qin K, Zhang Y, Jia W, Chen Y, Cheng B, Peng L, Chen N, Liu Y, Zhou W, et al.: S-glycosylation-based cysteine profiling reveals regulation of glycolysis by itaconate. Nat Chem Biol 2019, 15:983–991. 37. Grossman EA, Ward CC, Spradlin JN, Bateman LA, Huffman TR, Miyamoto DK, Kleinman JI, Nomura DK: Covalent ligand discovery against druggable hotspots targeted by anti-cancer natural products. Cell Chem Biol 2017, 24:1368–1376. e1364. 38. Blewett MM, Xie J, Zaro BW, Backus KM, Altman A, Teijaro JR, Cravatt BF: Chemical proteomic map of dimethyl fumaratesensitive cysteines in primary human T cells. Sci Signal 2016, 9. rs10. 39. Backus KM, Correia BE, Lum KM, Forli S, Horning BD, GonzalezPaez GE, Chatterjee S, Lanning BR, Teijaro JR, Olson AJ, et al.:
Current Opinion in Chemical Biology 2020, 54:28–36
36 Omics
Proteome-wide covalent ligand discovery in native biological systems. Nature 2016, 534:570–574. 40. Yang F, Gao J, Che J, Jia G, Wang C: A dimethyl-labelingbased strategy for site-specifically quantitative chemical proteomics. Anal Chem 2018, 90:9576–9582. 41. Flaxman HA, Woo CM: Mapping the small molecule interactome by mass spectrometry. Biochemistry 2018, 57: 186–193. 42. Guo H, Li Z: Developments of bioorthogonal handlecontaining photo-crosslinkers for photoaffinity labeling. MedChemComm 2017, 8:1585–1591. 43. Niphakis MJ, Lum KM, Cognetta 3rd AB, Correia BE, Ichu TA, Olucha J, Brown SJ, Kundu S, Piscitelli F, Rosen H, et al.: A global map of lipid-binding proteins and their ligandability in cells. Cell 2015, 161:1668–1680. 44. Gao J, Mfuh A, Amako Y, Woo CM: Small molecule interactome * mapping by photoaffinity labeling reveals binding site hotspots for the NSAIDs. J Am Chem Soc 2018, 140: 4259–4268. Authors developed a platform termed small molecule interactome mapping by photoaffinity labeling (SIM-PAL) to identify the protein interactions and direct binding site hotspots of bioactive molecule in a whole cell proteome using stable 12C: 13C isotope-targeted MS. 45. Gubbens J, Ruijter E, de Fays LE, Damen JM, de Kruijff B, Slijper M, Rijkers DT, Liskamp RM, de Kroon AI: Photocrosslinking and click chemistry enable the specific detection of proteins interacting with phospholipids at the membrane interface. Chem Biol 2009, 16:3–14.
54. Sridharan S, Kurzawa N, Werner T, Gunthner I, Helm D, Huber W, * Bantscheff M, Savitski MM: Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP. Nat Commun 2019, 10:1155. Authors mapped proteome-wide ATP-interactions by profiling thermal stability and solubility of proteins and revealed the concentrationdependent proteome-wide effect of ATP. 55. Huang JX, Lee G, Cavanaugh KE, Chang JW, Gardel ML, Moellering RE: High throughput discovery of functional protein modifications by hotspot thermal profiling. Nat Methods 2019, 16:894–901. 56. Schopper S, Kahraman A, Leuenberger P, Feng Y, Piazza I, Muller O, Boersema PJ, Picotti P: Measuring protein structural changes on a proteome-wide scale using limited proteolysiscoupled mass spectrometry. Nat Protoc 2017, 12:2391–2410. 57. Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, * * Sauer U, Picotti P: A map of protein-metabolite interactions reveals principles of chemical communication. Cell 2018, 172: 358–372. e323. Authors combined limited proteolysis (LiP) with mass spectrometry (MS) to develop a powerful chemical proteomic method (LiP-SMap) that can systematically analyze of metabolite–protein interactions and binding sites performed directly in the native cellular matrix. 58. Maurais AJ, Weerapana E: Reactive-cysteine profiling for drug discovery. Curr Opin Chem Biol 2019, 50:29–36. 59. Lin S, Yang X, Jia S, Weeks AM, Hornsby M, Lee PS, Nichiporuk RV, Iavarone AT, Wells JA, Toste FD, et al.: Redoxbased reagents for chemoselective methionine bioconjugation. Science 2017, 355:597–602.
46. Hulce JJ, Cognetta AB, Niphakis MJ, Tully SE, Cravatt BF: Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells. Nat Methods 2013, 10:259–264.
60. Hacker SM, Backus KM, Lazear MR, Forli S, Correia BE, Cravatt BF: Global profiling of lysine reactivity and ligandability in the human proteome. Nat Chem 2017, 9:1181–1190.
47. Liu X, Dong T, Zhou Y, Huang N, Lei X: Exploring the binding proteins of glycolipids with bifunctional chemical probes. Angew Chem Int Ed Engl 2016, 55:14330–14334.
61. Qin W, Qin K, Fan X, Peng L, Hong W, Zhu Y, Lv P, Du Y, * Huang R, Han M, et al.: Artificial cysteine S-Glycosylation induced by per-O-Acetylated unnatural monosaccharides during metabolic glycan labeling. Angew Chem Int Ed Engl 2018, 57:1817–1820. Authors revealed per-O-acetylated monosaccharides can form unexpected, non-enzymatic S-glycosylation of cysteine residues in vitro and in situ.
