Using Cooperatively Folded Peptides To Measure Interaction

Jul 19, 2017 - Published as part of the Accounts of Chemical Research special issue ... high dynamic range because the energies of interest are couple...
0 downloads 0 Views 2MB Size
Download PDF
Article pubs.acs.org/accounts

Using Cooperatively Folded Peptides To Measure Interaction Energies and Conformational Propensities Published as part of the Accounts of Chemical Research special issue “Chemical Biology of Peptides”. Maziar S. Ardejani,† Evan T. Powers,*,† and Jeffery W. Kelly*,†,‡,§ †

Department of Chemistry, The Scripps Research Institute, La Jolla, California 92037, United States Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California 92037, United States § The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, United States ‡

CONSPECTUS: The rates and equilibria of the folding of biopolymers are determined by the conformational preferences of the subunits that make up the sequence of the biopolymer and by the interactions that are formed in the folded state in aqueous solution. Because of the centrality of these processes to life, quantifying conformational propensities and interaction strengths is vitally important to understanding biology. In this Account, we describe our use of peptide model systems that fold cooperatively yet are small enough to be chemically synthesized to measure such quantities. The necessary measurements are made by perturbing an interaction or conformation of interest by mutation and measuring the difference between the folding free energies of the wild type (in which the interaction or conformation is undisturbed) and the mutant model peptides (in which the interaction has been eliminated or the conformational propensities modified). With the proper controls and provided that the peptide model system in question folds via a two-state process, these folding free energy differences can be accurate measures of interaction strengths or conformational propensities. This method has the advantage of having high sensitivity and high dynamic range because the energies of interest are coupled to folding free energies, which can be measured with precisions on the order of a few tenths of a kilocalorie by wellestablished biophysical methods, like chaotrope or thermal denaturation studies monitored by fluorescence or circular dichroism. In addition, because the model peptides can be chemically synthesized, the full arsenal of natural and unnatural amino acids can be used to tune perturbations to be as drastic or subtle as desired. This feature is particularly noteworthy because it enables the use of analytical tools developed for physical organic chemistry, especially linear free energy relationships, to decompose interaction energies into their component parts to obtain a deeper understanding of the forces that drive interactions in biopolymers. We have used this approach, primarily with the WW domain derived from the human Pin1 protein as our model system, to assess hydrogen bond strengths (especially those formed by backbone amides), the dependence of hydrogen bond strengths on the environment in which they form, β-turn propensities of both natural sequences and small molecule β-turn mimics, and the energetics of carbohydrate−protein interactions. In each case, the combination of synthetic accessibility, the ease of measuring folding energies, and the robustness of the structure of the Pin1 WW domain to mutation enabled us to obtain incisive measurements of quantities that have been challenging to measure by other methods.

1. INTRODUCTION

ments lie at the intersection of biophysics and physical organic chemistry, and methodology from both fields is required to make accurate measurements. Perhaps the conceptually most obvious way to measure interaction strengths between functional groups in biomolecules is to use simple model small molecules that interact intermolecularly in solution. Thus, our initial understanding of the contribution of the hydrophobic effect to protein folding originated from solubility studies of nonpolar solutes in water

Life requires the constant making and breaking of noncovalent interactions within proteins or between proteins and other macromolecules during folding/unfolding and association/ dissociation processes. The rates and equilibria of these processes hinge on the conformational propensities of the molecules involved and the strengths and multiplicities of the interactions that form in the folded or bound states. These are defined by the properties of the molecular entities involved and the medium in which the process takes place. The centrality of these processes to biology has led to an intense and longstanding interest in measuring their energetics. These measure© 2017 American Chemical Society

