Energetics of membrane protein folding.

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Energetics of Membrane Protein Folding Karen G. Fleming T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218; email: [email protected]

Annu. Rev. Biophys. 2014. 43:233–55

Keywords

The Annual Review of Biophysics is online at biophys.annualreviews.org

thermodynamics, protein–lipid interactions

This article’s doi: 10.1146/annurev-biophys-051013-022926

Abstract

c 2014 by Annual Reviews. Copyright All rights reserved

Fundamental to the central goals of structural biology is knowledge of the energetics of molecular interactions. Because membrane proteins reside in a free energy minimum dictated by their sequences, their lipid environment, and water, one must understand the energetics of membrane protein folding to generate physical descriptions of cellular processes. Several technical obstacles have recently been overcome to enable folding measurements for membrane proteins in lipid and detergent micelle environments, and several new folding free energies have been published within the past ten years. This review discusses the challenges, successes, and novel insights into the physical basis underlying membrane protein folds.

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Contents


INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CHALLENGES TO STABILITY MEASUREMENTS OF MEMBRANE PROTEINS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Theoretical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solubility of the Unfolded State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unfolding the Unfolded State, or, Alternatively, Defining the Conformational Nature of the Unfolded State That Can Be Populated . . . . . . . . . . . . . . . . . . . . . . . . . Refolding the Unfolded State and Unfolding the Folded State . . . . . . . . . . . . . . . . . . . . Overcoming Hysteresis: Folding and Unfolding Titrations Must Overlay . . . . . . . . . . WATER-TO-BILAYER MEMBRANE PROTEIN STABILITIES . . . . . . . . . . . . . . . . . OmpA325 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PagP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OmpLA and OmpW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of β-Barrel Stabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INTRAMEMBRANOUS UNFOLDED (IM U) TO FOLDED α-HELICAL MEMBRANE PROTEIN STABILITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bacteriorhodopsin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diacylglycerol Kinase (DGK) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peripheral Myelin Protein 22 (PMP22) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glycophorin A as a Model for Transmembrane Helix–Helix Dimerization . . . . . . . . . CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

234 235 235 237 238 238 238 240 240 242 243 245 246 247 249 249 250 251

INTRODUCTION In their classic 1999 review, White & Wimley (93) articulated a comprehensive thermodynamic framework outlining the physical forces to be considered in understanding and predicting the stabilities of transmembrane proteins. This insightful article illustrating the power of biophysical investigations on model compounds was published before any water-to-bilayer folding free energies were known for any transmembrane proteins. Rather, at the time, all knowledge of the physical forces stabilizing membrane proteins was derived from studies employing peptides or mimics of protein moieties as representations of membrane proteins (MPs). The most influential and contemporary hydrophobicity scale used an organic solvent, water-saturated octanol, as a representation of the bilayer interior, but it still was remarkably accurate in identifying and predicting regions of transmembrane α-helices (94). As Haltia & Freire observed in 1995 (32), it was generally accepted that the intramembrane regions of constitutive MPs were resistant to full denaturation either by temperature or by chemicals commonly used in stability studies of soluble proteins. And despite Khorana’s 1981 (42) successful refolding of bacteriorhodopsin (bR) without the need for an external energy source, it is well known that mechanical energy of the ribosome is used in the cell in the process of biosynthesizing membrane proteins (6, 47, 91, 92). This latter fact leads to the thought that somehow the molecular machinery required for folding in the cell encodes special conformational instructions that direct the MP to its final folded structure. This thinking encouraged skepticism about the Anfinsen hypothesis (5) and questioned whether MPs reside in a free energy minimum in the cell. The seeming inability to reversibly fold and refold MPs in vitro confounded resolution of this paradox.

MP: membrane protein bR: bacteriorhodopsin

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However, it has become clear that the Anfinsen hypothesis generally applies to MPs and that these proteins do in fact reside in a free energy minimum (5). There has been a virtual explosion of energetic data on transmembrane proteins, and the thermodynamic stabilities of seven different transmembrane proteins are now available. Four of these stabilities are measurements of waterto-bilayer free energies, and three are stabilities determined between intramembranous unfolded conformations and their corresponding native states. Although soluble protein folding systems far outnumber tractable MP systems, these systems have been considerably amenable to mutagenesis, leading to studies that have interrogated the energetic contributions of hydrogen bonding, transmembrane α-helix kinking, van der Waals packing, side chain transfer free energies, and the lipid bilayer. The ability to measure MP stabilities further enables probing of nearest-neighbor energetics between side chains, and several double-mutant cycle analyses have been executed that investigate cooperativity in such side chain interactions. Finally, two new hydrophobicity scales using transmembrane proteins and phospholipid environments have entered the field, leading to even greater insight into the physical forces at play in the cell. Still, understanding MP folding remains a challenging endeavor, and progress continues to lag far behind that in the field of soluble protein folding. Because overcoming these challenges is key to the next quantum leap in MP folding advances, the review begins with a discussion of these experimental issues, followed by descriptions of successful membrane protein folding systems and the physical and biological insights arising from these studies.

CHALLENGES TO STABILITY MEASUREMENTS OF MEMBRANE PROTEINS Theoretical Considerations Why are there so few thermodynamic stability measurements of membrane proteins? Often, the blame is attributed to the folded conformation. Membrane proteins can be difficult to isolate in native forms from their biological environments because doing so involves solubilizing membranes in detergents prior to purification. Eukaryotic membrane proteins are often recalcitrant to bacterial expression in large quantities because the biosynthetic machinery provided by bacteria is sufficiently different from that of the native environment that biological folding is not supported. In addition, detergents vary widely in their abilities to support the conformations of folded membrane proteins, and membrane proteins vary in their detergent preferences, so finding a good environment is still very much trial and error. Despite the aforementioned issues, mounting evidence suggests that the folded state is not the ultimate problem. Rather, nonnative interactions and the reactions of unfolded and partially folded conformations may pose the more significant obstacles to folding membrane proteins both in vitro and in vivo. The fact that all cells have developed elaborate machinery to enable membrane protein folding in vivo indicates that unfolded and partially folded conformations must be specifically managed by the cell. Especially for α-helical membrane proteins, solubility in an aqueous solution—such as the cytoplasm—is a problem. Cells solve this problem by cotranslationally translocating nascent polypeptide chains into the cell’s ultimate transmembrane chaperone—the specialized compartment known as the translocon—which has a unique ability to promote attainment of the native MP conformation by preventing proteins in the unfolded state from aggregating. The translocon further provides a chamber that allows a transmembrane region to sort itself to the chemical environment (the lipid bilayer) that supports its free energy minimum (6, 47, 91, 92). Understanding the possible reactions and interactions of the unfolded state in vitro should yield insight into the challenge that the cell must overcome. The functions of chaperones cannot www.annualreviews.org • Energetics of Membrane Protein Folding

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be defined unless one first knows what an MP can do on its own. As our group sees it, there are four main challenges involved in thermodynamic folding experiments that investigate membrane proteins, three of which concern the unfolded state, U: (a) the solubility of the unfolded state; (b) unfolding the unfolded state, or, alternatively, defining the conformational nature of the unfolded state that can be populated; (c) forming the folded state from whatever the unfolded state is; and (d ) simultaneously measuring folded and unfolded populations under conditions that avoid hysteresis. All of these issues are significant roadblocks to the—in principal—extremely simplistic goal of measuring the equilibrium constant for folding. I first review the fitting equations that are typically employed for these experiments because their examination highlights the requirements for their use. It is customary to write the equilibrium constant for the unfolding reaction, i.e., the F→U transition, as follows:


KU =

[U] , [F]

1.

where [F] and [U] are the concentrations of the folded and unfolded conformations, respectively, and K U equals the unfolding equilibrium constant between these two. The free energy of unfolding thus equals ◦

GU = −RT lnK U .

