CONFORMATIONAL EFFECTS IN PROTEIN ELECTROSPRAYIONIZATION MASS SPECTROMETRY Jinyu Li,1,2 Carlo Santambrogio,3 Stefania Brocca,3 Giulia Rossetti,1,4 Paolo Carloni,1** and Rita Grandori3* 1

Computational Biophysics, German Research School for Simulation Sciences, and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum J€ulich, 52425 J€ulich, Germany 2 Institute of Biochemistry and Molecular Biology, RWTH Aachen University, 52057 Aachen, Germany 3 Department of Biotechnology and Biosciences, University of MilanoBicocca, Piazza della Scienza 2, 20126 Milan, Italy 4 J€ulich Supercomputing Centre, Forschungszentrum J€ulich, 52425 J€ulich, Germany Received 17 September 2014; accepted 14 January 2015 Published online 7 May 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/mas.21465

Electrospray-ionization mass spectrometry (ESI-MS) is a key tool of structural biology, complementing the information delivered by conventional biochemical and biophysical methods. Yet, the mechanism behind the conformational effects in protein ESI-MS is an object of debate. Two parameters— solvent-accessible surface area (As) and apparent gas-phase basicity (GBapp)—are thought to play a role in controlling the extent of protein ionization during ESI-MS experiments. This review focuses on recent experimental and theoretical investigations concerning the influence of these parameters on ESIMS results and the structural information that can be derived. The available evidence supports a unified model for the ionization mechanism of folded and unfolded proteins. These data indicate that charge-state distribution (CSD) analysis can provide valuable structural information on normally folded, as well as disordered structures. # 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:111–122, 2016. Keywords: charge-state distributions; proton transfer reactions; solvent accessible surface area; molecular-dynamics simulations; gas-phase basicity

I. INTRODUCTION Detecting non-covalent interactions by electrospray-ionization mass spectrometry (ESI-MS) has paved the way to exciting applications in structural biology (Marcoux & Robinson, 2013; Schmidt & Robinson, 2014; Uetrecht & Heck, 2011) for the last couple of decades (Chowdhury, Katta, & Chait, 1990; Katta &




Correspondence to: Rita Grandori, Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy. E-mail: [email protected]

Correspondence to: Paolo Carloni, Computational Biophysics, German Research School for Simulation Sciences, and Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum J€ulich, 52425 J€ ulich, Germany. E-mail: [email protected]

Mass Spectrometry Reviews, 2016, 35, 111–122 # 2015 by Wiley Periodicals, Inc.

Chait, 1991). The widespread application of this method can be attributed to the peculiar advantage of combining the analytical power of ESI-MS with structural characterization. Nanoelectrospray sample sources are particularly well suited to this purpose, being compatible with very moderate voltage and temperature conditions, thanks to the sub-micrometer size of the first-generation droplets (Schmidt, Karas, & D€ ulcks, 2003). Coupling ion-mobility with native MS offers further enhancement in the separation and structural characterization (Hall, Politis, & Robinson, 2012; Jurneczko et al., 2012; Konijnenberg, Butterer, & Sobott, 2013; Lanucara et al., 2014; Williams & Pukala, 2013; Woods, Radford, & Ashcroft, 2013; Zhong, Hyung, & Ruotolo, 2012).1 In turn, the structural characterization greatly benefits from computational analysis (Adams et al., 2006; Arcella et al., 2012; Friemann et al., 2009; Iavarone et al., 2007; Le et al., 2013; Li et al., 2014; Marchese et al., 2012; Marklund et al., 2009; Meyer, de la Cruz, & Orozco, 2009; Meyer et al., 2012; Patriksson, et al., 2007; Patriksson, Marklund, & van der Spoel, 2007; Rueda et al., 2003; van der Spoel et al., 2011; Wang, Larsson, & van der Spoel, 2009). Although the final conditions in which proteins are detected by ESI-MS (the gas phase) are quite different from their original environment (usually aqueous solutions), non-covalent interactions can be preserved under appropriate experimental conditions (Kebarle & Verkerk, 2009; Verkerk & Kebarle, 2005). Hence, this kind of analysis is referred to as native MS (van Duijn et al., 2005) and its development has potential impact on many fields of basic and applied research (Bobst & Kaltashov, 2011; Grandori et al., 2009; Kaltashov, Bobst, & Abzalimov, 2013; Kaltashov et al., 2012). In spite of the wide application of ESI-MS for structural biology, several aspects of the underlying mechanisms are still poorly understood. One of the main challenges is to dissect and quantitatively assess the contributions of the different factors affecting protein ionization. These include solvent properties, 1

Although non-covalent interactions can also be detected by matrixassisted laser desorption/ionization (MALDI)-MS (Bolbach, 2005), ESI remains by far the most effective and used ionization method for this purpose.