48. Hammerschmidt P, Ostkotte D, Nolte H, Gerl MJ, Jais A, Brunner HL, Sprenger HG, Awazawa M, Nicholls HT, TurpinNolan SM, et al.: CerS6-Derived sphingolipids interact with Mff and promote mitochondrial fragmentation in obesity. Cell 2019, 177:1536–1552. e1523. 49. Zhuang S, Li Q, Cai L, Wang C, Lei X: Chemoproteomic profiling of bile acid interacting proteins. ACS Cent Sci 2017, 3:501–509. 50. Parker CG, Galmozzi A, Wang Y, Correia BE, Sasaki K, Joslyn CM, Kim AS, Cavallaro CL, Lawrence RM, Johnson SR, et al.: Ligand and target discovery by fragment-based screening in human cells. Cell 2017, 168:527–541. e529. 51. Kleiner P, Heydenreuter W, Stahl M, Korotkov VS, Sieber SA: A whole proteome inventory of background photocrosslinker binding. Angew Chem Int Ed Engl 2017, 56:1396–1401. 52. Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF, Eberhard D, Martinez Molina D, Jafari R, Dovega RB, Klaeger S, et al.: Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 2014, 346:1255784. 53. Huber KV, Olek KM, Muller AC, Tan CS, Bennett KL, Colinge J, Superti-Furga G: Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat Methods 2015, 12:1055–1057.
Current Opinion in Chemical Biology 2020, 54:28–36
62. Palaniappan KK, Pitcher AA, Smart BP, Spiciarich DR, Iavarone AT, Bertozzi CR: Isotopic signature transfer and mass pattern prediction (IsoStamp): an enabling technique for chemically-directed proteomics. ACS Chem Biol 2011, 6: 829–836. 63. Dai J, Liang K, Zhao S, Jia W, Liu Y, Wu H, Lv J, Cao C, Chen T, Zhuang S, et al.: Chemoproteomics reveals baicalin activates hepatic CPT1 to ameliorate diet-induced obesity and hepatic steatosis. Proc Natl Acad Sci U S A 2018, 115:5896–5905. 64. Chick JM, Kolippakkam D, Nusinow DP, Zhai B, Rad R, Huttlin EL, Gygi SP: A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat Biotechnol 2015, 33: 743–749. 65. Schelling M, Hopf TA, Rost B: Evolutionary couplings and sequence variation effect predict protein binding sites. Proteins 2018, 86:1064–1074.
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Chemoproteomic profiling of protein–metabolite interactions Wei Qin1,2,3,4, Fan Yang1,2,3,5 and Chu Wang1,2,3,4,5 Abstract
Small molecule metabolites play important roles in regulating protein functions, which are acted through either covalent nonenzymatic post-translational modifications or non-covalent binding interactions. Chemical proteomic strategies can help delineate global landscapes of cellular protein–metabolite interactions and provide molecular insights about their mechanisms of action. In this review, we summarized the recent progress in developments and applications of chemoproteomic strategies to profile protein–metabolite interactions. Addresses 1 Synthetic and Functional Biomolecules Center, Peking University, Beijing, China 2 Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China 3 Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China 4 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China 5 College of Chemistry and Molecular Engineering, Peking University, Beijing, China Corresponding author: Wang, Chu ([email protected])
Current Opinion in Chemical Biology 2020, 54:28–36 This review comes from a themed issue on Omics Edited by Raymond Moellering For a complete overview see the Issue and the Editorial
https://doi.org/10.1016/j.cbpa.2019.11.003 1367-5931/© 2019 Elsevier Ltd. All rights reserved.
Keywords Chemoproteomics, Protein-metabolite interactions, Post-translational modifications, Non-covalent binding, Activity-based protein profiling.
Introduction Small molecule metabolites play essential roles in maintaining cell physiology, such as feeding energy production cycles, activating signaling receptors, mediating cellular communications, etc. The diversified functions of metabolites are mainly acted by engagement with proper protein targets in cells, and it is, therefore, crucial to identify such proteinemetabolite interactions (PMIs), to decipher the physiological roles of each metabolite. However, the scale and complexity Current Opinion in Chemical Biology 2020, 54:28–36
of the PMI network in a cell present a formidable challenge for traditional genetic and biochemical strategies. For example, it has been estimated that in a bacterial cell, there are at least one million protein molecules and about 100-fold more metabolites exist, suggesting an extremely intermingled and convoluted PMI network [1,2]. A systematic approach to interrogate the crosstalk between proteins and metabolites will be highly desirable to gain better understanding of diverse biological processes such as transcription, signaling transduction, and metabolism [3]. PMIs could be generally classified into two major types: non-covalent PMIs and covalent post-translational modifications (PTMs) that are not enzymatically mediated, but instead directly induced by reactive metabolites themselves. Non-covalent PMIs include enzyme/substrate complexes, receptor/ligand complexes, and channel/cargo complexes [4]. Some metabolites could also bind to the enzyme away from its active site as allosteric cofactors, which could regulate the protein function in a rapid and reversible manner [4]. On the other hand, covalent PMIs usually occur when metabolites with reactive functional groups encounter nucleophilic residues such as cysteines and lysines, directly forming covalent modifications on the target proteins [5]. Examples of such non-enzymatic modifications include sulfenylation [6], glycation [7**], carbonylation [8], and non-enzymatic acylation [9], all of which have been shown to regulate protein functions. Compared to the traditional metabolite-centric methods that have been widely applied for ‘pulling down’ the interacting proteins of a particular metabolite in cell lysates [10], chemical proteomics offers another attractive approach to systematically map PMIs in an unbiased manner. It usually relies on specific chemical probes that can covalently capture PMI of interest from proteomes of living cells and enrich them for analysis by quantitative proteomic tools. With properly designed probes, the strategy can be flexibly applied to analyze both non-covalent and covalent PMIs. In this review, we will summarize recent advances in chemoproteomic approaches to profile PMIs and discuss perspectives for future method development.
