Mass spectrometry analysis of the structural proteome

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ScienceDirect Mass spectrometry analysis of the structural proteome Natalie de Souza1,2 and Paola Picotti1 Mass spectrometry (MS)-based proteomics is moving beyond the simple generation of protein inventories of biological samples. The ability of MS to quantitatively probe complex protein mixtures is increasingly being used to study protein structural and biophysical properties at proteome-scale. MS provides a readout for proteome-wide structural alterations, folding and stability, aggregation, and molecular interactions, all in native-like conditions such as cell lysates or even intact cells. We provide an overview of methods that yield such proteomewide structural information, covering cross-linking-MS, limited proteolysis-MS, co-fractionation-MS, hydroxyl radical footprinting-MS, thermal proteome profiling, and numerous approaches for monitoring molecular interactions at large scale. Methods to determine structural properties of the native proteome will drive structural systems biology. Addresses 1 Institute for Molecular Systems Biology, Department of Biology, ETH-Zu¨rich, Zu¨rich, Switzerland 2 Department of Quantitative Biomedicine, University of Zu¨rich, Zu¨rich, Switzerland Corresponding author: Picotti, Paola ([email protected])

Current Opinion in Structural Biology 2020, 60:57–65 This review comes from a themed issue on Folding and binding Edited by Shachi Gosavi and Ben Schuler

https://doi.org/10.1016/j.sbi.2019.10.006 0959-440X/ã 2019 Elsevier Ltd. All rights reserved.

crowded environment of the cell, proteins interact with each other and with other molecules, are post-translationally modified, and are subject to changes of the cellular matrix. All these phenomena can affect protein structure and are lost in a pure, dilute, in vitro environment. Methods like in-cell NMR [1] and single molecule fluorescence-based approaches [2] report on structure within the cellular milieu, but they require protein labeling and are typically applied one protein at a time. Cryo-electron tomography also provides structural information in situ [3], but protein identification remains challenging and the approach has limited ability to probe dynamics. Two-hybrid and protein complementation approaches report on large-scale protein interactions within the cell [4], but typically probe non-native proteins in a heterologous context. Quantitative mass spectrometry (MS) is increasingly used to profile the native structural proteome, making possible, for the first time, a dynamic and global structural view of molecules within a complex cellular matrix.

Main MS-based methods can probe proteome structural alterations, folding and stability, aggregation, and molecular interaction, in situ (Figure 1). These approaches are typically integrated into a standard proteomics experiment, which in its simplest form involves trypsin digestion of a sample, analysis of the resulting peptides by MS, and identification of peptides and proteins using dedicated computational tools. Several of the pioneering approaches described here push the limits of the technology, and methods development is ongoing. Nevertheless, these approaches have already provided fundamental insights into structural and biophysical aspects of cellular proteomes.

Introduction Cells are complex systems full of vast numbers of molecules, many of which are constantly in flux. Methodological innovations are steadily improving the ability to study biology at the systems level, whether of genomes, transcriptomes or proteomes. Yet powerful though such ‘omics approaches are, they do not provide a global structural view of molecules within a cell. The structure of proteins — arguably the best-understood components of the cellular machinery — has mainly been determined on purified proteins or protein complexes in vitro. Much has been learned and continues to be learned from such studies, but they are limited in the ability to probe important properties of proteins in situ. Within the www.sciencedirect.com

Protein structural alterations

Changes in protein structure, typically associated with changes in function, occur for many reasons: ligand binding, post-translational modification, mutation, or a change in the properties of the cell matrix, to mention just a few. The two main MS approaches that probe proteome structural alterations in situ are based on limited proteolysis (LiP) and hydroxyl radical footprinting (HRF) (Figure 2, Box 1). LiP-MS exploits local or global changes in protein sensitivity to a broad-specificity protease to draw inferences about structural alterations across the proteome [5]. It is particularly informative when applied to monitor changes Current Opinion in Structural Biology 2020, 60:57–65

58 Folding and binding

Figure 1

Structural alterations

HRF-MS

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Aggregation LiP-MS TPP

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XL-MS Co-Frac-MS PDB-MS

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Protein-protein interactions Current Opinion in Structural Biology

Structural properties of native proteomes that can be studied by mass spectrometry (MS). The schematic depicts MS-based techniques that can be applied to study proteome structural alterations, folding and stability, aggregation, and molecular interactions. Abbreviations as in Box 1.

across conditions, rather than at steady state, identifying structurally altered proteins between conditions and pinpointing protein regions involved in the transition with peptide-level resolution. We have shown that LiP-MS detects relatively large changes, like fibril formation in a-synuclein, and more subtle conformational shifts such as the local unfolding of a short a-helix during heme dissociation from myoglobin, all within a complex lysate. We used the approach to monitor roughly 1000 proteins simultaneously in yeast lysates, and detected a structural change in about 300 of them under different nutrient conditions [5]. LiP-MS has also been combined with SILAC to study differences in the proteomes of two breast cell lines, one of them a model for luminal breast cancer [6]. So far, LiP-MS has only been applied in lysates, which involves a substantial dilution of cellular components and therefore may affect proteome structure [7]; further development will be needed before it can be implemented within intact cells.

accessibility of some amino acids and so yield an altered oxidation pattern. Making use of laser photolysis of hydrogen peroxide to generate the hydroxyl radicals (a variant called fast photo-oxidation of proteins or FPOP), HRF-MS has been applied to proteins in mammalian cells [9] and in Caenorhabditis elegans [10].