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experimental conditions, instrumental setup, and, most importantly, the protein conformational state (Grandori, 2003a; Kaltashov & Abzalimov, 2008; Kaltashov et al., 2013; Kebarle & Verkerk, 2009; Samalikova et al., 2004; Verkerk & Kebarle, 2005). Indeed, the final charge states observed by ESI are strongly affected by the conformation that the protein holds at the moment of transfer into the gas phase. Generally, folded protein molecules acquire fewer charges than unfolded ones. Coexistence of two or more conformational states gives rise to bimodal or multimodal charge-state distributions (CSDs) (Fig. 1). In particular, conformational heterogeneity seems to be the main factor leading to multiple peak envelopes, while other parameters (i.e., solvent properties, experimental conditions, and instrumental setup) tend to shift CSDs gradually (Gumerov, Dobo, & Kaltashov, 2002; Hewavitharana et al., 2010; Ogorzalek Loo, Lakshmanan, & Loo, 2014; Samalikova et al., 2004). Conformational effects are, thus, at the basis of the most dramatic changes in CSDs and are quite universal. There is only one reported case, Vibrio harveyi acyl carrier protein, showing no spectral changes under conditions known to modulate protein conformational properties in solution (Murphy, Rowland, & Byers, 2007). Further experiments will be needed to explain the observed behavior. Of particular importance

would be to test whether those results are confirmed on a nanoESI-MS equipment. However, this example might be the exception that proves the rule, being likely due to loss of protein structure during electrospray. It should also be reminded that CSDs are sensitive to changes in protein tertiary rather than secondary structure, as long as these two levels can be uncoupled (Grandori, 2003b). Two structural features seem to play a critical role in protein ionization by electrospray: the solvent accessible surface area (As) and the apparent gas-phase basicity (GBapp). Both have been related to the protein charge states observed under ESI conditions (Hall & Robinson, 2012; Hautreux et al., 2004; Kaltashov & Mohimen, 2005; Li et al., 2014; Marchese et al., 2012; Schnier, Gross, & Williams, 1995b; Testa, Brocca, & Grandori, 2011). Here, we summarize recent experimental and computational evidence regarding the influence of these two parameters on the ESI behavior of folded and unfolded proteins.

A. As of Folded Proteins Protein size has long been recognized as one factor affecting protein ionization by electrospray. The average charge state

FIGURE 1. CSDs of globular, folded proteins (A, B) and IDPs (C, D). Nano-ESI-MS spectra in 10 mM ammonium acetate pH 7 of (A) chicken lysozyme, (B) maltose-binding protein, (C) human alpha-synuclein, and (D) fragment 1-291 of human ataxin-3. The most intense peak of each component is labeled by the corresponding charge state.

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(Zav) acquired by a protein during the electrospray process is defined as: X nI n n X Z av ¼ ð1Þ In n

where In is the intensity of charge state n. Globular proteins display a power–law dependence of Zav on protein mass (de la Mora, 2000; Heck & Van Den Heuvel, 2004; Nesatyy & Suter, 2004; Testa, Brocca, & Grandori, 2011). Reflecting conformational effects, unfolded proteins follow a distinct charge-to-mass relation (Testa, Brocca, & Grandori, 2011). If shape is constrained, like in the case of folded globular proteins, a relation to mass implies a relation to surface. However, if one considers structures with different shapes and architectures, no unique mass-to-surface relation will be given. Several studies have investigated the relation between average charge and As (Hall & Robinson, 2012; Hautreux et al., 2004; Kaltashov & Mohimen, 2005; Testa, Brocca, & Grandori, 2011). These calculations employ the As values computed on crystallographic or NMR structures. As is defined as the area swapped by the center of a sphere, representing a molecule of solvent, rolling on the surface of the protein structure. Calculations are generally performed with a probe radius of 1.4 Å (which approximates the radius of a water molecule) and accounting for protein hydrogen atoms. This relation, too, can be fitted by a power–law function, according to the general expression Z av ¼ K  ðAs Þa

ð2Þ

with K and a as constants. Equation (2) can be transformed to the double-log equation lnðZ av Þ ¼ a  lnðAs Þ þ lnK

ð3Þ

The values for the slope a in Equation 3 reported in the literature vary between 0.59 (Hall & Robinson, 2012) and 0.7 (Testa, Brocca, & Grandori, 2011). The relation has been shown to hold for monomeric structures, as well as for supramolecular assemblies, and has been documented so far in the range of molecular weight from 5 to 800 kDa (Hall & Robinson, 2012). Most importantly, the relation seems to hold also for elongated architectures and hollow structures (Kaltashov & Mohimen, 2005), which deviate from the mass-to-surface relation of globular proteins. Although fewer data are available in the negative-ion mode, a linear relation also seems to hold for inverted instrument polarity (Testa, Brocca, & Grandori, 2011). Thus, As seems to be the most prominent feature of protein structures correlating with the extent of ionization under non-denaturing electrospray conditions, regardless of size and shape. Several groups have compared the ionization behavior of proteins in positive- and negative-ion mode. Examples have been reported for proteins that, irrespective of the number of acidic and basic residues, present similar CSDs in positive and negative-ion mode, under the same experimental conditions