2.

Under folding conditions, most proteins display such a vanishingly small population of U that it cannot be experimentally detected. Therefore, to simultaneously observe F and U, experimentalists typically add a chemical denaturant that stabilizes the unfolded state more than the folded state and measure the reaction as a function of this denaturant. Empirically, it has been observed that there exists a linear relationship between the free energy of folding and the denaturant concentration, which is given by the following equation: ◦

◦

GU ([Den]) = mG ∗ [Den] + GU,Water .

3.

◦

In this equation, GU ([Den]) is the observed free energy change as a function of denaturant concentration, [Den]; the slope mG is referred to as the m value; and the intercept equals the free energy of unfolding in the absence of any denaturant. This latter parameter is the one of interest and is sometimes colloquially referred to as the “free energy of unfolding in water,” but, of course, these experiments are conducted in the presence of buffered solution, and the water designation simply means “extrapolated to the absence of denaturant.” In practice, both the folded and unfolded states often have a spectral response as a function of the denaturant concentration, and this response is usually linear with a nonzero slope. Therefore, the fitting equation most commonly employed has four additional fitted terms: the slope and intercept of the folded baseline and the slope and intercept of the denatured baseline. Because the errors will be best distributed if the data are fit directly, the final equation (for a two-state transition) takes the following form (70, 81, 82): ◦ GU,Water − mG [Den] (SF + mF [Den]) + (SU + mU [Den]) ∗ exp − RT ◦ , 4. S= GU,Water − mG [Den] 1 + exp − RT where S is the experimental signal at any concentration of denaturant, [Den], and SF and SU and mF and mU represent the intercepts and slopes of the folded and unfolded baselines, respectively. All three regions of the graph are important for well-determined folding free energy estimates: The transition region is where F and U can be observed simultaneously, and the baselines, highlighted by the dashed lines in the simulated data in Figure 1, provide the two signal limits across the titration that are needed to know what fraction of the signal arises from each population.

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Transitional region 1.0

Arbitrary normalized signal

a

0.8

Folded baseline

0.6 0.4 0.2 Unfolded baseline

0.0

b

100 Folded population

Unfolded population

80

Speciesi (%)


–0.2

60

40

20

0 0

2

4

6

8

[Denaturant] (M) Figure 1 Important regions of chemical denaturation data. The folded and unfolded baselines are marked. The steep region between them is the transition region (shaded gray). (a) The closed blue circles represent a folding titration; the open circles represent an unfolding titration. (b) The percentages of the folded and unfolded populations. The parameters used in the simulated data in panel a are as follows: SF = 1.0, SU = 0.0, mF = −0.025, mU = −0.025, G◦F,Water = 7.0 kcal mol−1 , and mG = 1.5 kcal mol−1 Molar [Denaturant]−1 . The percentage of each species in panel b was calculated assuming that %F +%U = 100%.

Solubility of the Unfolded State The lack of terms in Equation 4 that account for precipitation or oligomerization of either the folded or unfolded conformations indicates that this basic two-state linear extrapolation equation assumes that all protein forms are fully soluble and monomeric. For water-soluble proteins, lack of aggregation and solubility of the unfolded conformation in a denaturant buffer are usually ensured. However, populating a monomeric denatured state ensemble is not necessarily a straightforward endeavor in the case of membrane proteins. The source of this problem lies in the amino acid distributions in the primary sequences of membrane proteins as compared with those of soluble proteins. Solubilizing the denatured states of α-helical membrane proteins is especially problematic. The polypeptide chains of transmembrane domain (TMD) sequences typically have consecutive stretches of ∼20 hydrophobic amino acid residues in their primary sequence, allowing these domains to eventually span the phospholipid bilayer as α-helices when folded. Although www.annualreviews.org • Energetics of Membrane Protein Folding

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DMPC: 1,2-dimyristoyl-snglycero-3phosphocholine


CHAPS: 3-[(3cholamidopropyl) dimethylammonio]-1propanesulfonate

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urea and guanidine hydrochloride (GdnHCl) have chaotropic effects, neither denaturant wholly disrupts the hydrophobic effect in aqueous solutions. Therefore, neither will be a good solvent for such hydrophobic sequences when they are fully exposed. Upon removal of the hydrophobic cosolvent (detergent micelle or lipid vesicle) of a helical membrane protein, the polypeptide sequence is highly likely to either precipitate from solution or form a significant aggregate. Even membrane proteins with a single TMD precipitate when the detergent or lipid cosolvent is removed. Neither of these scenarios represents a population of monomeric denatured protein in a folding-competent conformation as required in Equation 4. The solubility issue is less of a problem for transmembrane β-barrel proteins, presumably because their primary sequences contain alternating patterns of hydrophobic and hydrophilic residues. Consequently, there are many more reports describing the folding of transmembrane β-barrels than regarding α-helical membrane proteins (86). However, Stanley & Fleming (86) reported anecdotal evidence that the solubility of the unfolded state can also be a problem for transmembrane β-barrels, giving the example of three different OmpLA homologs from Escherichia coli, Campylobacter jejuni, and Helicobacter pylori. Although the sequences are (pairwise) 20% identical and 40% similar, they exhibited widely different solubility characteristics in urea and GdnHCl (86).

Unfolding the Unfolded State, or, Alternatively, Defining the Conformational Nature of the Unfolded State That Can Be Populated When denaturants are added to folded helical MPs in micelles or bilayers, they may not be strong enough to extract the protein from the bilayer environment into the aqueous solution to denature it. Chen & Gouaux (11) published a classic example of this: Seven molar urea is actually a folding condition for bR in dimyristoyl phosphatidylcholine/3-[(3-cholamidopropyl)dimethylammonio]2-hydroxy-1-propanesulfonate (DMPC/CHAPSO). In fact, because of their lack of solubility and an inability to promote unfolding, no thermodynamic studies of helical membrane proteins have reported measurements of the free energy change between a fully unfolded and fully folded conformation. As discussed in the specific examples below, investigators circumvent this issue in α-helical membrane proteins by redefining the folding question from one that considers the classical unfolded state, U, i.e., the state of being fully unfolded in water, to one that considers the intramembranous unfolded state, IM U, which refers to a nonnative state embedded in the bilayer. The IM U often contains some fraction of the native α-helix content, although whether or not this helix content is the same as in the native structure is a point of discussion.

Refolding the Unfolded State and Unfolding the Folded State Once a denaturing condition has been found, the researcher’s task is to refold the protein into either micelles or bilayers by titrating the denaturant concentration to lower concentrations. This process is one of trial and error, so it is useful to quickly screen conditions by a quick dilution of the denaturant into a folding condition. However, the quick dilution method does not yield thermodynamic stability because both F and U (or IM U) must be simultaneously populated and interconverting on a reasonable time scale to measure the equilibrium constant.