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Direct capture methods based on chemical reactivity
later conjugated with a biotin affinity tag via bioorthogonal reactions such as Cu(I)-catalyzed azidee alkyne cycloaddition (CuAAC). The probe-labeled proteins can be enriched by streptavidin beads and identified by standard shotgun proteomics. To achieve sitespecific analysis of these covalent PMIs, orthogonal cleavable linkers are introduced into the biotin tags [13] and seamlessly integrated with chemical proteomic workflows (Figure 1a). In such a ‘tandem orthogonal proteolysis (TOP)’ workflow, after the streptavidin enrichment and trypsin digestion of the labeled proteins, the probe-adducted peptides could be specifically released by the second round of linker cleavage and analyzed by LC-MS/MS to locate the exact site of modification. Moreover, stable isotope-encoded cleavable linkers have been developed for quantitative analysis [14,15] of metabolite-induced modifications.
Certain metabolite-induced modifications contain unique functional moieties that could be selectively reacted with chemical probes with properly designed warheads (Figure 1a). Such chemoselective probes for direct capture often bear a reporter handle, which can be
Chemoselective probes have been developed for covalently capturing different types of metabolite-induced modifications (Figure 1b). For example, when cells are under oxidative stress, lipid-derived electrophiles
Chemoproteomic methods for mapping metabolite-induced protein modifications Metabolite-induced protein modifications are spontaneous and mostly occur on residues with enhanced chemical reactivity (e.g., cysteines and lysines). Current chemoproteomic methods to profile such nonenzymatic endogenous modifications can be generally classified into two approaches: (1) direct capture methods based on chemical reactivity; (2) competitive profiling of reactive residues. While the ‘bioorthogonal analogues’ probes have also been widely adopted (e.g., for glycosylation [11] and lipidation [12]), they could only be exogenously fed to cells to mimic endogenous metabolite-induced protein modifications and, therefore, will not be extensively discussed here.
Figure 1
Chemical proteomic approaches for direct capture of metabolite-induced modification by chemoselective probes. (a) Workflow for the sitespecific chemoproteomic analysis of metabolite-induced modifications. The structures of representative acid cleavable tags are shown. (b) Representative chemoselective reactions between metabolite-induced modifications and chemical probes.
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(LDEs) containing a,b-unsaturated aldehyde or ketone groups can react with nucleophilic sidechains of cysteines, lysines, and histidines via Michael addition, leaving a free carbonyl moiety on the modified protein [16]. To capture such ‘carbonylations,” several aldehydedirected probes with hydrazide, aminooxy, and aniline warheads have been developed and applied in the chemoproteomic pipeline to profile sites of modification induced by LDEs such as 4-hydroxy-2-nonenal (HNE) and acrolein [17-19**]. Moreover, thiol groups of cysteines can be reversibly oxidized to sulfenic acids or sulfinic acids, which can be captured by probes derived from dimedone analogues and electrophilic nitrogen species (ENS), respectively [20,21]. Clickable dimedone and ENS probes have enabled site-centric proteomic analysis of S-sulfenylations and S-sulfinylations [6,22].
Taking advantage of the site-specific resolution and quantitative power of isoTOP-ABPP, a competitive isoTOP-ABPP strategy was developed for identifying cysteine modifications induced by reactive metabolites such as LDEs [32] (Figure 2). It operates on the rationale that the cysteine modification induced by LDEs could compete the labeling of IA-alkyne, and the competition ratio of each cysteine could be quantified through the isoTOP-ABPP analysis. In this study, the reactivity profile of several LDEs against over 1000 cysteines was accurately quantified in parallel, and a group of ‘hot spot’ residues for LDE modifications were revealed. Moreover, this strategy has been extended to identify the cysteine modifications of microbiotaderived metabolite dipeptide aldehyde and fumarate, which is a covalent oncometabolite accumulated in renal cell carcinoma [33,34].
Chemoselective probes have also been developed for Snitrosylation, S-sulfhydration, and carbamylation, which are induced by gaseous signaling molecules such as nitric oxide (NO), hydrogen sulfide (H2S), and carbon dioxide (CO2) [23e25]. Interestingly, nitrosothiols can react with sulfinic acids to form a stable thiosulfonate bond, and such a ‘reciprocal’ activity has been leveraged to enrich endogenous S-nitrosated and S-sulfinated proteins using the sulfonate- and S-nitrosothiol-linked probes, respectively [26]. More recently, alkynyl thioester probes were developed to label homocysteinylation on lysines, and a hydroxylamine probe was used to capture phosphorylation on aspartic acids [27,28].
More recently, we applied the competitive isoTOPABPP strategy to profile cysteine modifications induced by itaconate, an abundant anti-inflammatory metabolite with weak electrophilicity in lipopolysaccharide-stimulated (LPS-stimulated) macrophages [35]. IA-alkyne was first used to label cysteines in proteomes; however, no effective competition was observed by itaconate, probably due to its intrinsically weak electrophilicity. To overcome this problem, we drew inspirations from an ‘off-target’ Sglycosylation reaction of the per-O-acetylated unnatural sugar probe, which has been traditionally used in metabolic glycan labeling, and we developed a cysteine-reactive monosaccharide probe that could be effectively competed by itaconate. Our competitive profiling with the new S-glycosylation probe identified 260 itaconate-reactive cysteines in macrophage proteomes and revealed that itaconate could modify key cysteines of glycolytic enzymes to inhibit glycolysis, which in turn mediates its antiinflammatory effect [36]. The example highlights the importance of using chemical probes for the competitive profiling that have the ‘matching’ reactivity with the metabolite of interest. It also warrants continuous development of new chemical probes with enhanced selectivity to different reactive residues in proteomes.