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(Kaltashov & Abzalimov, 2008; Watt et al., 2007). However, the ionization process is in general less effective in negative-ion rather than positive-ion mode, giving rise to CSDs centered on lower charge states (Heck & van den Heuvel 2004; Pinkse et al., 2003). This phenomenon can be ascribed to the different nature of the involved solvent components and protein functional groups in the two different instrumental settings (Verkerk, 2014b). As an example, in ammonium acetate buffer, the intrinsic basicity of protein and buffer is set more apart than their intrinsic acidity (Blades et al., 2002). Differences in the volatility of buffer components could also contribute to the observed effect (de la Mora et al., 2000).

B. As of Unfolded Proteins It would be interesting to compare the charge-to-surface relation of folded and unfolded proteins. To perform such a comparison, computational strategies need to be developed in order to overcome the lack of atomic structural models for proteins in the denatured state. An approach is to model the conformational ensemble of proteins in the unfolded state based on the amino acid sequence and derive average As values for the sampled structures. Such a procedure is implemented in the algorithm and server ProtSA (Estrada et al., 2009) and has been used to estimate As of unfolded proteins, for which ESI-MS data under denaturing conditions are available (Testa, Brocca, & Grandori, 2011). This study has shown that the charge-to-surface relation of folded and unfolded proteins is quite similar (Fig. 2). The equations calculated for folded and unfolded proteins are, respectively: lnðZ av Þ ¼ 0:604  lnðAs Þ  3:285

ð4Þ

lnðZ av Þ ¼ 0:913  lnðAs Þ  6:013

ð5Þ

The high-charge component from spectra of intrinsically disordered proteins (IDPs) acquired under non-denaturing conditions has been used as a control, to rule out solvent effects (Testa, Brocca, & Grandori, 2011). Thus, the extent of ionization of folded and unfolded proteins is very different if related to mass, but becomes quite similar if related to As. However, a certain discrepancy between the best linear fits can be observed in Figure 2, as also previously reported (Testa, Brocca, & Grandori, 2011). Several factors could contribute to such a divergence. It is possible that the computed As values for unfolded proteins in solution are systematically smaller than the real values under electrospray conditions. Alternatively, the higher flexibility of denatured proteins, as well as different preferential migration to the liquid-air interface (Verkerk, 2014a,b), could lead to larger conformational changes between bulk solution and electrospray droplets, compared to folded structures. Weaker kinetic trapping (Hamdy & Julian, 2012) of structures populating the denatured state could also act in the same direction. Another factor that could play a role is that the chemical properties of the protein surface are different for folded and unfolded structures. As discussed in the following sections, the intramolecular interactions specific of the folded state affect ionization reactions. These differences could lead, in average, to a higher net charge per surface unit for unfolded proteins (Marchese et al., 2012). The divergence between the

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FIGURE 2. Double-log plot for the charge-to-surface relation. The logarithm of the experimental Zav in positive-ion mode is plotted against the logarithm of the calculated As values obtained by ProtSA, for globular (black circles) and unfolded (gray circles) proteins. The fitting results are shown as a dashed or dotted line, respectively. Equations and R-squared values for the linear regressions are indicated on the figure. The figure was adapted with permission from Testa, Brocca, and Grandori, 2011 and includes the following additional points. From this work: strathmin RB3; Whi5; ataxin 3 fragment (1-291); ovalbumin; maltose-binding protein; bovine serum albumin; acylaminoacyl peptidase; LptA monomer, dimer, trimer and tetramer; major histocompatibility complex monomer and dimer; tubulin dimer and tetramer; a chimeric construct of acylaminoacyl peptidase fused to maltosebinding protein. From the literature: cytochrome c; transthyretin; bovine serum albumin; avidin; alcohol dehydrogenase; concanavalin A; serum amyloid P pentamer and decamer; pyruvate kinase; glutamate dehydrogenase; beta-galactosidase; GroEL (Hall & Robinson, 2012).