Overcoming Hysteresis: Folding and Unfolding Titrations Must Overlay As researchers have made progress in biophysical folding of MPs, hysteresis in folding curves has come to the forefront as a significant obstacle to the success of these experiments (66). Because one can obtain a sigmoidal response of an MP to either folding or unfolding by titration with 238

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denaturant fairly easily, the issue of hysteresis was a surprising one. However, titration curves are devoid of thermodynamic information unless path independence is demonstrated. A key aspect of folding studies is therefore to appreciate the experimental region over which the folding/unfolding equilibrium is actually being observed. Path independence must be demonstrated within this transition region in order for the experiment to reveal a meaningful thermodynamic stability. Figure 1 shows simulated folding and unfolding titrations exhibiting path independence with the regions of the curve defined. By comparing the signal in Figure 1a with the species populations in Figure 1b, one can observe that the signal in the folded baseline on the left side of Figure 1 is derived only from the folded conformation because [F] [U]; similarly, the observed signal in the unfolded baseline on the right side of Figure 1 is derived only from the unfolded conformation because [U] [F]. It is only within the transition region—the middle region in which the slope is steep and [F] ∼ = [U]—that F and U can be simultaneously observed. Because the equilibrium constant for folding is measured within this region, it is essential that the titrations overlay here. The concept of overall reaction reversibility is often confused with the aforementioned issue of path independence. For example, as shown in Figure 2a, an unfolding titration can be considered reversible if the original signal is recovered following a jump back down to the folded baseline after titration to a predominantly unfolded form (e.g., a single jump in denaturant concentration from 9.5 M to 0 M). Even if the unfolding titration shows the sigmoidal shape characteristic of chemical denaturation experiments and can be fitted to the linear extrapolation equations given by Equations 3 and 4—as it does in this example—the jump back to the beginning does not provide proof that the folding titration itself was in a path-independent equilibrium state. Rather, the folding titration only shows that jumping the denaturant concentration back to one that favors folding can reverse protein unfolding. The inverse is also true: A sigmoidal folding transition does not measure thermodynamic stability if the corresponding unfolding curve does not overlay, even if a denaturant jump back to the start recovers the original signal. A mixed situation can also occur, as shown in Figure 2b. In this case, both titrations are responsive to the change in denaturant concentration, but to different extents. Although the end

a

b

c

1.0

Normalized arbitrary signal


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0.8 0.6

Folding Folding Unfolding

0.4

Unfolding

0.2 0.0 Unfolding –0.2 0

2

4

6

[Denaturant] (M)

8

0

2

4

6

[Denaturant] (M)

8

0

2

4

6

8

[Denaturant] (M)

Figure 2 Three hysteresis scenarios for folding titrations. Folding (closed circles) and unfolding (open circles) directions are indicated in each panel. The red arrow in panel a indicates the single denaturant jump from 9.5 M to the initial (0 M) condition indicated by the star. www.annualreviews.org • Energetics of Membrane Protein Folding

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PMP22: peripheral myelin protein 22 POPC: palmitoyl-oleoylphosphatidylcholine


PC: phosphatidylcholine

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points overlay, the data within the transition region do not coincide and therefore have not reached the path-independent equilibrium conditions required for thermodynamic studies. Moon et al. (67) observed this type of data for OmpA and FadL, and Myers et al. (68) made similar observations in early studies of the peripheral myelin protein 22 (PMP22). Unfortunately, it is not possible to know from data with this shape if either the folding or the unfolding or neither of the titrations is at equilibrium. Pocanschi et al. (72) showed that OmpA displays a completely flat response to unfolding; the general shape of the OmpA response is similar to that shown in Figure 2c. In the same experiment, the folding titration displayed a beautiful sigmoidal shape that was fitted to the linear extrapolation equations. However, because the titration directions do not overlay, no thermodynamic conclusions can be drawn from these data. Finally, it is worth noting that transitions should overlay irrespective of the denaturant employed. Here, I have discussed chemical denaturants explicitly, but the folding and unfolding responses to other denaturants such as temperature and denaturing detergents must also follow path independence for thermodynamic equations to be applied.

WATER-TO-BILAYER MEMBRANE PROTEIN STABILITIES Water-to-bilayer stabilities for integral MPs have been measured for four transmembrane β-barrels, all of which were protein sequences natively found in the outer membranes of a single organism, E. coli. Because lipid compositions can vary significantly between different microorganisms (and between different organelles within a eukaryotic cell), the fact that the first MP stabilities were derived from a set of proteins residing in the same native membrane is fortuitous. All of these sequences have experienced similar evolutionary pressures with respect to their lipid compositions. This commonality simplifies any comparisons between them because the observed differences cannot be simply dismissed as arising from distinct environments; rather, such differences can be attributed to properties of the respective amino acid sequences of these proteins. The thermodynamic values obtained to date are summarized in Table 1. Details of these studies are described below.

OmpA325 In 2004, the Tamm group (40) was the first to report the thermodynamic stability for full-length OmpA (OmpA325 ) using urea as the denaturant and small unilamellar vesicles (SUVs) as the lipid bilayer geometry. In 92.5:7.5 (mol:mol) palmitoyl-oleoyl-phosphatidylcholine:palmitoyl-oleoylphosphatidylglycerol (POPC:POPG), the OmpA325 folding free energy, G◦F,Water , was −3.4 kcal mol−1 . Hong & Tamm (40) further showed that the OmpA325 stability depended strongly on the lipid composition and varied over a wide range. By substituting guest lipids in place of the POPC, the G◦F,Water became more favorable upon lengthening or desaturating the phosphatidylcholine (PC) acyl chains. In contrast, titrating in guest lipids with fully saturated or shorter chains resulted in OmpA325 destabilization. The magnitudes of these lipid effects on folding are noteworthy as the full range of G◦F,Water values spanned nearly 8 kcal mol−1 . Hong & Tamm (40) rationalized these findings by positing that an increase in lateral bilayer pressure is accompanied by an increase in stability. The equilibrium m values showed similar linear trends, albeit with smaller magnitudes, and the m value dependence was attributed to changes in hydrophobic thickness mismatch and its subsequent impact on the change in the water-accessible surface area of OmpA325 in the folded state (40). In subsequent studies, OmpA325 was used as a thermodynamic platform for mutagenesis studies that interrogated the consequences of disrupting the central salt bridge involved in gating or 240

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Table 1 Folding free energies of membrane proteins


Protein

Folding Free

Lipid

Energy (kcal mol−1 )

Composition and Geometry

Denaturant

Buffer

Temperature (◦ C)

Denatured State Ensemble

Reference

OmpA325

−3.4

POPC:POPG 92.5:7.5 mol:mol SUV

Urea

10 mM glycine, 2 mM EDTA, pH 10

37.5

Off the bilayer in water

40

OmpA325

−7.0

Extrapolated to diC16:1 PC in the presence of 7.5 mol% POPG SUV

Urea

10 mM glycine, 1 mM EDTA pH 9.2

37


40

OmpA325

−9.2

diC16:1 PC:POPG 89.5:10.5 mol:mol SUV

Urea

10 mM glycine, 1 mM EDTA pH 9.2

37


37, 39

PagP with Nterminal His-tag

−14.4

diC12 PC LUV

Urea

pH 8.0

25

On the bilayer surface

44

OmpLA

−32.1

diC12 PC LUV

GdnHCl

pH 3.8

37


65

OmpW

−18.6

diC12 PC LUV

GdnHCl

pH 3.8

37


67

PagP

−24.4

diC12 PC LUV

GdnHCl

pH 3.8

37


67

bR

−20.6

DMPC/CHAPS micelles

SDS

pH 6.0

25

Intramembranous unfolded state

14

DGK

−16

DM micelles

SDS

pH 7.0

Room temperature

Intramembranous unfolded state

52

Abbreviations: bR, bacteriorhodopsin; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate; DGK, diacylglycerol kinase; diC12 PC, dilauroyl-phosphatidylcholine; DM, n-decyl β-D-maltoside; DMPC, 1,2-dimyristoyl-sn-glycero-3-phosphocholine; EDTA, ethylenediaminetetraacetic acid; GdnHCl, guanidine hydrochloride; LUV, large unilamellar vesicle; POPC, palmitoyl-oleoyl-phosphatidylcholine; POPG, palmitoyl-oleoylphosphatidylglycerol; SDS, sodium dodecylsulfate; SUV, small unilamellar vesicle.