Competitive profiling of reactive residues
The direct capture approach is suitable only for metabolite-induced modifications with reactive functional moiety. Considering that covalent PMIs often occur on reactive residues such as cysteines and lysines, it is possible to implement a competitive profiling strategy to map metabolite-induced modifications on these residues that are too chemically inert to be directly captured. We here focused on the competitive isoTOP-ABPP profiling of cysteine modifications, as it is one of the most widely implemented strategies for such purposes. Activity-based protein profiling (ABPP) is a versatile platform to monitor the functional states of enzymes by active site-directed chemical probes, which was originally developed for enzyme and drug discovery [29]. The technology was enhanced for systematic identification of reactive cysteines in native proteomes by TOPABPP analysis with a thiol-reactive iodoacetamide (IA)alkyne probe [30]. Moreover, the chemical reactivity of cysteines in proteomes could be quantitatively ranked and correlated with their functional importance by the isotopic TOP-ABPP (‘isoTOP-ABPP’) analysis [31]. Current Opinion in Chemical Biology 2020, 54:28–36
It is noteworthy that competitive isoTOP-ABPP can be applied to profile cysteine modifications not only by reactive metabolites, but also by reactive natural products or covalent drugs [37,38]. It has also been implemented to evaluate the reactivity of a cysteinereactive small-molecule fragment library and to discover unique anchor sites from a large number of proteins that are previously deemed as undruggable [39]. More recently, the pipeline of competitive isoTOP-ABPP was adapted to be compatible with reductive demethylation, as well as different cleavable azide-biotin tags, which not only expands its analytical www.sciencedirect.com
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Figure 2
Workflow of the competitive isoTOP-ABPP analysis for identification of metabolite-induced cysteine modifications. Representative cysteine probes and reactive metabolites are shown.
capacity from duplex to triplex quantification, but also significantly reduces the cost of the method for easy accessibility [40].
Methods for profiling non-covalent protein–metabolite interactions Many metabolites regulate protein functions in a noncovalent manner, which is usually transient and with weak affinity within a complicated cellular environment. An ideal profiling strategy should allow the capture of functional PMIs directly in living cells and then enrichment of them for subsequent identification. In recent years, photo-affinity chemical probes have emerged as powerful tools to study non-covalent interactions between target metabolites and proteomes [41]. A photo-affinity probe typically contains three components: (1) a binding scaffold based on the metabolite structure that can direct the probe to its genuine targets, (2) a photo-reactive group that transforms reversible interactions into covalent adducts upon ultraviolet (UV) irradiation, and (3) a bio-orthogonal
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group (e.g., alkyne and azide) that allows the following conjugation with biotin tags for enrichment (Figure 3a). Different types of photo-reactive groups have been developed including a suite of ‘minimalist’ photocrosslinkers [42]. In combination with quantitative proteomics such as stable-isotope labeling by amino acids in cell culture (SILAC), the protein targets can be identified by LC-MS/MS analysis and the extent of binding engagement can be quantified [43] (Figure 3a). To minimize false identification due to non-specific binding, additional experiments with UV-dependent labeling and/or competition by the native metabolite should be performed to obtain genuine PMIs with high confidence. In certain cases, the binding site of a photoreactive probe on the target protein could be mapped by locating the probe-adducted peptide regions with unique isotopic patterns [44*]. In the context of PMIs, photo-affinity labeling has been successfully applied to capture the interacting proteins of various types of lipid metabolites, including Current Opinion in Chemical Biology 2020, 54:28–36
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phospholipids [45], cholesterol [46], glycolipids [47], and sphingolipid [48] (Figure 3b). By synthesizing a panel of photo-reactive lipid probes featuring archidonoyl, oleoyl, palmitoyl, and stearoyl acyl chains, the Cravatt group established a comprehensive map of lipidbinding proteins and surveyed their ligandability in living cells [43]. Recently, our group also developed a number of structurally diverse and photo-reactive bile acid (BA) probes to globally profile BA-protein interactions in mammalian cells and uncovered a large number of novel BA-binding proteins with potential functional implications [49]. Implemented in a
competitive profiling approach, the photo-affinity probes can also be used for mapping thousands of reversible ligandeprotein interactions in human cancer cell, facilitating rapid fragment-based ligand discovery for undruggable protein targets [50]. Despite its robustness, there are still certain drawbacks limiting the wide application of the photo-affinity labeling approach. First, the design of the photo-reactive probes needs to be guided by structureeactivity relationship of the ligand because the introduction of the photo-reactive and bio-orthogonal group may affect its
Figure 3
Identification of non-covalent protein–metabolite interactions by photo-affinity labeling. (a) Workflow of the UV-dependent (upper) and competition (below) experiments using SILAC-ABPP. Structures of the representative photo-crosslinkers are shown. (b) Structures of representative photo-reactive probes with the photo-crosslinkers and bioorthogonal groups shown in yellow and red, respectively.
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binding affinity and selectivity to the target proteins. Second, synthesis of the photo-reactive probes is very technically demanding and cannot be generalized to any metabolites of interest. Last but not the least, it has been shown that photo-crosslinkers may have ‘nonspecific’ binding, too, which could contaminate the validity of mass spectrometry (MS) results [51]. It is, therefore, important that any target that is selected for further functional annotation should be carefully validated by orthogonal biochemical methods.