Hydroxyl radical footprinting (HRF)-MS follows from the fact that almost all (19 out of 20) amino acids can be oxidized by hydroxyl radicals, albeit with varying reactivity, giving a mass change that can be detected by MS [8]. A protein structural change will alter the surface

Protein folding and stability

Current Opinion in Structural Biology 2020, 60:57–65

At steady state, Jones et al. observed oxidation of many hundreds of proteins in each study, covering many tissue types in the worm and many organelles in cells, but with quite substantial technical [9] and biological variation [9,10]. Further optimization could help mitigate such variation and harness the many advantages of HRF-MS — rapid kinetics, irreversible labeling, and small size of the modifying reagent — to probe fast structural changes within the cellular milieu. More specific protein-modifying reagents, such as those targeting lysines or cysteines, could also be employed proteomewide in the future.

Most cellular functions require proteins to occupy stable or semi-stable folded states. Evolutionary pressure has produced proteins that occupy such states; in addition, folding and stability are affected by extrinsic parameters www.sciencedirect.com

Mass spectrometry of the structural proteome de Souza and Picotti 59

Figure 2

condition 1

condition 2 Proteins subject to proteolysis

condition 1

condition 2 Proteins subject to oxidation

native

native

denaturing

denaturing proteolysis

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Peptides subject to MS

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HRF-MS Current Opinion in Structural Biology

The principles of limited proteolysis-mass spectrometry (LiP-MS) and hydroxyl radical footprinting-mass spectrometry (HRF-MS) to detect protein structural change. For LiP-MS, the first proteolysis step is under native conditions. For HRF-MS, oxidation is under native conditions.

like temperature, osmolarity, or molecular interaction. Biological systems devote substantial resources to mechanisms, like molecular chaperones, to promote correct folding [11].

stability across the mammalian cell cycle [14,15], identifying in the range of 1000 proteins with changing stability, including metabolic enzymes, RNA polymerase II and several disordered proteins.

Protein stability has historically been studied on single purified proteins in vitro, in specific ionic conditions and pH, and using different techniques. As a result, there are stability data available only for a few proteins, these studies are not always comparable, and they do not take into account the influence of the cellular milieu. MS-based approaches can overcome these problems.

We have used LiP-MS for thermal profiling of roughly 6000 proteins across four species: Escherichia coli, a thermophilic bacterium, yeast and human cells [16]. Here, cell lysates are subject to limited proteolysis across a temperature gradient (Figure 3). Protease sensitivity over different temperatures yields thermal stability profiles, with increased sensitivity interpretable as protein unfolding. LiP-MS revealed that temperature-dependent lethality in bacteria is due to thermal unfolding of a limited subset of essential proteins, rather than of the whole proteome, and that intrinsically disordered proteins are likely to be more structured than expected in the cellular context. So far, thermal profiling by LiP-MS has been in cell lysates rather than in intact cells.

Most studies of proteome stability examine thermal stability. In thermal protein profiling (TPP) [12], lysates or intact cells are subject to a temperature gradient (Figure 3), and proteins that remain soluble (i.e. in the supernatant after centrifugation) are quantified by MS to yield a thermal denaturation profile. Savitski et al. have used this approach to profile the thermal stability of many thousands of bacterial and mammalian proteins at steady state [12,13]. They observe that the bacterial proteome is overall more stable than the human one and that proteins within complexes tend to ‘co-melt’. In related work, Nordlund et al. also reported that proteins in annotated complexes have similar melting curves, a concept they called thermal proximity coaggregation and used to profile protein interactions across cell lines and states [7]. TPP has recently been applied to profile proteome www.sciencedirect.com

Melting temperatures of E. coli proteins determined in parallel by TPP and LiP-MS were correlated, though not perfectly (r = 0.65, at best) [13]. TPP generally yielded higher melting temperatures than LiP-MS, probably because the methods probe unfolding in different ways. LiP-MS denaturation profiles of purified proteins closely resembled those obtained with spectroscopic techniques, but melting temperatures of endogenous proteins in lysates were different from those previously reported for purified Current Opinion in Structural Biology 2020, 60:57–65

60 Folding and binding

Box 1 Brief descriptions of methods covered. Unless otherwise specified, coverage refers to the number of detected proteins in studies using the technique.

Figure 3

Methods based on limited proteolysis (LiP) LiP-MS*: Identifies protein structural changes by quantifying altered sensitivity to a protease under different conditions. Also used to profile protein stability and to identify protein targets of small molecules. Coverage reported ranges from 2565 proteins in bacteria [49] to 4000 [16] and 5000 [63] proteins in human cells. DARTS: Drug affinity responsive target stability. Identifies drug targets by MS analysis of the protease-protected fraction in the presence of drug.