two linear fits is not significantly affected by the probe radius employed for As calculations in the range of 1.2–1.7 Å (data not shown). The conclusion of these studies is that the charge-to-surface relation unifies folded and unfolded proteins much more than the charge-to-mass relation. This observation is merely phenomenological and does not yet provide a mechanistic explanation. Nevertheless, it is consistent with ionization reactions taking place on the surface of protein structures and suggests that the surface development accompanying protein unfolding plays a key role in conformational effects in ESI-MS. The observed relation has also useful practical implications, enriching the structural information that can be extracted from ESI-MS data. The derived As values can be used as constraint for computational modeling, analogously to other, routinely used, MS-based descriptors, like hydrogen exchange (HX) protection factors, protein footprinting, and chemical cross-linking (Jaswal, 2013; Merkley et al., 2014; Pi & Sael, 2013; Politis et al., 2014; Silva, 2014; Walzthoeni et al., 2013). The development of MS-restrained molecular modeling is relevant to the peculiar challenges of structural proteomics, allowing high-throughput structural characterization and investigation of dynamic systems not amenable to conventional biophysical methods. This is the case, for instance, of IDPs (Uversky, 2013). Metastable, compact conformations of these proteins can be detected by native MS, even if poorly populated (Chowdhury, Katta, & Chait, 1990; Grandori, 2002; Kaltashov & Abzalimov, 2008). Among the above-mentioned methods, CSD analysis has the 114

advantage of being much less invasive; however, its major shortcoming is the strict requirement of volatile weak electrolytes. An example of application to IDP conformational studies is offered by the kinase inhibitory domain (KID) of Sic1 from Saccharomyces cerevisiae, which is responsible for inhibition of the cyclin-dependent protein kinase Cdk1 (Barberis, 2012). Modulation of this complex through the cell cycle is responsible for the correct timing of the S phase. Sic1 is an IDP that populates a metastable compact state, which can be detected by native MS (Brocca et al., 2009; Brocca et al., 2011a,b; Testa et al., 2011). Collapsed structures of the KID domain have been modeled by molecular dynamics (MD) simulations in water, leading to representative sampling of the conformational ensemble (Lambrughi et al., 2012). The derived models suggest predominance of electrostatic interactions, consistent with the experimental evidence that the compact conformer responds to acids but not to organic solvents (Lambrughi et al., 2012). Thus, these conformations seem to represent reliable models of the Sic1 KID partially folded state and offer atomic structures for As calculation. The As values calculated from the simulated molecular ensemble vary between 53 and 65 nm2, while the average experimental value derived from ESI-MS is 58.6  1 nm2 (Fig. 3). The good agreement between these results provides further support to the hypothesis that As can, indeed, be accurately derived from ESI-MS data, also for partially folded states.

C. Predicting Protein Flexibility and Conformational Changes Upon Binding Globular, folded proteins display a well-known relationship between As and molecular weight (M). From the original equations for total and buried areas calculated by Chothia in 1975 (Chothia, 1975) it follows that As ¼ 0:581  M  0:774  105  M 2

ð6Þ

the equation was then revisited by Teller (Teller, 1976) As ¼ ð11:116  0:161Þ  M 2=3

ð7Þ

by Miller (Miller et al., 1987) As ¼ 6:3  M 0:73

ð8Þ

and recently by Marsh (Marsh & Teichmann, 2011) As ¼ 4:84  M 0:760

ð9Þ

Equation (9) allows accurate prediction of As values for monomeric, folded, globular proteins, including multi-domain structures, spanning a range from ca. 2 to 120 kDa. Since this correlation is due to the common structural features of folded, globular structures, deviations from this behavior are indicative of structural disorder and conformational flexibility (Marsh, 2013; Marsh & Teichmann, 2011; Testa et al., 2013). In Mass Spectrometry Reviews DOI 10.1002/mas

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FIGURE 3. Average As values for partially folded states of Sic1 KID. (A) Nano-ESI-MS spectrum of Sic1 KID under non-denaturing conditions (50 mM ammonium acetate, pH 6.5), showing an extended and a compact component. As values for the compact form were calculated by the equations reported in Fig. 2. (B) Example of compact conformation of Sic1 KID extracted from the conformational ensemble modeled by MD simulations. The 3D structure is highlighted by a color gradient, from N-terminus (cyan) to C-terminus (yellow). As values were calculated on the simulated molecular ensemble. The figure was adapted with permission from Lambrughi et al., 2012.

particular, it has been shown that the relative solvent accessible surface area (Arel) Arel ¼