Label-free strategies for mapping protein–metabolite interactions in proteomes As an alternative to the photo-affinity labeling strategy, label-free methods for detecting endogenous noncovalent PMIs have gained more and more attention recently. Representative approaches include the thermal proteome profiling (TPP) and the limited proteolysis-coupled MS (LiP-MS), both of which rely on determining the change of physical parameters of the target proteins as induced by the metabolite binding at the proteomic scale (Figure 4). TPP integrates multiplex quantitative proteomics with the cellular thermal shift assay (CETSA), enabling identification of proteins with significantly shifted melting temperatures in presence of a small molecule
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[52] (Figure 4a). The cellular targets of endogenous metabolites, such as 20 30 -cGAMP and ATP, have been systematically investigated in living cells by TPP [53,54*]. Very recently, the Moellering group developed a hotspot TPP strategy for high throughput discovery of functional protein modifications by analyzing the impact of site-specific PTMs on protein thermal stability [55]. LiP-MS measures the protein structural changes by a quick treatment of cell extracts with a promiscuous protease to generate confirmation-dependent proteolytic patterns [56] (Figure 4b). The Picotti group has developed a method named ‘LiP-small molecule mapping (LiP-SMap)’ to globally detect proteins that become differentially susceptible to protease cleavage in response to binding of endogenous metabolites, establishing a comprehensive database containing 1678 PMIs as well as 7345 putative binding sites [57**]. Remarkably, false discovery rate was stringently controlled by comparison with previously known PMIs, which suggests the high confidence for a large fraction of novel PMIs detected in this dataset. It should be noted that metabolites may indirectly induce changes on protein stability and conformational changes through a connected proteineprotein interaction network, which could also be captured by the aforementioned label-free profiling strategies. It is also possible that some weak PMIs may fail to induce any
Figure 4
Label-free strategies for proteomic mapping protein–metabolite interactions. (a) Workflow of the thermal proteome profiling for identification of protein–metabolite interactions. (b) Workflow of the limited proteolysis-coupled MS for identification of protein–metabolite interactions.
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detectable stability and structural changes. Compared to the photo-affinity labeling approaches, these methods lack the step of enrichment and may not be suitable for PMIs with substoichiometric binding engagements. Lastly, successful implementation of TPP and LipSMap requires extensive proteomic runs with demanding sample-to-sample reproducibility, and such experimental setups and expertise may not be readily available to regular labs.
Conclusions and future outlook PMIs play diverse roles in regulating many important biological processes. While traditional biochemical and genetic methods have functionally characterized individual PMIs case-by-case, chemical proteomics aims to establish a global landscape of binding proteins for a given metabolite of interest in a complexed system. Depending on the nature of the interaction (e.g., covalent modification versus non-covalent binding), different strategies have been developed accordingly, as we have summarized above, and a large number of new PMIs have been revealed. However, considering the complexity of life, what have been discovered only constitutes ‘the tip of an iceberg,” and challenges remain significant in the field, which warrant development of new methods with enhanced sensitivity and coverage for PMI profiling. First, new chemical probes are desperately needed to cover reactive proteomes beyond cysteines. A variety of chemical probes have been developed to profile cysteines, with distinct reactivity and substrate selectivity [58]. Although cysteines are functionally critical for many enzymes, they are generally less abundant in proteomes, and it is conceivable that competitive isoTOP-ABPP using reactive probes toward other types of amino acids would significantly expand the coverage of potential PMI landscape. While the probes of lysine and methionine have been implemented for profiling in a similar manner [59,60], residue-specific probes for histidines, tyrosines, arginines, carboxyl sidechains, etc. are still waiting to be developed. Second, the resolution of the profiling methods needs to be improved from the protein level to the site level. It is particularly important for probes with promiscuous reactivity, as the resulting list of target proteins might be otherwise largely contaminated. For example, with a cleavable azide-biotin linker, we discovered an unexpected S-glycosylation reaction for the per-O-acetylated unnatural monosaccharides, which has been widely used imaging and profiling of O-glycosylation [61*]. Such site-specific analysis should be implemented not only for covalent PTMs, but also for photo-affinity probes used for capturing non-covalent PMIs. While isotopically encoded enrichment tags and associated algorithms have been developed to facilitate mapping binding sites Current Opinion in Chemical Biology 2020, 54:28–36
of the photo-affinity probes [44,62], in silico docking simulations could also be considered when structural information is available [63]. Third, chemoproteomic methods should be developed with more compatibility with in situ or in vivo profiling. As most of the direct capture methods and competitive profiling for mapping covalent PMIs are currently performed in cell lysates, cell penetration and toxicity of the probes need further improvement to enable spatial and temporal profiling of modifications in living contexts. While certain photo-affinity probes might be able to cross plasma membranes to get inside cells, label-free methods investigating non-covalent PMIs always record the protein structural information in vitro, which could change when cells are lysed. It is, therefore, highly desired to develop label-free proteomic approaches that can be directly applied in living cells. Lastly, with the rapid development of the metabolomics technology, it is anticipated that more and more metabolites with unique reactivity will be discovered, and investigating their interactions with the proteomes might require new enabling tools, which include, but are not limited to, strategies to search unknown PTMs [64], methods to detect metabolite crosslinking [7], and algorithms to discover residue networks for metabolite binding [65]. Nevertheless, with enhanced sensitivity and easy accessibility, chemoproteomic will become a standard tool to interrogate PMIs and provide valuable clues for studying the mechanisms of action of bioactive metabolites.
Conflict of interest statement Nothing declared.
Acknowledgements We acknowledge the funding support by the National Key Research and Development Projects (No. 2016YFA0501500) and the National Natural Science Foundation of China (No. 21521003, No. 81490740 and No. 21778004).