removing aromatic side chains (37, 39). These studies used diC16:1c9 PC:POPG (9:1) SUVs because these vesicles supported a higher wild-type stability (39), which in turn enabled greater access to mutagenic energies. The energetic effects of the aromatic side chain variants are particularly interesting. Statistical analyses of MP structures show that aromatic side chains are frequently found at the interfacial regions of MPs, often forming an aromatic belt around the protein (2, 88). Mutagenesis of OmpA325 aromatic residues in this region of the protein indeed showed that interfacial tryptophan, tyrosine, and phenylalanine side chains contributed favorably to stability by 2, 2.6, and 1 kcal mol−1 , respectively (37). These values agree well with those observed in the interfacial hydrophobicity scale (95). www.annualreviews.org • Energetics of Membrane Protein Folding

diC16:1c9 PC: 1,2-dipalmitoleoyl-snglycero-3phosphocholine

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SDS: sodium dodecylsulfate


diC12 PC: dilauroylphosphatidylcholine

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However, because aromatic side chains are less common in the central hydrophobic (lipidfacing) regions of MPs, one might expect that they would confer less stability in those environments than at the interface. Surprisingly, a subsequent study using OmpA325 showed no depth dependence when the energies were extrapolated to the absence of denaturant (38). One explanation for these unexpected results may be inconsistencies in the results obtained using different methods to demonstrate the two-state behavior: OmpA325 unfolding is incomplete when assayed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and the urea dependence of the folded fraction does not overlay for the SDS-PAGE and fluorescence data (38). These issues raise a concern about path independence in these titrations and indicate that the two-state analysis of the data employed may not yield accurate free energy values (38). A similar investigation suffers from a related problem with nonoverlap between SDS-PAGE forward and reverse titrations and different urea midpoints for SDS-PAGE and fluorescence titrations (79). The issue of whether the energetics of aromatic side chain insertion will recapitulate frequencies of observation merits further investigation.

PagP It was not until 2010, six years after Hong & Tamm’s (40) work was published, that Huysmans et al. (44) published a thermodynamic investigation of a second E. coli β-barrel: PagP. The experimental setup was quite different in this study in that these authors used equilibrium large unilamellar vesicles (LUVs). In addition, unfolded PagP displays a much stronger tendency to aggregate (22), so the PagP unfolded state needed to be solubilized in GdnHCl before the folding and unfolding titrations were measured using urea as the denaturant. PagP folding displayed a two-state transition for which G◦F,Water was −14.4 kcal mol−1 in diC12 PC LUVs at pH 8.0 (44). Although I do not discuss it here, Huysmans et al. (44) further used amino acid variants of PagP to conduct a phi value analysis that defined the transition state for PagP folding. This state was found to be a partially folded β-barrel tilted at the membrane (44). Three years later, Moon et al. (67) published an independent value for the PagP thermodynamic stability; they reported a value of −24.4 kcal mol−1 in the same lipid environment. Although this value favors the folded state by 10 kcal mol−1 more than the previous report, important differences between the experimental conditions can reconcile the apparent discrepancy. One distinction relates to the polypeptide sequence. Huysmans et al. (44) used a histidine-tagged PagP, whereas only the mature protein sequence was used in the Moon et al. study (67). A second difference was the pH: Huysmans et al. employed a neutral pH, whereas Moon et al. used a low pH. In addition, although soluble proteins are known to display a pH dependence on stability, it is more likely that the denaturant employed was the primary source of the different folding energies between the two reports. The first experiment used urea, whereas Moon et al. used GdnHCl to titrate PagP. For the linear extrapolation theory to apply, the extrapolated stability should be independent of the denaturant employed. However, this denaturant independence also assumes that the reaction end points are the same. Herein lies the difference between the two PagP studies. Urea denaturation resulted in a denatured state ensemble that was adsorbed to the diC12 PC membrane surface, whereas titration with GdnHCl, which is a stronger denaturant, promoted the population of an aqueous denatured state ensemble existing free in solution (unbound from membranes). One can easily imagine that these two denatured states could have quite distinct free energies, and it is a simple exercise to estimate the free energy change for membrane surface partitioning by the PagP polypeptide sequence using the PC membrane interface scale (95). Using this scale, the water-to-membrane surface partitioning of PagP was estimated to be favorable by 10 kcal mol−1 . This number exactly accounts for the 242

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discrepancies in the stabilities published by the two groups. Furthermore, these results highlight the importance of understanding the conformation(s) of the unfolded state(s) whose populations are promoted by denaturant. A molecular interpretation of any energetic measurements will depend strongly on the conformations present at the titration end points.

In the three years following publication of the first PagP stability, Moon & Fleming (65) and Moon et al. (67) published the thermodynamic stabilities for OmpLA, OmpW, and PagP (discussed above) (65, 67). Similar to the study conducted by Huysmans et al. (66), equilibrium LUVs composed of diC12 PC were used as the lipid geometry; however, after an extensive search for experimental conditions that would not show hysteresis (summarized in an entire publication), GdnHCl was employed as the denaturant, and a pH of 3.8 was required for thermodynamic path independence in these studies. The titration data for OmpLA presented an additional challenge by displaying three-state equilibrium behavior. This simply means that an equilibrium intermediate is populated along the titration from folded to unfolded, i.e., F↔I↔U. Typical data for wild-type OmpLA, a destabilizing OmpLA variant (A210R), and a stabilizing OmpLA variant (A210F) are shown in Figure 3. The two transitions are most prominent in the A210R data because the first transition (at the lower GdnHCl concentration) was strongly affected by the sequence change in this variant; this response was typical for OmpLA sequence changes to polar or ionizable groups. In contrast, mutations to nonpolar residues did not affect the first transition, but they shifted the second transition to higher