Temperature or denaturant

Pulsed proteolysis-MS: Determines protein stability by monitoring altered protease sensitivity of proteins over a gradient of chemical denaturant. Also used to identify protein targets of drugs. Methods based on stability profiling TPP: Thermal protein profiling. Determines protein thermal stability by quantifying the soluble fraction of proteins over a temperature gradient. Also used to identify protein targets of small molecules. Coverage reported ranges from 1800 proteins in bacteria [13] to 5000 [12,15] and 6000 [14] proteins in human cells. SPROX-MS: Stability of proteins from rates of oxidation-MS. Determines protein stability by quantifying methionine oxidation over a gradient of chemical denaturant. Coverage of 800 proteins has been reported [18]. Methods based on proximity labeling XL-MS: Cross-linking-MS. Determines interacting proteins by covalent crosslinking of residues in close proximity and MS identification of cross-linked peptides. Coverage reported ranges from 2426 unique crosslinks corresponding to 326 PPIs in human cells [25], to 3329 unique crosslinks corresponding to 359 proteins in mammalian mitochondria [27], and 2068 unique crosslinks corresponding to 400 proteins in bacteria [30]. Co-fractionation-MS: Determines interacting proteins based on MS identification of those that co-fractionate in one or more chromatographic techniques (e.g. size exclusion or ion exchange chromatography). Coverage reported ranges from 1176 proteins in Chaetomium thermophilum [36] to 3855 proteins in Caenorhabditis elegans [38], and upto 8000 proteins in human cells [35]. PDB-MS: Proximity-dependent biotinylation-MS. Identifies proteins in the vicinity of one or more baits based on biotinylation via an enzyme fused to the bait(s). Methods to determine RNA-protein interaction. A suite of approaches based on UV cross-linking of RNA and protein, followed by various methods to identify the cross-linked complexes: oligoDT capture, presence at the aqueous:organic interface, or capture on silica beads, all followed by MS, or computational methods applied to mass spectra. Methods based on surface modification HRF-MS*: Hydroxyl-radical footprinting-MS. Identifies protein structural changes by detecting altered hydroxyl radical oxidation of amino acids. FPOP (fast photolysis of proteins) is a version of this technique. Coverage reported ranges from 545 proteins in Caenorhabditis elegans [10] to 1391 proteins in mammalian cells [9]. *For LiP and HRF, coverage alone does not report on the ability to detect structural change. To detect such change, it is necessary to detect peptides that map to structurally altering regions of the protein.

Current Opinion in Structural Biology 2020, 60:57–65

centrifugation

TPP

protease

oxidant

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SPROX Current Opinion in Structural Biology

Protein stability profiling methods monitor protein unfolding over a gradient of temperature or denaturant. TPP and LiP-MS use a temperature gradient, PP and SPROX use a denaturant gradient. Abbreviations as in Box 1.

recombinant proteins [16], illustrating the importance of studies in native conditions. TPP melting temperatures in intact E. coli versus in lysates were well correlated (r = 0.82) [13], but this was not so in mammalian cells [12]. Another approach relying on denaturation profiles, SPROX (stability of proteins from rates of oxidation) (Figure 3) [17], has been used to monitor altered states, for instance profiling stability changes in brain tissue lysates from old and young mice [18]. In sum, the stability of thousands of proteins in cells or lysates can be probed with MS-based methods, at steady state or under changing conditions.

Protein aggregation

The ability to respond to mutational or environmental stress is a key property of biological systems. Protein that misfold or unfold under stress can aggregate and become insoluble, historically interpreted as a non-functional or toxic state, though this view is now being questioned. In early work, Marcotte et al. used centrifugation followed by MS analysis of proteins in the supernatant versus the pellet to characterize >100 proteins that reversibly enter an insoluble state in nutrient-deprived yeast [19]. More recently, centrifugation has identified proteins that rapidly become insoluble in response to heat stress in yeast [20,21]. These insoluble proteins do not entirely overlap

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Mass spectrometry of the structural proteome de Souza and Picotti 61

with stress granules and again re-solubilize once the stress is removed [21], providing further support for the notion that solubility changes may be in part an adaptive response. Interestingly, many of the same proteins become insoluble in response to heat, nutrient deprivation, and chemical (arsenic) stress in yeast [20]. TPP in mild versus harsh detergent has also been used to probe solubility [22]. While centrifugation is an appealing technique for its simplicity, aggregated proteins in the pellet cannot be distinguished from other insoluble entities such as membrane-bound proteins (in the absence of detergent) or merely unfolded insoluble proteins; this should temper interpretations about the biophysical nature of insoluble proteins in such experiments. LiP-MS can also provide insight into aggregation. In LiPMS thermal denaturation profiles, several proteins enter — after unfolding — a relatively protease-insensitive regime at higher temperatures, consistent with unfolding-induced aggregation [16]. This group of techniques will help elucidate which proteins aggregate or become insoluble in response to disease or stress and how this response may benefit or harm the cell. Protein–protein interactomics