References Papers of particular interest, published within the period of review, have been highlighted as: * of special interest * * of outstanding interest 1.
Milo R: What is the total number of protein molecules per cell volume? A call to rethink some published values. Bioessays 2013, 35:1050–1055.
2.
Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD: Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol 2009, 5:593–599.
3.
Chubukov V, Gerosa L, Kochanowski K, Sauer U: Coordination of microbial metabolism. Nat Rev Microbiol 2014, 12:327–340.
4.
Gerosa L, Sauer U: Regulation and control of metabolic fluxes in microbes. Curr Opin Biotechnol 2011, 22:566–575.
www.sciencedirect.com
PMI profiling by chemoproteomics Qin et al.
5.
Harmel R, Fiedler D: Features and regulation of non-enzymatic post-translational modifications. Nat Chem Biol 2018, 14: 244–252.
6.
Yang J, Gupta V, Carroll KS, Liebler DC: Site-specific mapping and quantification of protein S-sulphenylation in cells. Nat Commun 2014, 5:4776.
7. **
Bollong MJ, Lee G, Coukos JS, Yun H, Zambaldo C, Chang JW, Chin EN, Ahmad I, Chatterjee AK, Lairson LL, et al.: A metabolite-derived protein modification integrates glycolysis with KEAP1-NRF2 signalling. Nature 2018, 562:600–604. Authors revealed a direct link between glycolysis and NRF2 signaling. Inhibition of the glycolytic enzyme PGK1 results in accumulation of the methylglyoxal, which modifies KEAP1 to form crosslinking between proximal cysteine and arginine residues. 8.
Hauck AK, Huang Y, Hertzel AV, Bernlohr DA: Adipose oxidative stress and protein carbonylation. J Biol Chem 2019, 294: 1083–1088.
9.
Trub AG, Hirschey MD: Reactive acyl-CoA species modify proteins and induce carbon stress. Trends Biochem Sci 2018, 43:369–379.
10. Guo H, Peng H, Emili A: Mass spectrometry methods to study protein-metabolite interactions. Expert Opin Drug Discov 2017, 12:1271–1280. 11. Hao Y, Fan X, Shi Y, Zhang C, Sun DE, Qin K, Qin W, Zhou W, Chen X: Next-generation unnatural monosaccharides reveal that ESRRB O-GlcNAcylation regulates pluripotency of mouse embryonic stem cells. Nat Commun 2019, 10:4065. 12. Storck EM, Morales-Sanfrutos J, Serwa RA, Panyain N, LanyonHogg T, Tolmachova T, Ventimiglia LN, Martin-Serrano J, Seabra MC, Wojciak-stothard B, et al.: Dual chemical probes enable quantitative system-wide analysis of protein prenylation and prenylation dynamics. Nat Chem 2019, 11:552–561. 13. Szychowski J, Mahdavi A, Hodas JJ, Bagert JD, Ngo JT, Landgraf P, Dieterich DC, Schuman EM, Tirrell DA: Cleavable biotin probes for labeling of biomolecules via azide-alkyne cycloaddition. J Am Chem Soc 2010, 132:18351–18360. 14. Yang J, Tallman KA, Porter NA, Liebler DC: Quantitative chemoproteomics for site-specific analysis of protein alkylation by 4-hydroxy-2-nonenal in cells. Anal Chem 2015, 87: 2535–2541. 15. Qin K, Zhu Y, Qin W, Gao J, Shao X, Wang YL, Zhou W, Wang C, Chen X: Quantitative profiling of protein O-GlcNAcylation sites by an isotope-tagged cleavable linker. ACS Chem Biol 2018, 13:1983–1989. 16. Chen Y, Qin W, Wang C: Chemoproteomic profiling of protein modifications by lipid-derived electrophiles. Curr Opin Chem Biol 2016, 30:37–45. 17. Chen Y, Cong Y, Quan B, Lan T, Chu X, Ye Z, Hou X, Wang C: Chemoproteomic profiling of targets of lipid-derived electrophiles by bioorthogonal aminooxy probe. Redox Biol 2017, 12: 712–718. 18. Chen Y, Liu Y, Lan T, Qin W, Zhu Y, Qin K, Gao J, Wang H, * * Hou X, Chen N, et al.: Quantitative profiling of protein carbonylations in ferroptosis by an aniline-derived probe. J Am Chem Soc 2018, 140:4712–4720. Authors used a quantitative chemoproteomic method to detecting endogenous target proteins and residue sites of carbonylation by a new aniline-based probe during ferroptosis, and validated that a functional cysteine C210 in the channel protein VDAC2 might play an important role in mediating ferroptosis. 19. Chen Y, Liu Y, Hou X, Ye Z, Wang C: Quantitative and sitespecific chemoproteomic profiling of targets of acrolein. Chem Res Toxicol 2019, 32:467–473. 20. Paulsen CE, Truong TH, Garcia FJ, Homann A, Gupta V, Leonard SE, Carroll KS: Peroxide-dependent sulfenylation of the EGFR catalytic site enhances kinase activity. Nat Chem Biol 2011, 8:57–64. 21. Lo Conte M, Carroll KS: Chemoselective ligation of sulfinic acids with aryl-nitroso compounds. Angew Chem Int Ed Engl 2012, 51:6502–6505.