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concentrations of GdnHCl. A three-state transition requires a more complex linear extrapolation equation with a total of 10 fitting parameters. Even by collecting data every tenth molar, obtaining stability values required Moon & Fleming (65) to globally fit the data for the wild-type OmpLA and 19 variants using a strategy in which the m value for each transition was considered a global variable whose value was shared by all variants. Although they observed two-state folding, Huysmans et al. (44) used a similar fitting strategy because baselines for some of their variants were not fully accessible. The molecular nature of the OmpLA equilibrium intermediate has not been fully characterized. Because the nonpolar variants affect the second transition more strongly than the first, the authors speculated that this transition represents the reaction by which the protein exits the membrane and enters the aqueous phase. In this scenario, the first transition, which was more prominent among variants containing polar and ionizable groups, would represent a “loosening” of the structure within the membrane or an unfolding of the loop regions, an idea proposed by Haltia & Freire (32). The observation that the wavelength of maximum fluorescence emission experienced a greater red shift in the second transition than did the first is consistent with these speculations. Moon & Fleming (65) used OmpLA to develop a host–guest platform with the purpose of measuring a novel side chain transfer free energy scale. They mutated OmpLA site Ala210, located at the center of the bilayer on the lipid-facing surface, to each of the natural amino acid side chains and measured the folding free energy. The Ala-X stability difference between the wild-type and each of the variant proteins, GAla→X , represents the water-to-bilayer transfer free energy for that side chain relative to alanine. This scale was the first of its type to measure side chain transfer free energies in the context of a native protein using a phospholipid bilayer. A nonpolar atomic solvation parameter (ASPNP ) favoring membrane insertion by −23 cal mol−1 A˚−2 was determined from the slope of GAla→X values for nonpolar side chains plotted as a function of their ASA in a Gly-X-Gly tripeptide (65). This value is remarkably similar to that previously observed by Chothia (12), as well as to the ASPNP derived from the Wimley–White octanol scale (94). ASPNP availability also enabled the Moon & Fleming (MF) scale to be normalized for the contribution to alanine, making this scale reference free (65). The MF scale correlates quite well with both the Wimley–White octanol scale (94) and the White–von Heijne central position biological scale (33). However, the magnitudes of the MF energies are nearly twice those found in the Wimley–White and biological scales, as indicated by the slopes in Figure 4. It may be expected that the MF energies would be greater than those associated with partitioning into water-saturated octanol because the latter has different relaxation properties than does the phospholipid bilayer. Water-saturated octanol forms reverse micelles and may be able to adopt conformations that stabilize side chains. However, a difference in the hydrophobic environment cannot rationalize the greater magnitude of the MF scale as compared with that of the biological scale because both scales use lipid bilayers. Moreover, although the biological scale has been criticized for being the result of a nonequilibrium measurement, it nevertheless correlates quite well to verifiable equilibrium data such as the MF scale and the Wimley–White octanol scale (31). Whatever the mechanism that allows transmembrane helices to partition between the translocon and the lipid bilayer, it nonetheless mimics a thermodynamic sorting process and is not driven by a Maxwell’s demon. From a biological perspective, the hydrophobic partitioning potential only needs to be strong enough to sort transmembrane helices into the bilayer. The system will have more pliability if the thermodynamic potential is softer than its maximum potential, possibly explaining why the magnitude of the biological scale is smaller than the true water-to-bilayer transfer free energies. Although the full MF scale was collected only at the central bilayer position, Moon & Fleming used a series of Ala-to-Arg mutations to further demonstrate that the OmpLA side chain transfer


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free energies showed a dependence on their depth of burial within the membrane (65). Unlike the biological scale, in which the depth-dependent partitioning values of all side chains were measured (34), the set of OmpLA mutations spoke to the question of how much it costs to introduce ionizable groups into the lipid bilayer environment. Previous molecular dynamics calculations showed that putting an Arg side chain in the lipid bilayer could cost as much as 14–17 kcal mol−1 at the central position; however, this cost was lower near the interface region (18, 61). Although the same extent of lipid burial could not be experimentally achieved, the MF experiments recapitulated a similar trend (65). The OmpLA platform further reconciles one obstacle between the computational and experimental fields in that previous molecular dynamics systems were not experimentally tractable and could not be tested. Although OmpLA embedded in a phospholipid bilayer is not the smallest computational setup, Fleming et al. (28) showed that this setup is certainly amenable to computational interrogation. Subsequent free energy perturbation calculations indeed agree with the experimentally observed Ala→Arg mutation energies (30).

Comparison of β-Barrel Stabilities Moon et al. (67) conducted several sequence–structure–energy comparisons of PagP, OmpW, and OmpLA. They were able to do so because the stability measurements were all taken under identical lipid and buffer conditions. They found that the total thermodynamic stability correlated well with the summed transfer free energy for side chains on the lipid-facing surfaces of the three proteins. In addition, they showed that after correction for the additional surface area shielded from water upon folding, the m values for PagP, OmpW, and OmpLA follow the same linear correlation with buried (water-)accessible surface area as that previously observed for water-soluble proteins (69). Because the hydrophobic thickness of the diC12 PC bilayer used in those experiments is well matched to that of these OMPs (28, 56, 57, 67, 73), the Moon et al. (39) m value correlation dovetails well with the m value dependence previously noted in the OmpA325 folding studies. www.annualreviews.org • Energetics of Membrane Protein Folding

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OmpLA

OmpW

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Figure 5 Structures of the Escherichia coli transmembrane β-barrels for which water-to-bilayer stabilities are available. The transmembrane β-barrels for OmpLA, OmpW, PagP, and OmpA were equilibrated in diC12 PC membranes using molecular dynamics calculations as described in References 28 and 67. The structural representation of the soluble domain of OmpA is a homology model as described in Reference 17. Its placement with respect to the OmpA171 transmembrane domain is arbitrary.

As the authors admitted, it is somewhat preliminary to draw general conclusions on sequence– structure–energy relationships from data on only three proteins. However, in those experiments, OmpA325 , OmpX, OmpT, and FadL all showed hysteresis for which no underlying physical basis could be identified (67). Moreover, the thermodynamic data for OmpA325 are difficult to add to the comparison because the observed folding free energy includes contributions from not only the protein, but also the lipid environment, and SUVs and LUVs must certainly impart different energetic influences. In addition, it is not obvious how to parse the folding free energy of the fulllength protein OmpA325 , as its architecture is distinct from that of PagP, OmpW, and OmpLA (Figure 5). OmpA325 is a 325-residue, two-domain protein, of which half the mass comprises a soluble periplasmic domain. Structural studies demonstrate that residues 1–171 (OmpA171 ) constitute the transmembrane β-barrel; this domain is followed by a short linker and a folded periplasmic domain (OmpAPer ). The five native tryptophan residues used as the spectral probe in the thermodynamic studies are all found within OmpA171 , and it is unclear whether or not tryptophan fluorescence changes in these studies contain contributions from the whole protein or just the transmembrane region. Even though these studies show OmpA325 folding to be thermodynamically two state, Danoff & Fleming (17) have shown that OmpAPer can be independently expressed and folded with a folding stability of −6.2 kcal mol−1 . The transition midpoint of OmpAPer occurs at 4.3 M urea, which differs from that observed in the OmpA325 studies (39). Moreover, attempts have been made to fold only the barrel, OmpA171 , but, unfortunately, these experiments show hysteresis (79).