Aobserved s Apredicted s

ð10Þ

is a good predictor of chain flexibility and extent of conformational change upon binding to interactors. Here, Asobserved is the experimental As of a given protein chain, while Aspredicted is the As value of a compact globular protein of the same size, predicted by equation (7). The Asobserved value used to compute Arel can be derived from the structure of the free protein or from the bound conformation, upon isolation from the rest of the complex, resulting in the Arel(free) or Arel(bound) parameter, respectively. Arel(free) correlates well with chain flexibility, as measured by several experimental and computational methods (Marsh, 2013; Marsh & Teichmann, 2011). Furthermore, Arel(free) correlates with the root-mean-square deviation (RMSD) between free and bound protein structures in hetero-oligomeric protein complexes, offering quantitative estimates of induced conformational changes (Marsh, 2013; Marsh & Teichmann, 2011). In the absence of experimental structural models, as in the case of IDPs, Arel(free) can be calculated from the As values provided by MS (Equation 5), yielding, in turn, prediction of Mass Spectrometry Reviews DOI 10.1002/mas

chain flexibility and induced conformational changes. In line with this kind of analysis, deviations from the ionization behavior of folded globular proteins in ESI-MS has been shown to provide a powerful tool for the identification of IDPs (Testa et al., 2013). It has been shown that Arel(bound) should be used, rather than Arel(free) in order to estimate the interaction surface area and the conformational change upon binding for homo-oligomers (Marsh & Teichmann, 2011). This is likely due to the predominance of domain swapping in the binding mechanism of this class of assemblies. This mechanism combines large conformational changes upon binding with highly compact conformations of the monomers and, therefore, is associated with large Arel(bound) but small Arel(free) values. Unfortunately, CSD analysis does not provide straightforward information about Arel(bound). This information could, in principle, be derived upon dissociation of the subunits of a given complex, calculating Asobserved in the bound state from the number of charges that the subunit had acquired during ionization of the complex. However, such a procedure is hampered, in practice, by asymmetric charge partitioning during complex dissociation by collision-induced dissociation (CID) (Jurchen & Williams, 2003). Indeed, due to partial or complete protein unfolding during dissociation, subunits departing from a complex can be 115

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disproportionally charged, relative to the original partial charge inside the complex. Methods are being developed to counteract such dissociation pathways, in favor to more structurally conservative mechanisms (Boeri Erba et al., 2010; Hall et al., 2013; Hall, Politis, & Robinson, 2012; van den Heuvel et al., 2006). However, it should also be noted that these methods are based on charge manipulation of the complexes, by either supercharging or charge-reduction reagents. Therefore, Arel(bound) measurements by native MS might remain difficult. Nevertheless, other MS methods, like HX-MS or protein footprinting, could provide this kind of information (Haladova et al., 2012; Sinz, Kalkhof, & Ihling, 2005).

D. Solution Versus Gas-Phase Structures There is an intense debate in the literature concerning comparison of solution and gas-phase protein structures. All the analyses described above refer to solvated structures. Indeed, experimental structures from NMR or crystallographic studies have been used to compute the As values for Equation 9. Thus, if a correlation between such values and ESI charge states exists, it can be used to predict solution As values from MS data, independently from structural changes that the protein might encounter in the gas phase. However, it is interesting to ask whether a similar correlation holds for gas-phase structures. To test this hypothesis, structural models for desolvated protein ions produced by ESI are needed (Verkerk, 2014a; Verkerk, 2014b). As discussed in the following sections, development of such models is complicated by the extraordinary large number of different patterns of ionized residues (protomers) corresponding to a given value of net charge. A simulation pipeline, developed to this purpose, provides us with atomic models for the estimated, most probable protomer of a given protein in the gas phase, from which As values can be computed. Also in this case, a linear dependence seems to be present in the double-log charge-to-surface plot, as shown from data in the 4–30 kDa range (Marchese et al., 2012). The best linear fit yields the equation lnðZ av Þ ¼ 0:401  lnðAs Þ  1:426

ð11Þ

Hopefully, the number and the molecular-weight range of the available models will increase in the future, yielding more accurate fitting. In conclusion, using the appropriate equation, solution or gas-phase As can be predicted from MS data.

E. Apparent Gas-Phase Basicity To further investigate the molecular mechanisms underlying conformational effects in protein ESI, it is essential to interpret proton-transfer reactions by energy calculations and, therefore, provide reliable values of gas-phase basicity (GB) (Kebarle & Verkerk, 2010). This analysis refers to protein charging by “pure protonation”. More complex scenarios emerge when protonation competes with other cation adduct formation, based on physicochemical properties of the additives and the nature and number of the protein sites (Douglass & Venter, 2012; Flick et al., 2012; Frey et al., 2008; Liu & Cole, 2014; Verkerk & Kebarle, 2005; Verkerk, Peschke, & Kebarle, 2003). GB measures the propensity of acquiring a proton under vanishing-solvent conditions. In the case of proteins, the tabulated values of intrinsic GB for 116