www.sciencedirect.com
35
22. Akter S, Fu L, Jung Y, Conte ML, Lawson JR, Lowther WT, Sun R, Liu K, Yang J, Carroll KS: Chemical proteomics reveals new targets of cysteine sulfinic acid reductase. Nat Chem Biol 2018, 14:995–1004. 23. Doulias PT, Tenopoulou M, Greene JL, Raju K, Ischiropoulos H: Nitric oxide regulates mitochondrial fatty acid metabolism through reversible protein S-nitrosylation. Sci Signal 2013, 6. rs1. 24. Zhang D, Macinkovic I, Devarie-Baez NO, Pan J, Park CM, Carroll KS, Filipovic MR, Xian M: Detection of protein S-sulfhydration by a tag-switch technique. Angew Chem Int Ed Engl 2014, 53:575–581. 25. Linthwaite VL, Janus JM, Brown AP, Wong-Pascua D, O’Donoghue AC, Porter A, Treumann A, Hodgson DRW, Cann MJ: The identification of carbon dioxide mediated protein post-translational modifications. Nat Commun 2018, 9: 3092. 26. Majmudar JD, Konopko AM, Labby KJ, Tom CT, Crellin JE, Prakash A, Martin BR: Harnessing redox cross-reactivity to profile distinct cysteine modifications. J Am Chem Soc 2016, 138:1852–1859. 27. Chen N, Liu J, Qiao Z, Liu Y, Yang Y, Jiang C, Wang X, Wang C: Chemical proteomic profiling of protein N-homocysteinylation with a thioester probe. Chem Sci 2018, 9: 2826–2830. 28. Chang JW, Montgomery JE, Lee G, Moellering RE: Chemoproteomic profiling of phosphoaspartate modifications in prokaryotes. Angew Chem Int Ed Engl 2018, 57:15712–15716. 29. Niphakis MJ, Cravatt BF: Enzyme inhibitor discovery by activity-based protein profiling. Annu Rev Biochem 2014, 83: 341–377. 30. Weerapana E, Simon GM, Cravatt BF: Disparate proteome reactivity profiles of carbon electrophiles. Nat Chem Biol 2008, 4:405–407. 31. Weerapana E, Wang C, Simon GM, Richter F, Khare S, Dillon MB, Bachovchin DA, Mowen K, Baker D, Cravatt BF: Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 2010, 468:790–795. 32. Wang C, Weerapana E, Blewett MM, Cravatt BF: A chemoproteomic platform to quantitatively map targets of lipid-derived electrophiles. Nat Methods 2014, 11:79–85. 33. Kulkarni RA, Bak DW, Wei D, Bergholtz SE, Briney CA, Shrimp JH, Alpsoy A, Thorpe AL, Bavari AE, Crooks DR, et al.: A chemoproteomic portrait of the oncometabolite fumarate. Nat Chem Biol 2019, 15:391–400. 34. Guo CJ, Chang FY, Wyche TP, Backus KM, Acker TM, Funabashi M, Taketani M, Donia MS, Nayfach S, Pollard KS, et al.: Discovery of reactive microbiota-derived metabolites that inhibit host proteases. Cell 2017, 168:517 – 526. e518. 35. Michelucci A, Cordes T, Ghelfi J, Pailot A, Reiling N, Goldmann O, Binz T, Wegner A, Tallam A, Rausell A, et al.: Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production. Proc Natl Acad Sci U S A 2013, 110:7820–7825. 36. Qin W, Qin K, Zhang Y, Jia W, Chen Y, Cheng B, Peng L, Chen N, Liu Y, Zhou W, et al.: S-glycosylation-based cysteine profiling reveals regulation of glycolysis by itaconate. Nat Chem Biol 2019, 15:983–991. 37. Grossman EA, Ward CC, Spradlin JN, Bateman LA, Huffman TR, Miyamoto DK, Kleinman JI, Nomura DK: Covalent ligand discovery against druggable hotspots targeted by anti-cancer natural products. Cell Chem Biol 2017, 24:1368–1376. e1364. 38. Blewett MM, Xie J, Zaro BW, Backus KM, Altman A, Teijaro JR, Cravatt BF: Chemical proteomic map of dimethyl fumaratesensitive cysteines in primary human T cells. Sci Signal 2016, 9. rs10. 39. Backus KM, Correia BE, Lum KM, Forli S, Horning BD, GonzalezPaez GE, Chatterjee S, Lanning BR, Teijaro JR, Olson AJ, et al.:
Current Opinion in Chemical Biology 2020, 54:28–36
36 Omics
Proteome-wide covalent ligand discovery in native biological systems. Nature 2016, 534:570–574. 40. Yang F, Gao J, Che J, Jia G, Wang C: A dimethyl-labelingbased strategy for site-specifically quantitative chemical proteomics. Anal Chem 2018, 90:9576–9582. 41. Flaxman HA, Woo CM: Mapping the small molecule interactome by mass spectrometry. Biochemistry 2018, 57: 186–193. 42. Guo H, Li Z: Developments of bioorthogonal handlecontaining photo-crosslinkers for photoaffinity labeling. MedChemComm 2017, 8:1585–1591. 43. Niphakis MJ, Lum KM, Cognetta 3rd AB, Correia BE, Ichu TA, Olucha J, Brown SJ, Kundu S, Piscitelli F, Rosen H, et al.: A global map of lipid-binding proteins and their ligandability in cells. Cell 2015, 161:1668–1680. 44. Gao J, Mfuh A, Amako Y, Woo CM: Small molecule interactome * mapping by photoaffinity labeling reveals binding site hotspots for the NSAIDs. J Am Chem Soc 2018, 140: 4259–4268. Authors developed a platform termed small molecule interactome mapping by photoaffinity labeling (SIM-PAL) to identify the protein interactions and direct binding site hotspots of bioactive molecule in a whole cell proteome using stable 12C: 13C isotope-targeted MS. 45. Gubbens J, Ruijter E, de Fays LE, Damen JM, de Kruijff B, Slijper M, Rijkers DT, Liskamp RM, de Kroon AI: Photocrosslinking and click chemistry enable the specific detection of proteins interacting with phospholipids at the membrane interface. Chem Biol 2009, 16:3–14.