INTRAMEMBRANOUS UNFOLDED (IM U) TO FOLDED α-HELICAL MEMBRANE PROTEIN STABILITIES Interrogation of the full U→F folding reaction has not yet been possible for α-helical MPs, presumably because these sequences are insoluble in water. This reality prevents direct measurement of some important thermodynamic parameters (such as side chain transfer free energies) using 246

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α-helical MP sequences. However, defining the nonnative state as a membrane-embedded one is superior for addressing biophysical questions related to the energetics of lateral interactions between transmembrane helices because these reactions can sometimes be isolated from the energetics of water-to-bilayer insertion. Accordingly, the energies associated with these measurements are quite different from water-to-bilayer folding measurements. To distinguish this membraneembedded nonnative conformation from the classical, water-soluble unfolded state, I refer to it as the intramembranous unfolded (IM U) state. From a biological perspective one can argue that the IM U state is a more relevant conformation for rationalizing MP stability mutations in the cell. In contrast to the nascent chain of a transmembrane β-barrel, which is synthesized completely in the cytoplasm and translocated into the periplasm posttranslationally, the nascent chain for an α-helical protein is biosynthesized within a controlled environment (the translocon), transferred directly into the membrane, and never released into the aqueous cytoplasmic environment. Most in vitro experiments populate the IM U state using a mixed micelle system in which one detergent favors the native conformation and a second, denaturing detergent favors the IM U state. The groups of Booth, Bowie, Park, and Sanders have all used this strategy to probe the partial unfolding of bR, diacylglycerol kinase (DGK), or PMP22.

bO: bacterioopsin

Bacteriorhodopsin Bacteriorhodopsin (bR) is a light-driven proton pump found in the purple membranes of Halobacterium salinarum. It has been the workhorse for stability measurements of α-helical membrane proteins. The bR polypeptide bacterioopsin (bO) folds into a conformation of seven transmembrane α-helices, and the holoprotein contains a covalently bound retinal chromophore with absorbance properties that are exquisitely sensitive to the conformational state of the protein. Over 30 years ago, Khorana’s group (42) showed that the completely unfolded bO polypeptide could be refolded to its native state and could bind retinal without the addition of an external energy source, thus demonstrating that the Anfinsen hypothesis applied to transmembrane proteins. Engelman’s group (43) used synthetic transmembrane helices corresponding to each of the bR helices to demonstrate that a single α-helix represented a thermodynamically independent folding unit. Kahn et al. (48) separately expressed segments of bR in cells, demonstrating that transmembrane helix–helix interactions (and not the connecting loops) could specify the native bR structure. These observations confirmed the principal tenets of the “two-stage” hypothesis, in which independently stable transmembrane helices are first established and then interact laterally to form the bundle of α-helices that defines the native structure (74). Despite the fact that newly solved MP structures are displaying increased and unanticipated structural complexity, the two-stage model is still an extremely useful and widely used thermodynamic construct employed to understand the energetic basis of α-helical membrane proteins. Almost 20 years after Khorana’s landmark paper (42), Chen & Gouaux (11) demonstrated that bR could reversibly fold and unfold to a membrane-embedded unfolded state in DMPC/CHAPSO micelles using SDS as the denaturing detergent. Curiously, they did not report a stability estimate from these experiments. Bowie and coworkers (23) used the bR platform to address the energetic consequences of introducing alanine mutations along helix B. For side chains that pointed towards the interior of the bR helix bundle, Faham & Bowie (23) found a linear correlation of 38 A˚2 kcal−1 mol−1 between the buried nonpolar accessible surface area and the energetic cost. This value agreed well with results previously found in a similar mutagenesis study of the glycophorin A helix–helix dimer interface (26). This van der Waals interaction parameter is also remarkably similar to a packing energy observed for the interiors of soluble proteins (46). The fact that bR mutants crystallize enabled the solution of bR X-ray structures confirming the structural consequences of mutation. www.annualreviews.org • Energetics of Membrane Protein Folding

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This same bR mutagenesis study stimulated insight on helix kink energetics and their evolution. Because of its unique backbone bonding, a proline side chain induces kinks in α-helical secondary structures. Surprisingly, a proline-to-alanine mutation at bR position 50, a kink site in the helix, had no effect on stability; remarkably, the crystal structure showed that the helix stayed kinked, even with the alanine mutation (23). This led to the realization that tertiary interactions—not prolines— stabilize helix kinks in MPs. Yohannan et al. (96) proposed an evolutionary hypothesis for kink formation in which kinks initially formed in ancient sequences following a proline mutation. The subsequent helix distortions were accommodated by compensatory sequence changes in the surrounding residues, and over time, the proline was no longer required. Predictions of kink patterns in G protein–coupled receptors suggest structural diversity (96). Joh et al. (45) further used bR as a platform for double-mutant cycle analysis that interrogated hydrogen bond energetics within a membrane protein. Theoretical considerations suggest that hydrogen bonds should be strong within a membrane because of the apolar environment and the lack of competition from water. Yet, mutations of hydrogen bonding partners in several different membrane proteins had suggested that the hydrogen bond energies were surprisingly modest (1, 23, 27, 29, 54, 85). Because the coupling energy can be more accurately evaluated in the context of a double-mutant cycle analysis, this strategy was used to interrogate hydrogen bond strength for eight different hydrogen bonding pairs throughout bR. These, too, revealed that a hydrogen bond contributed on average only 0.6 kcal mol−1 towards stability (45). Booth and coworkers (3, 7–9, 13–16, 49, 59) have investigated both the folding and unfolding kinetics of bR, as well as its thermodynamic stability. In particular, the IM U→F folding free energy for bR was found to be −20 kcal mol−1 in DMPC/CHAPS micelles from both kinetic and equilibrium folding experiments (14). Subsequently, Schlebach et al. (83) discovered that the bR folding rate constant depended on the total molar concentration of the folding lipid/detergent (DMPC/CHAPS) solution and could be significantly slower than previously reported allowing hydrolysis of the Schiff base during the refolding reaction. The unexpected result that the unfolding rate constant did not display these dependencies means that the bR thermodynamic stability depends on the total molar concentrations of DMPC and CHAPSO, which were not fully controlled in the previous study (14). Curiously, the results of experiments that kept the molar concentrations of DMPC/CHAPSO constant but lowered the bR concentration indicated that the folding rate dependence was not related to dilution of bR within the micellar phase. Thus, the physical origins of this dependence remain uncertain. In sum, these surprising findings caution that the linear dependence of the unfolding detergent and the total molar concentration of the folding detergent are parameters whose concentration effects should be investigated. To resolve this issue, Park and coworkers (10) redefined the folding reaction from one between IM U-bR↔bRF , in which the retinal is bound to both states, to IM U-bO↔bRF , in which the unfolded state lacks covalently bound retinal. This required both the addition of excess retinal to drive the folding reaction and longer incubation periods, but the new reaction scheme showed path independence. Fortuitously, this change had no effect on the previously published energetic costs attributable to amino acid changes, indicating that the mutagenic effects were mainly manifested on the folded state, as postulated in the earlier studies. A thermodynamic comparison of the total stability between this study (10) and its predecessor (83) is not possible because an extrapolated G◦F,Water value was not reported for the IM U-bO↔bRF reaction. One lingering criticism of the bR folding data concerns the structural interpretation of the IM U state. Circular dichroism (CD) spectra suggest that IM U bR possesses approximately 50% of the α-helical intensity of the native state (58). However, no one has determined whether this 50% corresponds to the helix content at locations found in the folded state or whether regions that are not natively helical form helices in the SDS-induced IM U state. Moreover, on the basis of NMR

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and CD studies of peptides and transmembrane regions of MPs, Renthal (75) has questioned the reliability of CD as a measure of secondary structure in SDS solutions (60, 62, 64, 75–77, 87, 90). Because bR is so amenable to thermodynamic and kinetic interrogation, more definitive structural data on the IM U-bO conformation would be invaluable for interpreting its energetics. Even so, this information gap is not tragic because uncertainties about unfolded state conformations have not hindered many advances in the soluble protein field.