The constituents of native protein complexes are proving accessible to analysis by MS-coupled to cross-linking, proximity-labeling or co-fractionation approaches. We do not cover here the large literature on affinity purification-MS since it typically probes a single bait protein at a time. We also do not cover native MS, which has recently been used to directly monitor protein complexes extracted without detergent from bacterial membranes [23], clarifying stoichiometries of outer, inner and mitochondrial membrane complexes. In cross-linking-mass spectrometry (XL-MS), covalent cross-linking of proteins in a sample is followed by the MS identification of cross-linked peptides. When performed in native conditions, this gives a global view of proteins in close proximity and which are thus likely to interact as part of protein complexes. XL-MS has been widely applied to purified proteins or to simple reconstituted systems in vitro, to provide distance restraints (a Ca-Ca distance range of 7–35 A˚ depending on the crosslinker) for high-resolution structure determination. Extending this approach to complex cellular systems faces formidable challenges. Chief among them are the detection of cross-linked peptides, which are in very low abundance compared to non-cross-linked ones, and the correct identification of cross-linked peptides, and therefore of proteins, from the mass spectra. Since early studies in human cell lysates showed proteome-wide XL-MS to be feasible [24,25], improvements are continuously being made to cross-linking reagents, www.sciencedirect.com

fragmentation schemes, instrumentation, and database searching. For instance, a cleavable crosslinker much improves the identification of cross-linked peptides. Such a reagent was used on E. coli and Hela cell lysates [26], and on intact mammalian mitochondria and nuclei [27,28], identifying many thousands of crosslinks between many hundreds of proteins. Among other findings, these studies probed the quaternary structure of bacterial protein complexes involved in co-translational protein folding and carbohydrate catabolism [26], and uncovered evidence for supercomplexes in mammalian mitochondrial membranes [27]. Crosslinking in intact mammalian [29] and bacterial cells [30] has been demonstrated, identifying interactors of proteins involved in antibiotic resistance in a nosocomial pathogen [30]. Nevertheless, XL-MS in a complex sample such as a cell lysate still yields on average relatively few crosslinks per interacting protein pair, making it difficult to map interaction interfaces. XL-MS also lacks the sensitivity for comprehensive interactome mapping since it typically detects cross-links of only the more abundant proteins in the sample. Advances in affinity-purifiable crosslinkers [31] could help with these problems and move XL-MS towards both more comprehensive and structurally informative native interactome mapping. Co-fractionation-MS identifies protein complexes based on chromatographic or electrophoretic co-fractionation of proteins [32,33]. In early studies, size exclusion chromatography (SEC)-MS identified hundreds of protein complexes involving thousands of human proteins in mammalian cell lines [34,35]. These studies recovered both known and novel protein complexes, monitored interactome dynamics [34] and probed variations in protein complexes due to protein isoforms [35]. More recently, SEC-MS has been used as part of a structural pipeline to identify higher-order sets of protein complexes within a thermophilic bacterium [36], hinting at cellular organization, which the authors term protein communities, at a scale intermediate between macromolecular complexes and organelles. A challenge with co-fractionation approaches is to distinguish true complexes from proteins that merely have similar fractionation properties. Most analyses use some degree of prior knowledge to identify true complexes. Machine learning-based approaches can predict protein-protein interactions and protein complexes from co-fractionation data [37,38], identifying tens of thousands of PPIs within about 600 complexes in C. elegans [38]. Target-decoy-based analysis quantified false discovery rate of complexes from SEC-MS, showing that about 55% of the observed protein mass in HEK293 T cells is in complexes rather than in a monomeric state [39]. Attempts have been made to reduce dependence on prior knowledge in identifying true complexes, by requiring co-elution based on both size and charge [40] or using a statistical approach [41]. Current Opinion in Structural Biology 2020, 60:57–65

62 Folding and binding

In proximity-dependent biotinylation (PDB)-MS, a biotin ligase or a peroxidase capable of biotinylating nearby proteins is targeted to a subcellular location or fused to a target protein [42]. This yields biotinylated proteins in the immediate vicinity (estimated at 10–20 nm) [42] of the enzyme, which can then be affinity-purified and identified with MS. While most studies fuse the enzyme to a single bait protein, and are not covered here, systematic studies integrating biotinylation patterns from 10 s or 100 s of baits have mapped protein interaction networks in primary cilia [43] or in RNA-containing granules [44]. The chief difficulty with this approach is in distinguishing between true interactors and bystander proteins and care must be taken to ensure that the resulting lists are not badly contaminated by the latter [42]. Interaction of proteins with other molecules