ionizable residues need to be corrected, accounting for intramolecular interactions and, thus, yielding the conformation-dependent parameter, apparent GB (GBapp) (Peschke, Blades, & Kebarle, 2002; Peschke, Verkerk, & Kebarle, 2004; Verkerk, Peschke, & Kebarle, 2003). Computing As and GB poses opposite problems with regard to protein conformational state. While unfolded proteins represent an obstacle for As calculation, due to the lack of structural models, folded proteins represent the major challenge for GB calculations, because of a more complex influence of intramolecular interactions on the intrinsic GB of ionizable sites. In particular, unfolded proteins can be reasonably modeled assuming that acidic groups exist in their protonated, neutral form. Under this assumption, only basic groups contribute to protein charge, implying that the total number of ionized residues equals the net charge of the protein. This assumption is in agreement with experimental evidence showing that capping carboxylic acid groups with neutral functional groups does not significantly affect CSDs of unfolded proteins (Krusemark et al., 2009; Schnier, Gross, & Williams, 1995b). The GBapp of unfolded proteins has been calculated by this assumption back in 1995 (Schnier, Gross, & Williams, 1995b). Accounting for Coulomb repulsions in extended protein conformations, it has been shown that the calculated GBapp of a given protein in its highest observable charge state approaches the GB of the solvent used for sample infusion (Schnier, Gross, & Williams, 1995b). However, intramolecular interactions specific of the native conformation could stabilize zwitterionic states of the proteins, even in the gas phase (Grandori, 2003b), leading to a very large number of possible protomers (particularly for low charge states), which need to be accounted for. Considering a protein with N protonable sites, n of which are protonated, there are N!/ n! (N-n)! possible protomers (Jarrold, 2000; Schnier, Gross, & Williams, 1995a). Even for a small protein-like cytochrome c (105 amino acids), the number of different protomers is enormous (Jarrold, 2000; Schnier, Gross, & Williams, 1995a). To cope with this challenge for GBapp calculations on folded proteins, several Monte-Carlo (MC) (Miteva, Demirev, & Karshikoff, 1997; Saikusa et al., 2013) and pseudo-randomwalk (Schnier, Gross, & Williams, 1995a) protocols have been proposed. The GBapp of folded cytochrome c has been calculated from a simple model in which the charged sites have been assigned by using a pseudo-random-walk algorithm (Schnier, Gross, & Williams, 1995a). The derived GBapp value for the 11þ ion (the maximum charge state observed for native-like cytochrome c in ESI-MS) (Smith et al., 1991) has been shown to be very close to the GB value for water, suggesting that the GB model, initially developed for unfolded proteins (Schnier, Gross, & Williams, 1995b), could be extended to folded ones. In line with this result, it has been shown (Miteva, Demirev, & Karshikoff, 1997) that the charge states for the minimum values of GBapp calculated from a Coulomb formula (Gross et al., 1996) on the most probable protomers identified by a MC procedure are consistent with the CSDs detected by ESI-MS of native-like proteins. For instance, the calculated minimum-energy states are 8þ and 9þ for lysozyme and between 6þ and 8þ for ubiquitin, closely corresponding to the experimentally measured charge states (Cassady et al., 1994; Gross et al., 1996). However, all these protocols and calculations suppose that the protein structure does not change with the protomer and they Mass Spectrometry Reviews DOI 10.1002/mas

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usually assume that the protein structure in the gas phase is similar to that in solution. This might be true for the protein backbone, in particular cases, but not for side chains. The latter undergo wide reorganizations in the course of the dehydration process, optimizing self-solvation, as suggested by theoretical studies (Barril et al., 1998; D’Abramo et al., 2009; Meyer, de la Cruz, & Orozco, 2009; Patriksson, Marklund, & van der Spoel, 2007; Steinberg et al., 2007) and experimental observations (Adams et al., 2004; Iavarone et al., 2007).

F. Predicting Protomer and GBapp for Folded Proteins and Protein Complexes Based on Williams’s formulation (Schnier, Gross, & Williams, 1995b), a novel protocol has been recently developed in order to calculate the GBapp of folded proteins by using a combined MC/ MD scheme (Marchese et al., 2012). In contrast to the original formulation (Schnier, Gross, & Williams, 1995b), in which only the point-charge Coulomb interactions were considered for the GBapp calculation, all of the classical energy terms were included to completely take into account the contributions from intramolecular interactions and conformational changes in folded proteins. Additionally, a MC/MD scheme to scramble protons on all the possible ionizable sites was introduced, in order to identify the most probable protomers for the GBapp calculations. The calculations based on such a protocol on nine proteins of variable size and fold have shown that the calculated GBapp values on the most probable protomers decrease linearly (R2 ¼ 0.99) as the protein net charge increases (Fig. 4A) (Marchese et al., 2012). Furthermore, the intersection of the GBapp fitted line with the line of solvent GB corresponds with remarkable agreement to the experimental main charge state under ESI-MS conditions (Marchese et al., 2012). This result establishes the relevance of GBapp for folded proteins under ESI-MS conditions, i.e., the liquid medium of the precursor droplet provides (or accepts)