DPC: ndodecylphosphocholine LS: n-lauroylsarcosine

Diacylglycerol Kinase (DGK)


TDPC: tetradecylphosphocholine

Diacylglycerol kinase (DGK) is a trimeric transmembrane enzyme that functions to produce phosphatidic acid and adenosine diphosphate (ADP) in E. coli. Both NMR and X-ray crystal structures show that each subunit of this protein contains three α-helical segments and two amphipathic helices on the membrane surface (53, 89). Experiments on DGK were the first to demonstrate that the free energy of folding for a membrane protein in a micellar environment depended linearly on the SDS mole fraction (52). As is the case for the empirically observed linear dependence of stability on denaturant concentration for soluble proteins, the physical basis for such a dependence is not well understood. Nevertheless, this dependence allows for extrapolation of the folding free energy. DGK displayed two distinct spectral signals upon unfolding by SDS. These signals could be assigned independently to either transmembrane or surface regions, and they displayed G◦F,Water values of −16 and −6 kcal mol−1 , respectively, in n-decyl β-D-maltoside (DM) micelles (52). The transmembrane stability is particularly interesting in its magnitude because it may predominantly reflect disruption of helix–helix interactions. When denatured in SDS micelles, the DGK IM U conformation displays 85% of the native-like CD intensity at 222 nm, suggesting that a loss of secondary structure is not the main structural transition.

Peripheral Myelin Protein 22 (PMP22) Peripheral myelin protein 22 (PMP22) is a 160-residue eukaryotic MP involved in myelin formation (4, 71). In contrast to studies of bR and DGK, PMP22 folding studies are particularly convenient because of the availability of structural information about both the folded and unfolded conformations of this protein. NMR studies in n-tetradecylphosphocholine (TDPC) micelles show that PMP22 comprises four transmembrane α-helices (78). However, even when folded, transmembrane helix 1 is only transiently packed against the α-helical bundle formed by transmembrane helices 2, 3, and 4. The PMP22 helix packing was further probed using near-UV CD, which measures the chiral structural arrangement of aromatic residues. These data showed that the helix–helix interactions depend on pH, temperature, and the detergent micelle environment, whereas far-UV CD data confirm that the helix content appears to be unperturbed (63). This information was used to design a thermodynamic assay in which PMP22 in n-dodecylphosphocholine (DPC) micelles (a folding environment) was titrated with nlauroylsarcosine (LS), a denaturing detergent. In preliminary experiments, PMP22 did not appear to be fully folded and displayed only a partial unfolding transition, consistent with the NMR data (68). However, the addition of 15% glycerol conferred stability to the protein, allowing a full F↔U reaction to be observed. Notably, even though the far-UV CD spectra confirmed that helix content was retained, the near-UV CD spectral probe of the aromatic region revealed an IM U conformation in which helix–helix interactions were disrupted. The folding free energy of this transition was found to be −1.5 kcal mol−1 . The modest stability of PMP22 raises the question of how relevant these types of in vitro studies are to the biological situation. In fact, both the molten globule nature of PMP structure and its www.annualreviews.org • Energetics of Membrane Protein Folding

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modest stability are quite consistent with its behavior in cells. Cellular studies showed that only 20% of PMP22 synthesized in a cell actually folds into the native conformation; the remaining 80% is degraded. Assigning all of the folding inefficiency to thermodynamic origins (which may or may not be the case), this ratio of folded to unfolded PMP22 represents an unfavorable cellular free energy of folding equal to +0.9 kcal mol−1 at 37◦ C. Even if the environment of the native cellular membrane imparts a couple of kcal worth of stability, PMP22 is clearly a metastable protein both in vivo and in vitro. Sanders and coworkers (63, 68) speculate that the IM U structural state may represent a latestage folding intermediate. Consistent with this idea are the structural consequences of mutations in PMP22 that lead to heritable neuropathies. The PMP22 G150D and L16P “Trembler” mutants lack ordered tertiary regions and show slower folding kinetics (68, 78). As Myers & Sanders point out in their 2004 review (80), either of these effects could lead to impaired folding in the cell, which would cause the ER quality control system to target these IM U conformations for degradation. In this manner, the PMP22 biophysical investigations serve as a model for the types of modest folding or misfolding events that may cause membrane proteins to be inefficiently folded in the cell.


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Glycophorin A as a Model for Transmembrane Helix–Helix Dimerization Although this review focuses on the energetics of full-length membrane proteins, some advances in understanding transmembrane helix–helix interactions using model systems merit a short discussion. Lateral (intramembrane) dimer formation mediated by transmembrane α-helices has long served as one of the simplest models to investigate the energetics of helix–helix interactions. One of the most thoroughly investigated reactions is that of the glycophorin A transmembrane domain (GpATM). In the late 1990s, NMR studies of GpATM revealed a symmetric dimeric structure in micelles. The helix–helix interface observed in these studies agreed quite well with the extensive mutagenesis studies used to predict it. The GpATM dimer has been a particularly useful model system for developing experimental methods to measure stability. Fleming and coworkers (19, 20, 25–27, 50) used GpATM to find sedimentation equilibrium (SE) conditions in detergent micelles that allowed a quantitative measure of the energetic consequences of mutations at the GpATM dimer interface. Over 50 variants were investigated, and their interaction energetics ranged from forced cohabitation in micelles to strong preferential interactions. Double-mutant cycle analysis showed thermodynamic coupling between the ends of the helix, and it was discovered that mutations at sites away from the GxxxG helix could cost as much energy as Gly-to-Ala mutations within the motif. Fluorescence resonance energy transfer (FRET) assays for transmembrane helix interactions were also developed using the GpATM dimer (24). Both FRET and SE have subsequently been applied to interrogate a wide variety of helix–helix interactions, including transmembrane domains of growth factor receptors (21, 50, 54, 84), integrins (55), the M2 proton channel (41, 51), and designed transmembrane helix dimers (29). Although FRET requires one to assume the reaction order because it does not allow a direct measurement of molecular weight (as in sedimentation experiments), FRET experiments have the advantage that they can be conducted in lipid vesicles, which arguably provide a more native-like environment. More recently, Bowie (35, 36) used the GpATM sequence to vet a novel and creative steric trapping approach that measures transmembrane helix interaction affinities in lipid bilayers. The brilliance of steric trapping is that it does not require MP dilution, which can be difficult to accomplish within the limited volume of a lipid vesicle. The method takes advantage of a coupled equilibrium between the association and dissociation of biotinylated transmembrane helices, along 250

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with the competitive binding of monovalent streptavidin (mSA). Using steric trapping, Hong & Bowie (36) showed that the GpATM dimer could interact over an incredible 8–kcal mol−1 range (from −12 to −3 kcal mol−1 ). This range was modulated by the electrostatic potential and elastic stiffness of the membrane as well as by the presence of other nonspecific MPs at concentrations found in E. coli plasma membranes.


CONCLUSIONS The recent availability of MP folding free energies shows unequivocally that these proteins lie at a free energy minimum. Given the success in overcoming the experimental challenges to date, researchers have many reasons to be optimistic about even more advances in membrane protein energetics in the near future. Moreover, although this review focused on protein measurements, experimental results increasingly demonstrate the role played by the lipid bilayer in modulating the energetics of the system. The membrane is not a static hydrophobic slab imposing its structure upon a membrane protein; rather, the two work in concert with the aqueous medium to minimize the free energy of the system. This cooperation increases the structural, functional, and energetic pliability of transmembrane proteins.