Molecular interactions important for cellular function are not limited to those between proteins; we focus here on interactions of proteins with two other classes: with small molecules or with RNA, on which most recent proteomewide work has been done. Small molecule metabolites interact with proteins in many different contexts: they are substrates and products of enzymatic reactions, function as second messengers or allosteric regulators, and constitute the most common category of drug. Small molecule binding to proteins typically changes protein stability, which can be exploited to identify binding targets in the cell. TPP has been used to identify drug targets that are either cytosolic [12] or membrane proteins [45], as well as to identify targets of endogenous metabolites [46], although notably this approach will in some contexts identify downstream effectors of drugs as well. Recently, thermal stability profiling was used to describe many hundreds of mammalian proteins that change stability in an ATP-dependent or GTP-dependent way [22] and identified Plasmodium falciparum purine nucleoside phosphorylase as the likely target of the antimalarial compound quinine [47]. SPROX-MS can also be used to screen for candidate drug targets by detecting protein stability alterations in a gradient of chemical denaturant [48]. A second set of approaches to monitor protein-small molecule binding is based on limited proteolysis, where structural changes due to binding are detected via altered protease sensitivity [49,50,51]. We have used LiP-MS to generate a map of about 1600 protein–metabolite interactions in E. coli lysates, of which about 1400 had not been previously reported [49]. The use of LiP-MS in lysates rather than inside cells can reduce false positives, since downstream effects of the small molecule are less likely to confound direct target identification. LiP-MS also has the substantial advantage that it can pinpoint small molecule binding sites on the target protein [49]. Knowledge of the binding site is powerful to study drug structure–activity Current Opinion in Structural Biology 2020, 60:57–65

relationships and, if the target protein structure is known, enables generation of hypotheses about the nature of the interaction (e.g. whether catalytic or allosteric, in the case of an enzyme). Another approach based on limited proteolysis, DARTS (drug affinity responsive target stability), includes an electrophoresis step to define protease-protected proteins and then identifies putative targets by MS of gel-extracted samples [51], although the approach has also recently been applied in a more unbiased fashion [52]. Proteins also interact widely with RNA in the cell, with implications that go beyond the regulation of RNA biology or of gene expression [53]. Profiling these interactions proteome-wide generally begins with UV cross-linking of nearby RNA and protein molecules within cells. While earlier approaches then captured polyA RNA and identified cross-linked proteins with MS [54,55], more recently, methods that are not limited to polyA RNA have been developed; these capture interesting categories such as non-coding RNAs, and can be used in species like bacteria that lack poly-adenylation. Several groups have adapted classical phenol-chloroformbased phase separation of RNA and protein for this purpose. Crosslinked RNA-protein complexes are present at the aqueous:organic interface, from where they are collected and profiled by MS [56–58], yielding RNA-protein interactomes for bacteria, yeast and mammalian cells. In related approaches, cross-linked complexes are captured via RNA-binding to silica beads [59] or identified via depletion of cross-linked peptides in the mass spectra [60]. Notably, several of these methods can also identify the RNA-binding site on proteins. Finally, a biotinylated isoxazole reversibly precipitates cellular proteins that overlap with those in RNA granules, which are thought to form via phase separation in several systems [61]. Although not proteome-wide, this should lead to further insight into the formation and function of these intriguing biophysically driven structures.

Conclusions Structural changes can be caused by numerous molecular events and several of the methods we discuss here (LiP-MS, TPP, HRF-MS, SPROX) can, at least in principle, detect such changes irrespective of the causative event. For example, LiP-MS detects structural changes due to small molecule binding, unfolding, aggregation, or allostery. This is both a strength and a challenge of these methods; it means that they are powerful and broadly applicable, but also that they typically require orthogonal information to interpret the origin of a particular signal. Proximity-based approaches (XL-MS, PDB-MS, CF-MS), by contrast, more specifically report on molecules that are near each other in the cell. It is also worth noting that these approaches share limitations that are common to the MS readout. First, they share a bias towards the more abundant proteins in a sample and www.sciencedirect.com

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cannot be expected to yield a comprehensive picture. Second, when multiple protein states co-exist, as is quite possible within biological systems, these methods will report on the average of these states.

8.

Maleknia SD, Brenowitz M, Chance MR: Millisecond radiolytic modification of peptides by synchrotron X-rays identified by mass spectrometry. Anal Chem 1999, 71:3965-3973.

9.

Rinas A, Mali VS, Espino JA, Jones LM: Development of a microflow system for in-cell footprinting coupled with mass spectrometry. Anal Chem 2016, 88:10052-10058.

These constraints notwithstanding, the growing ability of MS-based methods to probe dynamic structural and biophysical properties of large numbers of proteins within the complex cellular milieu is a major step towards a systematic structural view of the cell. These approaches are poised to make important contributions to the emerging field of structural systems biology, which integrates experimental or predicted protein structure into systems-level studies [62]. Since protein structure is intimately linked to function, global structural readouts, properly interpreted, should in the future also serve as proxies for systematic studies of protein function in situ.

10. Espino JA, Jones LM: Illuminating biological interactions with in vivo protein footprinting. Anal Chem 2019, 91:6577-6584.

Conflict of interest statement P.P. is a scientific advisor for the company Biognosys AG (Zurich, Switzerland) and an inventor of a patent licensed by Biognosys AG.

Acknowledgements We thank Alexander Leitner, ETH Zu¨rich, for helpful discussions. PP is funded by a Personalized Health and Related Technologies (PHRT) grant (PHRT-506) and a Sinergia grant from the Swiss National Science Foundation (SNSF grant CRSII5_177195).

References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:  of special interest  of outstanding interest 1.