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protons according to its GB (solvent nature) relative to the GBapp of the protein (intrinsic protein features). Very recently, the MC/MD scheme for monomeric proteins were extended to a protein complex, the human insulin dimer, to test its applicability on more complex biological systems, as well as to develop a comprehensive protocol for structural prediction of protein assemblies under ESI-MS conditions (Li et al., 2014). In line with previous calculations on single proteins, the GBapp values of the protein complex decrease linearly (R2 ¼ 0.99) with the increase of net charge (Fig. 4B). Notably, the main charge state of the protein complex derived from this protocol is in remarkable agreement to the experimental value from ESI-MS (Fig. 4B). These results show that this protocol is quite promising and robust for either monomeric proteins or protein complexes, allowing accurate calculation of GBapp and prediction of the most probable protomers. Specifically, the predicted protomer can be further used for MD simulations to study the structural determinants of proteins and protein complexes in the gas phase. For example, 0.075-ms MD simulations in vacuum on the human insulin dimer, performed on the predicted most probable protomer for the main charge state, have correctly reproduced the experimentally determined collisional cross section (CCS) (Li et al., 2014). Altogether, the available evidence strongly suggests that the ionization behavior of folded and unfolded proteins can be explained in terms of GBapp values of the protein structure, relative to the GB of the solvent. These results also provide strong support to the hypothesis that the specific intramolecular interactions of native-like protein structures dramatically affect the protonation propensity of ionizable groups, leading to zwitterionic structures of folded proteins transmitted to the gas phase by nondenaturing electrospray (Grandori, 2003b). Such a mechanism is likely at the basis of conformational effects in protein ESIMS. This hypothesis has been referred to as conformationdependent neutralization (CDN) theory (Nesatyy & Suter, 2004). The original formulation of this hypothesis was

FIGURE 4. Average GBapp (in kJ/mol) calculated for the most probable protomers of folded protein and protein complex (black line and black circles). (A) Hen egg-white lysozyme. Orange circles represent GBapp values calculated from the non-optimized (pdb) structures. The orange line is the result of a linear fitting. Standard deviation from the average is given as error bar (when not visible, the standard deviation is smaller than the symbol size). The horizontal lines indicate the GB of various solvents: water (red line), isopropanol (blue line), ammonia (purple line), triethylammonium bicarbonate (cyan line), 1,5-diazabicyclo[4.3.0]-5-ene (green line). The experimental main charge states observed from these solvents are shown by symbols colored accordingly. (B) Human insulin dimer. The red horizontal line indicates the GB of water (660.3 kJ/mol taken from Hunter and Lias, 1998. The experimental main charge state from water solutions (Salbo et al., 2012) is shown by a red solid circle. Panel A was adapted with permission from Marchese et al. 2012); panel B was adapted with permission from Li et al. 2014.

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accompanied by the argument that the net charge of basic proteins in positive-ion mode and that of acidic proteins in negative ion-mode approaches the difference between basic and acidic residues, consistent with a highly zwitterionic nature (Grandori, 2003b). This argument has been later on misinterpreted as a general predictive rule for folded proteins in ESI-MS (Nesatyy & Suter, 2004; Touboul, Jecklin, & Zenobi, 2008). All the weak forces stabilizing folded protein conformations have an electrostatic component and, therefore, could contribute to conformational effects on GBapp. These include salt bridges, neutral and charged hydrogen bonds, van der Waals, p-charge, and long-range electrostatic interactions (Grandori, 2003b; Marchese et al., 2012). Existence of salt bridges in the gas phase has been postulated for peptide models (Boutin et al., 2007; Pollreisz et al., 2005; Prell et al., 2009). Conservation and/ or formation of ion pairs in folded proteins ions in the gas phase has been suggested, too, based on ion/molecule reactions (Stephenson & McLuckey, 1997), experimental GBapp determination by electrosonic spray ionization (ESSI) (Touboul, Jecklin, & Zenobi, 2008), native electron capture dissociation (ECD) (Steinberg et al., 2008), and ultra-violet photodissociation (UVPD) (Kjeldsen, Silivra, & Zubarev, 2006). A zwitterionic nature of protein ions generated by electrospray has also been inferred by the analysis of adducts with salts (Prakash, Kansara, & Mazumdar, 2010; Prakash & Mazumdar, 2005; Verkerk & Kebarle, 2005).