DISCLOSURE STATEMENT The author is not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS The author gratefully acknowledges support from the National Science Foundation (MCB 0919868) and the National Institutes of Health (GM R01 GM 07990).

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Contents

Annual Review of Biophysics Volume 43, 2014


Fun and Games in Berkeley: The Early Years (1956–2013) Ignacio Tinoco Jr. p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1 Reconstructing Folding Energy Landscapes by Single-Molecule Force Spectroscopy Michael T. Woodside and Steven M. Block p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p19 Mechanisms Underlying Nucleosome Positioning In Vivo Amanda L. Hughes and Oliver J. Rando p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p41 Microfluidics Expanding the Frontiers of Microbial Ecology Roberto Rusconi, Melissa Garren, and Roman Stocker p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p65 Bacterial Multidrug Efflux Transporters Jared A. Delmar, Chih-Chia Su, and Edward W. Yu p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p93 Mechanisms of Cellular Proteostasis: Insights from Single-Molecule Approaches Carlos J. Bustamante, Christian M. Kaiser, Rodrigo A. Maillard, Daniel H. Goldman, and Christian A.M. Wilson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 119 Biophysical Challenges to Axonal Transport: Motor-Cargo Deficiencies and Neurodegeneration Sandra E. Encalada and Lawrence S.B. Goldstein p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 141 Live Cell NMR Darón I. Freedberg and Philipp Selenko p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 171 Structural Bioinformatics of the Interactome Donald Petrey and Barry Honig p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 193 Bringing Bioelectricity to Light Adam E. Cohen and Veena Venkatachalam p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 211 Energetics of Membrane Protein Folding Karen G. Fleming p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 233 The Fanconi Anemia DNA Repair Pathway: Structural and Functional Insights into a Complex Disorder Helen Walden and Andrew J. Deans p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 257 vii

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˚ Angstr om-Precision Optical Traps and Applications ¨ Thomas T. Perkins p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 279 Photocontrollable Fluorescent Proteins for Superresolution Imaging Daria M. Shcherbakova, Prabuddha Sengupta, Jennifer Lippincott-Schwartz, and Vladislav V. Verkhusha p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 303 Itch Mechanisms and Circuits Liang Han and Xinzhong Dong p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 331 Structural and Functional Insights to Ubiquitin-Like Protein Conjugation Frederick C. Streich Jr. and Christopher D. Lima p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 357 Annu. Rev. Biophys. 2014.43:233-255. Downloaded from www.annualreviews.org by University of Texas - Austin on 06/09/14. For personal use only.

Fidelity of Cotranslational Protein Targeting by the Signal Recognition Particle Xin Zhang and Shu-ou Shan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 381 Metals in Protein–Protein Interfaces Woon Ju Song, Pamela A. Sontz, Xavier I. Ambroggio, and F. Akif Tezcan p p p p p p p p p p p 409 Computational Analysis of Conserved RNA Secondary Structure in Transcriptomes and Genomes Sean R. Eddy p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 433 Index Cumulative Index of Contributing Authors, Volumes 39–43 p p p p p p p p p p p p p p p p p p p p p p p p p p p 457 Errata An online log of corrections to Annual Review of Biophysics articles may be found at http://www.annualreviews.org/errata/biophys

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Annual Reviews It’s about time. Your time. It’s time well spent.

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Annual Review of Statistics and Its Application Volume 1 • Online January 2014 • http://statistics.annualreviews.org

Editor: Stephen E. Fienberg, Carnegie Mellon University


Associate Editors: Nancy Reid, University of Toronto Stephen M. Stigler, University of Chicago The Annual Review of Statistics and Its Application aims to inform statisticians and quantitative methodologists, as well as all scientists and users of statistics about major methodological advances and the computational tools that allow for their implementation. It will include developments in the field of statistics, including theoretical statistical underpinnings of new methodology, as well as developments in specific application domains such as biostatistics and bioinformatics, economics, machine learning, psychology, sociology, and aspects of the physical sciences.

Complimentary online access to the first volume will be available until January 2015. table of contents:

• What Is Statistics? Stephen E. Fienberg • A Systematic Statistical Approach to Evaluating Evidence from Observational Studies, David Madigan, Paul E. Stang, Jesse A. Berlin, Martijn Schuemie, J. Marc Overhage, Marc A. Suchard, Bill Dumouchel, Abraham G. Hartzema, Patrick B. Ryan

• High-Dimensional Statistics with a View Toward Applications in Biology, Peter Bühlmann, Markus Kalisch, Lukas Meier • Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data, Kenneth Lange, Jeanette C. Papp, Janet S. Sinsheimer, Eric M. Sobel

• The Role of Statistics in the Discovery of a Higgs Boson, David A. van Dyk

• Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond, Elena A. Erosheva, Ross L. Matsueda, Donatello Telesca

• Brain Imaging Analysis, F. DuBois Bowman

• Event History Analysis, Niels Keiding

• Statistics and Climate, Peter Guttorp

• Statistical Evaluation of Forensic DNA Profile Evidence, Christopher D. Steele, David J. Balding

• Climate Simulators and Climate Projections, Jonathan Rougier, Michael Goldstein • Probabilistic Forecasting, Tilmann Gneiting, Matthias Katzfuss • Bayesian Computational Tools, Christian P. Robert • Bayesian Computation Via Markov Chain Monte Carlo, Radu V. Craiu, Jeffrey S. Rosenthal • Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models, David M. Blei • Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues, Martin J. Wainwright

• Using League Table Rankings in Public Policy Formation: Statistical Issues, Harvey Goldstein • Statistical Ecology, Ruth King • Estimating the Number of Species in Microbial Diversity Studies, John Bunge, Amy Willis, Fiona Walsh • Dynamic Treatment Regimes, Bibhas Chakraborty, Susan A. Murphy • Statistics and Related Topics in Single-Molecule Biophysics, Hong Qian, S.C. Kou • Statistics and Quantitative Risk Management for Banking and Insurance, Paul Embrechts, Marius Hofert

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Membrane protein folding and stability.

Kinetics and thermodynamics of membrane protein folding.

Energetics of folding of a lysozyme beta-bend.

Relative domain folding and stability of a membrane transport protein.

Viroporins, Examples of the Two-Stage Membrane Protein Folding Model.

Energetics of folding and unfolding of pancreatic trypsin inhibitor.

Structure formation during translocon-unassisted co-translational membrane protein folding.

Membrane protein folding and oligomerization: the two-stage model.

Predictive energy landscapes for folding membrane protein assemblies.

Marginally hydrophobic transmembrane α-helices shaping membrane protein folding.

Protein-catalysed protein folding.

Mutational scanning reveals the determinants of protein insertion and association energetics in the plasma membrane.

The energetics of a three-state protein folding system probed by high-pressure relaxation dispersion NMR spectroscopy.

Pathway of protein folding.

Protein folding. Translational tuning optimizes nascent protein folding in cells.

Combining in Vitro Folding with Cell Free Protein Synthesis for Membrane Protein Expression.

Energetics of repacking a protein interior.

Concepts in protein folding.

Fast protein folding kinetics.

Biochemistry. Protein folding, interrupted.

Protein folding in vitro.

Protein folding absent selection.

Computer simulation of protein folding.

Protein folding and chaperonins.