Sakakibara D, Sasaki A, Ikeya T, Hamatsu J, Hanashima T, Mishima M, Yoshimasu M, Hayashi N, Mikawa T, Wa¨lchli M et al.: Protein structure determination in living cells by in-cell NMR spectroscopy. Nature 2009, 458:102-105.

2.

Okamoto K, Hiroshima M, Sako Y: Single-molecule fluorescence-based analysis of protein conformation, interaction, and oligomerization in cellular systems. Biophys Rev 2018, 10:317-326.

3.

Oikonomou CM, Jensen GJ: Cellular electron cryotomography: toward structural biology in situ. Annu Rev Biochem 2017, 86:873-896.

4.

Buntru A, Trepte P, Klockmeier K, Schnoegl S, Wanker EE: Current approaches toward quantitative mapping of the interactome. Front Genet 2016, 7:74.

5.

Feng Y, De Franceschi G, Kahraman A, Soste M, Melnik A, Boersema PJ, de Laureto PP, Nikolaev Y, Oliveira AP, Picotti P: Global analysis of protein structural changes in complex proteomes. Nat Biotechnol 2014, 32:1036-1044.

6.

Liu F, Fitzgerald MC: Large-scale analysis of breast cancerrelated conformational changes in proteins using limited proteolysis. J Proteome Res 2016, 15:4666-4674.

7. 

Tan CSH, Go KD, Bisteau X, Dai L, Yong CH, Prabhu N, Ozturk MB, Lim YT, Sreekumar L, Lengqvist J et al.: Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 2018, 359:1170-1177 The authors establish that members of protein complexes show correlated thermal denaturation profiles, and study protein complexes in different mammalian cells and cell states.

www.sciencedirect.com

11. Jayaraj GG, Hipp MS, Hartl FU: Functional modules of the proteostasis network. Cold Spring Harb Perspect Biol 2019 http://dx.doi.org/10.1101/cshperspect.a033951. 12. 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. 13. Mateus A, Bobonis J, Kurzawa N, Stein F, Helm D, Hevler J, Typas A, Savitski MM: Thermal proteome profiling in bacteria: probing protein state in vivo. Mol Syst Biol 2018, 14:e8242. 14. Dai L, Zhao T, Bisteau X, Sun W, Prabhu N, Lim YT, Sobota RM, Kaldis P, Nordlund P: Modulation of protein-interaction states through the cell cycle. Cell 2018, 173:1481-1494.e13. 15. Becher I, Andres-Pons A, Romanov N, Stein F, Schramm M,  Baudin F, Helm D, Kurzawa N, Mateus A, Mackmull MT et al.: Pervasive protein thermal stability variation during the cell cycle. Cell 2018, 173:1495-1507.e18 Recent demonstration of the use of thermal proteome profiling to probe cell biology. The authors report proteome-wide stability changes over the mammalian cell cycle. 16. Leuenberger P, Ganscha S, Kahraman A, Cappelletti V,  Boersema PJ, von Mering C, Claassen M, Picotti P: Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science 2017, 355 First demonstration of LiP-MS for proteome thermal stability profiling. Identifies key determinants of this fundamental property across species. 17. West GM, Tang L, Fitzgerald MC: Thermodynamic analysis of protein stability and ligand binding using a chemical modification- and mass spectrometry-based strategy. Anal Chem 2008, 80:4175-4185. 18. Roberts JH, Liu F, Karnuta JM, Fitzgerald MC: Discovery of agerelated protein folding stability differences in the mouse brain proteome. J Proteome Res 2016, 15:4731-4741. 19. Narayanaswamy R, Levy M, Tsechansky M, Stovall GM, O’Connell JD,  Mirrielees J, Ellington AD, Marcotte EM: Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation. Proc Natl Acad Sci U S A 2009, 106:10147-10152 Early demonstration that mass spectrometry coupled to centrifugation can detect protein solubility changes under stress, and that these changes are reversible. 20. O’Connell JD, Tsechansky M, Royal A, Boutz DR, Ellington AD, Marcotte EM: A proteomic survey of widespread protein aggregation in yeast. Mol Biosyst 2014, 10:851-861. 21. Wallace EW, Kear-Scott JL, Pilipenko EV, Schwartz MH, Laskowski PR, Rojek AE, Katanski CD, Riback JA, Dion MF, Franks AM et al.: Reversible, specific, active aggregates of endogenous proteins assemble upon heat stress. Cell 2015, 162:1286-1298. 22. 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 The authors identify proteins in mammalian cells where stability and solubility are ATP-dependent or GTP-dependent, thus probing the role of these metabolites proteome-wide. 23. Chorev DS, Baker LA, Wu D, Beilsten-Edmands V, Rouse SL, Zeev-Ben-Mordehai T, Jiko C, Samsudin F, Gerle C, Khalid S et al.: Protein assemblies ejected directly from native membranes yield complexes for mass spectrometry. Science 2018, 362:829-834. 24. Chavez JD, Weisbrod CR, Zheng C, Eng JK, Bruce JE: Protein interactions, post-translational modifications and topologies in human cells. Mol Cell Proteomics 2013, 12:1451-1467. Current Opinion in Structural Biology 2020, 60:57–65