G. Protein Structure Versus Rayleigh Limit The originally observed charge-to-mass relation of folded, globular proteins has been interpreted as reflecting the Rayleigh-limit charge of the precursor ESI droplet (de la Mora, 2000; Heck & Van Den Heuvel, 2004; Nesatyy & Suter, 2004). Indeed, the experimental points can be fitted well by the Rayleigh equation (Rayleigh, 1882), which describes the limit charge (QR) of liquid droplets of radius R and surface tension g before Coulomb explosion and governs droplet evolution in the electrospray plume (Kebarle & Verkerk, 2009)  1=2 QR ¼ 8p e0 gR3

ð12Þ

where e0 is the electrical permittivity of vacuum. The fitting has been performed assuming a droplet size equal to the protein structure, approximated to a sphere, and employing the value of water surface tension at 25 ˚C (de la Mora, 2000). However, it has also been shown that this correlation does not hold when solvents with different surface tension than water are considered, suggesting that the observed correlation might reflect some other properties of the system (Samalikova & Grandori, 2003, 2005; Samalikova et al., 2004). In particular, protein charging seems to be heavily affected by the specific structural features and not merely interpretable as a reflection of droplet charging. Furthermore, droplet charging itself is affected by solvent composition in a very complex way (Grimm & Beauchamp, 2010; Verkerk, 2014a). The calculations described above offer a possible alternative interpretation. Indeed, the derived gas-phase structural models show that native-like protein structures have a quite constant charge-solvation potential per surface unit. This feature results from the balance between repulsive electrostatic terms 118

and stabilizing contributions, including salt bridges (Marchese et al., 2010, 2012). Based on the aforementioned MC/MD protocol, a simple and general model for the correlation between charge and mass for any folded, globular protein can be extracted (Fig. 5) (Marchese et al., 2012). The model reproduces the observed relation without involving adjustable parameters and without introducing any dependence on the solvent surface tension. Thus, the experimentally observed charge states can be interpreted in terms of charge-solvation potential of the protein structure, rather than droplet charge. Furthermore, this model offers a molecular basis to the charge-to-surface relation in protein ionization under electrospray conditions. In summary, molecular modeling and other computational techniques can significantly enhance the amount of information that can be gained from ESI-MS data. Their synergism with experimental research is giving an important contribution to the understanding of the ESI behavior of folded and unfolded proteins. The vast majority of structural studies employing MS have focused on proteins, while only a limited number of publications have dealt with other macromolecules, such as glycans and nucleic acids (Abzalimov, Dubin, & Kaltashov, 2007; Fabris, 2011; Guo et al., 2005; Han & Costello, 2013; Kailemia et al., 2014; Laughlin et al., 2014; Rosu, De Pauw, & Gabelica, 2008; Touboul & Zenobi, 2009). Relevant to the present discussion is the observation that oligonucleotides display linear double-log charge-to-mass plots similar to polypeptides, in either positiveor negative-ion mode (Touboul & Zenobi, 2009). The common features of this dependence suggest that a similar tradeoff between ionization and destabilization affects the ESI process of nucleic acids. Distinct coefficients likely reflect the different chemical nature of these two classes of analytes. Bringing the analogy to proteins a step further, conformational effects also seem to characterize ESI-MS of nucleic acids (Guo et al., 2005; Touboul & Zenobi, 2009). Although the initially reported effect was reversed, compared to proteins (Guo et al., 2005), it seems that the same trend emerges if ammonium adduct formation is prevented (Touboul & Zenobi, 2009). Thus, it is conceivable

FIGURE 5. Experimental average charge state as a function of the protein mass. The curves predicted by the Rayleigh-charge hypothesis (blue line) and the model introduced in Marchese et al. 2012 (red line) are shown. The figure was adapted with permission from Marchese et al., 2012.

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that conformational studies on nucleic acids will profit by future applications of native MS and modeling.

II. CONCLUSIONS Although many parameters affect the final results of the electrospray process, structural information on proteins and protein complexes can be retrieved from CSD analysis by native ESIMS under controlled experimental conditions. Estimated As values can be used for prediction of chain flexibility, IDP identification, and development of structural models by restrained molecular simulations. The calculated GBapp values of the either folded or unfolded proteins compared to the solvent can be related to the final ionization state. The influence of intramolecular interactions on GBapp sheds light on protein conformational effects in the electrospray process. The present analysis is still compatible with both the current, alternative models concerning the ESI mechanism, the ion-evaporation model (IEM) (Iribarne & Thomson, 1976) and the chargedresidue model (CRM) (Dole et al., 1968). Further studies will be required to understand which model more adequately describes the production of protein ions in the gas phase (Konermann et al., 2013; Ogorzalek Loo, Lakshmanan, & Loo, 2014; Verkerk, 2014b; Yue, Vahidi, & Konermann, 2014).

ACKNOWLEDGMENTS The authors are thankful to Udo Verkerk for insightful discussions and critical reading. The authors gratefully acknowledge the support from the John von Neumann Institute for Computing (NIC), Germany.

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Mass Spectrometry Reviews DOI 10.1002/mas