54. Sridharan S, Kurzawa N, Werner T, Gunthner I, Helm D, Huber W, * Bantscheff M, Savitski MM: Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP. Nat Commun 2019, 10:1155. Authors mapped proteome-wide ATP-interactions by profiling thermal stability and solubility of proteins and revealed the concentrationdependent proteome-wide effect of ATP. 55. Huang JX, Lee G, Cavanaugh KE, Chang JW, Gardel ML, Moellering RE: High throughput discovery of functional protein modifications by hotspot thermal profiling. Nat Methods 2019, 16:894–901. 56. Schopper S, Kahraman A, Leuenberger P, Feng Y, Piazza I, Muller O, Boersema PJ, Picotti P: Measuring protein structural changes on a proteome-wide scale using limited proteolysiscoupled mass spectrometry. Nat Protoc 2017, 12:2391–2410. 57. Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, * * Sauer U, Picotti P: A map of protein-metabolite interactions reveals principles of chemical communication. Cell 2018, 172: 358–372. e323. Authors combined limited proteolysis (LiP) with mass spectrometry (MS) to develop a powerful chemical proteomic method (LiP-SMap) that can systematically analyze of metabolite–protein interactions and binding sites performed directly in the native cellular matrix. 58. Maurais AJ, Weerapana E: Reactive-cysteine profiling for drug discovery. Curr Opin Chem Biol 2019, 50:29–36. 59. Lin S, Yang X, Jia S, Weeks AM, Hornsby M, Lee PS, Nichiporuk RV, Iavarone AT, Wells JA, Toste FD, et al.: Redoxbased reagents for chemoselective methionine bioconjugation. Science 2017, 355:597–602.
46. Hulce JJ, Cognetta AB, Niphakis MJ, Tully SE, Cravatt BF: Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells. Nat Methods 2013, 10:259–264.
60. Hacker SM, Backus KM, Lazear MR, Forli S, Correia BE, Cravatt BF: Global profiling of lysine reactivity and ligandability in the human proteome. Nat Chem 2017, 9:1181–1190.
47. Liu X, Dong T, Zhou Y, Huang N, Lei X: Exploring the binding proteins of glycolipids with bifunctional chemical probes. Angew Chem Int Ed Engl 2016, 55:14330–14334.
61. Qin W, Qin K, Fan X, Peng L, Hong W, Zhu Y, Lv P, Du Y, * Huang R, Han M, et al.: Artificial cysteine S-Glycosylation induced by per-O-Acetylated unnatural monosaccharides during metabolic glycan labeling. Angew Chem Int Ed Engl 2018, 57:1817–1820. Authors revealed per-O-acetylated monosaccharides can form unexpected, non-enzymatic S-glycosylation of cysteine residues in vitro and in situ.
48. Hammerschmidt P, Ostkotte D, Nolte H, Gerl MJ, Jais A, Brunner HL, Sprenger HG, Awazawa M, Nicholls HT, TurpinNolan SM, et al.: CerS6-Derived sphingolipids interact with Mff and promote mitochondrial fragmentation in obesity. Cell 2019, 177:1536–1552. e1523. 49. Zhuang S, Li Q, Cai L, Wang C, Lei X: Chemoproteomic profiling of bile acid interacting proteins. ACS Cent Sci 2017, 3:501–509. 50. Parker CG, Galmozzi A, Wang Y, Correia BE, Sasaki K, Joslyn CM, Kim AS, Cavallaro CL, Lawrence RM, Johnson SR, et al.: Ligand and target discovery by fragment-based screening in human cells. Cell 2017, 168:527–541. e529. 51. Kleiner P, Heydenreuter W, Stahl M, Korotkov VS, Sieber SA: A whole proteome inventory of background photocrosslinker binding. Angew Chem Int Ed Engl 2017, 56:1396–1401. 52. Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF, Eberhard D, Martinez Molina D, Jafari R, Dovega RB, Klaeger S, et al.: Tracking cancer drugs in living cells by thermal profiling of the proteome. Science 2014, 346:1255784. 53. Huber KV, Olek KM, Muller AC, Tan CS, Bennett KL, Colinge J, Superti-Furga G: Proteome-wide drug and metabolite interaction mapping by thermal-stability profiling. Nat Methods 2015, 12:1055–1057.
Current Opinion in Chemical Biology 2020, 54:28–36
62. Palaniappan KK, Pitcher AA, Smart BP, Spiciarich DR, Iavarone AT, Bertozzi CR: Isotopic signature transfer and mass pattern prediction (IsoStamp): an enabling technique for chemically-directed proteomics. ACS Chem Biol 2011, 6: 829–836. 63. Dai J, Liang K, Zhao S, Jia W, Liu Y, Wu H, Lv J, Cao C, Chen T, Zhuang S, et al.: Chemoproteomics reveals baicalin activates hepatic CPT1 to ameliorate diet-induced obesity and hepatic steatosis. Proc Natl Acad Sci U S A 2018, 115:5896–5905. 64. Chick JM, Kolippakkam D, Nusinow DP, Zhai B, Rad R, Huttlin EL, Gygi SP: A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat Biotechnol 2015, 33: 743–749. 65. Schelling M, Hopf TA, Rost B: Evolutionary couplings and sequence variation effect predict protein binding sites. Proteins 2018, 86:1064–1074.
www.sciencedirect.com