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25. Liu F, Rijkers DT, Post H, Heck AJ: Proteome-wide profiling of protein assemblies by cross-linking mass spectrometry. Nat Methods 2015, 12:1179-1184. 26. Liu F, Lossl P, Scheltema R, Viner R, Heck AJR: Optimized fragmentation schemes and data analysis strategies for proteome-wide cross-link identification. Nat Commun 2017, 8:15473. 27. Liu F, Lossl P, Rabbitts BM, Balaban RS, Heck AJR: The interactome of intact mitochondria by cross-linking mass  spectrometry provides evidence for coexisting respiratory supercomplexes. Mol Cell Proteomics 2018, 17:216-232 Advances in cross-linking mass spectrometry were applied to probe the protein–protein interactome of an intact organelle, mammalian mitochondria. 28. Fasci D, van Ingen H, Scheltema RA, Heck AJR: Histone interaction landscapes visualized by crosslinking mass spectrometry in intact cell nuclei. Mol Cell Proteomics 2018, 17:2018-2033. 29. Kaake RM, Wang X, Burke A, Yu C, Kandur W, Yang Y, Novtisky EJ, Second T, Duan J, Kao A et al.: A new in vivo crosslinking mass spectrometry platform to define protein-protein interactions in living cells. Mol Cell Proteomics 2014, 13:35333543. 30. Wu X, Chavez JD, Schweppe DK, Zheng C, Weisbrod CR, Eng JK, Murali A, Lee SA, Ramage E, Gallagher LA et al.: In vivo protein  interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075. Nat Commun 2016, 7:13414 Cross-linking mass spectrometry used to study the important problem of antibiotic resistance. 31. Steigenberger B, Pieters RJ, Heck AJR, Scheltema RA: PhoX: an IMAC-enrichable cross-linking reagent. ACS Cent Sci 2019, 5:1514-1522 http://dx.doi.org/10.1021/acscentsci.9b00416. 32. Havugimana PC, Hart GT, Nepusz T, Yang H, Turinsky AL, Li Z, Wang PI, Boutz DR, Fong V, Phanse S et al.: A census of human soluble protein complexes. Cell 2012, 150:1068-1081. 33. Camacho-Carvajal MM, Wollscheid B, Aebersold R, Steimle V, Schamel WW: Two-dimensional Blue native/SDS gel electrophoresis of multi-protein complexes from whole cellular lysates: a proteomics approach. Mol Cell Proteomics 2004, 3:176-182. 34. Kristensen AR, Gsponer J, Foster LJ: A high-throughput approach for measuring temporal changes in the interactome. Nat Methods 2012, 9:907-909. 35. Kirkwood KJ, Ahmad Y, Larance M, Lamond AI: Characterization of native protein complexes and protein isoform variation using size-fractionation-based quantitative proteomics. Mol Cell Proteomics 2013, 12:3851-3873. 36. Kastritis PL, O’Reilly FJ, Bock T, Li Y, Rogon MZ, Buczak K,  Romanov N, Betts MJ, Bui KH, Hagen WJ et al.: Capturing protein communities by structural proteomics in a thermophilic eukaryote. Mol Syst Biol 2017, 13:936 Demonstration of the integration of co-fractionation MS into a larger structural pipeline to identify a potentially new layer of protein organization in the cell. 37. Stacey RG, Skinnider MA, Scott NE, Foster LJ: A rapid and accurate approach for prediction of interactomes from coelution data (PrInCE). BMC Bioinformatics 2017, 18:457. 38. Hu LZ, Goebels F, Tan JH, Wolf E, Kuzmanov U, Wan C, Phanse S,  Xu C, Schertzberg M, Fraser AG et al.: EPIC: software toolkit for elution profile-based inference of protein complexes. Nat Methods 2019, 16:737-742 A machine learning-based approach to infer protein complexes from cofractionation-MS data, applied to discover protein complexes in the worm. 39. Heusel M, Bludau I, Rosenberger G, Hafen R, Frank M, Banaei Esfahani A, van Drogen A, Collins BC, Gstaiger M, Aebersold R: Complex-centric proteome profiling by SEC-SWATH-MS. Mol Syst Biol 2019, 15:e8438 A statistical approach to aid identification of protein complexes from cofractionation-MS data. The authors discover that more than half of the proteins in a mammalian cell line are in the form of a complex. Current Opinion in Structural Biology 2020, 60:57–65

40. McBride Z, Chen D, Lee Y, Aryal UK, Xie J, Szymanski DB: A labelfree mass spectrometry method to predict endogenous protein complex composition. Mol Cell Proteomics 2019, 18:1588-1606. 41. Drew K, Muller CL, Bonneau R, Marcotte EM: Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLoS Comput Biol 2017, 13:e1005625. 42. Gingras AC, Abe KT, Raught B: Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles. Curr Opin Chem Biol 2019, 48:44-54. 43. Gupta GD, Coyaud E, Goncalves J, Mojarad BA, Liu Y, Wu Q, Gheiratmand L, Comartin D, Tkach JM, Cheung SW et al.: A dynamic protein interaction landscape of the human centrosome-cilium interface. Cell 2015, 163:1484-1499. 44. Youn JY, Dunham WH, Hong SJ, Knight JDR, Bashkurov M,  Chen GI, Bagci H, Rathod B, MacLeod G, Eng SWM et al.: Highdensity proximity mapping reveals the subcellular organization of mRNA-associated granules and bodies. Mol Cell 2018, 69:517-532.e11 Recent use of proximity-dependent biotinylation to map proteins interacting with RNA granules, identifying many hundreds of new potential players in RNA biology. 45. Reinhard FB, Eberhard D, Werner T, Franken H, Childs D, Doce C, Savitski MF, Huber W, Bantscheff M, Savitski MM et al.: Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat Methods 2015, 12:1129-1131. 46. 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. 47. Dziekan JM, Yu H, Chen D, Dai L, Wirjanata G, Larsson A,  Prabhu N, Sobota RM, Bozdech Z, Nordlund P: Identifying purine nucleoside phosphorylase as the target of quinine using cellular thermal shift assay. Sci Transl Med 2019, 11 A recent application of thermal stability profiling to identify the target of an antimalarial compound. 48. West GM, Tucker CL, Xu T, Park SK, Han X, Yates JR 3rd, Fitzgerald MC: Quantitative proteomics approach for identifying protein-drug interactions in complex mixtures using protein stability measurements. Proc Natl Acad Sci U S A 2010, 107:9078-9082. 49. 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. e23 LiP-MS is used to generate a systematic protein-small molecule interactome in E. coli. The authors show that the approach pinpoints metabolite-binding sites and uncover many new interactions. 50. Adhikari J, Fitzgerald MC: SILAC-pulse proteolysis: a mass spectrometry-based method for discovery and crossvalidation in proteome-wide studies of ligand binding. J Am Soc Mass Spectrom 2014, 25:2073-2083. 51. Lomenick B, Hao R, Jonai N, Chin RM, Aghajan M, Warburton S, Wang J, Wu RP, Gomez F, Loo JA et al.: Target identification using drug affinity responsive target stability (DARTS). Proc Natl Acad Sci U S A 2009, 106:21984-21989. 52. Kim D, Hwang H-Y, Kim JY, Lee JY, Yoo JS, Marko-Varga G, Kwon HJ: FK506, an immunosuppressive drug, induces autophagy by binding to the V-ATPase catalytic subunit a in neuronal cells. J Proteome Res 2017, 16:55-64. 53. Hentze MW, Castello A, Schwarzl T, Preiss T: A brave new world of RNA-binding proteins. Nat Rev Mol Cell Biol 2018, 19:327-341. 54. Baltz AG, Munschauer M, Schwanhausser B, Vasile A, Murakawa Y, Schueler M, Youngs N, Penfold-Brown D, Drew K, Milek M et al.: The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts. Mol Cell 2012, 46:674-690. 55. Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, Davey NE, Humphreys DT, Preiss T, Steinmetz LM et al.: www.sciencedirect.com

Mass spectrometry of the structural proteome de Souza and Picotti 65

Insights into RNA biology from an atlas of mammalian mRNAbinding proteins. Cell 2012, 149:1393-1406. 56. Queiroz RML, Smith T, Villanueva E, Marti-Solano M, Monti M, Pizzinga M, Mirea DM, Ramakrishna M, Harvey RF, Dezi V et al.: Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat Biotechnol 2019, 37:169-178. 57. Trendel J, Schwarzl T, Horos R, Prakash A, Bateman A, Hentze MW, Krijgsveld J: The human RNA-binding proteome and its dynamics during translational arrest. Cell 2019, 176:391-403.e19. 58. Urdaneta EC, Vieira-Vieira CH, Hick T, Wessels HH, Figini D, Moschall R, Medenbach J, Ohler U, Granneman S, Selbach M et al.: Purification of cross-linked RNA-protein complexes by phenol-toluol extraction. Nat Commun 2019, 10:990. 59. Shchepachev V, Bresson S, Spanos C, Petfalski E, Fischer L, Rappsilber J, Tollervey D: Defining the RNA interactome by total

www.sciencedirect.com

RNA-associated protein purification. Mol Syst Biol 2019, 15: e8689. 60. He C, Sidoli S, Warneford-Thomson R, Tatomer DC, Wilusz JE, Garcia BA, Bonasio R: High-resolution mapping of RNA-binding regions in the nuclear proteome of embryonic stem cells. Mol Cell 2016, 64:416-430. 61. Kato M, Han TW, Xie S, Shi K, Du X, Wu LC, Mirzaei H, Goldsmith EJ, Longgood J, Pei J et al.: Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 2012, 149:753-767. 62. Aloy P, Russell RB: Structural systems biology: modelling protein interactions. Nat Rev Mol Cell Biol 2006, 7:188-197. 63. Piazza I, Beaton N, Bruderer R, Knobloch T, Barbisan C, Siepe I, Rinner O, de Souza N, Picotti P, Reiter L: LiP; -Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes. bioRxiv 2019 http://dx.doi.org/10.1101/ 860072.

Current Opinion in Structural Biology 2020, 60:57–65