Received: April 21, 2017 Published: July 19, 2017 1875

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research and the thermodynamics of transfer of models of amino acid side chains between nonpolar and aqueous media.1−5 Similarly, the first estimates of the contribution of hydrogen bonds (Hbonds) to protein stability came from Schellman’s studies of aqueous urea solutions,6 which were later expanded and refined by Klotz and others.7,8 The difficulties associated with using small molecule models in solution to measure interaction strengths are that (1) detecting the product complexes can be difficult because small molecule association constants are usually small (Schellman calculated an association constant of 4.1 × 10−2 M−1 for the urea dimer6), requiring high concentrations of solutes to be used, which can in turn affect the properties of the solvent, and (2) the geometries of the interactions are unconstrained, leading to ambiguity in the assignment of a measured energy to a particular interaction mode. The conformational propensities of amino acids have been studied similarly in small model peptides.9−11 However, such peptides do not fold cooperatively. Instead, they exist as an ensemble of conformations with varying degrees of structure, which complicates the relationship between observables (e.g., the circular dichroism signal at 222 nm, which is proportional to helicity12) and conformational propensities.13 One way in which interaction strengths can be measured is through the use of “molecular balances”, small molecules that can exist in two (or more) states, one of which enforces an interaction of interest that is absent in the other conformation(s). The perturbation of the equilibrium by the interaction, typically measured by NMR, reflects the interaction’s strength.14−18 Molecular balances have also been used to measure helical propensities of amino acids via templateinduced helix initiation.19 They can sensitively measure weak interactions, and because they are synthetic, the interacting functional groups can be varied to probe the nuances of the interaction in question. However, strong interactions can be difficult to measure with molecular balances because the population of the noninteracting state can be difficult to detect and quantify. Moreover, the microenvironmental properties, especially the effective concentration, of functional groups in a small molecule in solution are likely to be different than they are in a folded protein. Another method to quantify intramolecular interactions and conformational propensities that does not suffer from these drawbacks is to perturb interactions that exist in cooperatively folded biomolecules, usually proteins, and use the resulting change in the folding free energy (ΔGf) as a measure of the strength of the interaction or the change in conformational propensity.20,21 For example, a serine-to-alanine mutation in a given protein will eliminate any H-bonds formed by the serine side chain hydroxyl. Then the difference between the folding free energies (ΔΔGf) of the serine-containing wild type protein and the alanine mutant is partly a measure of the strength of the H-bonds formed by the serine side chain. However, mutations can alter the local structure, leave unsatisfied H-bonding partners, introduce cavities into the protein core, etc. Thus, factors other than H-bond strength can contribute to ΔΔGf in the serine-to-alanine example. Fersht and co-workers have analyzed mutational effects on folding free energies22 and have shown that “double mutant cycles” can be an effective method to extract interaction energies from ΔΔGf measurements.22−24 In a double mutant cycle, two interacting residues (denoted X and Y in Figure 1) are mutated individually and as a pair. The differences in folding free energies between each mutant and

Figure 1. Schematic of the double mutant cycle for assessing interaction strengths (red dashed line) between side chains X and Y, where these side chains are mutated to residues that cause minimal structural perturbation. The black dashed lines represent the interaction of X and Y with the rest of the protein.

the wild type are then calculated, yielding three ΔΔGf values: one for each of the single mutants (ΔΔGf,X and ΔΔGf,Y) and one for the double mutant (ΔΔGf,XY). The contribution of the interaction of interest to the folding free energy can then generally be isolated by subtracting the sum of ΔΔGf,X and ΔΔGf,Y from ΔΔGf,XY (Figure 1).22 Protein folding free energies can be measured over a wide range of protein stabilities by thermal or chemical denaturation with precisions of tenths of kilocalories.25 Thus, interactions with a wide range of strengths can be characterized by analyzing the effects of mutations on folding energetics. However, with the exception of some elegant work using unnatural amino acid incorporation,26,27 the main drawback of mutational methods is that the ways in which interactions in proteins can be perturbed is limited by the side chains of the naturally occurring amino acids. A “middle ground” between small-molecule-based molecular balances and biopolymer-based mutational methods for measuring interaction strengths and conformational propensities is to use mutational methods in peptides that fold (or selfassemble28) cooperatively to a well-defined structure, yet are small enough to be chemically synthesized. This approach enables the measurement of quantities of interest via folding free energy perturbations while retaining the flexibility to introduce such perturbations without being limited by ribosomal protein biosynthesis. In this Account, we describe our use of this approach to probe conformational propensities and interaction strengths.

2. COOPERATIVELY FOLDED PEPTIDE MODEL SYSTEMS A peptide must possess several features to be suitable for studying intramolecular interactions and conformational propensities. Foremost, it and its mutants should fold and unfold cooperatively so that the folding free energy can be measured accurately. The unfolding transition of cooperatively folded peptides, analogous to a first order phase transition, happens in a narrow window of a thermodynamic variable (e.g., denaturant concentration or temperature). Two-state cooperative unfolding can be experimentally verified by, for example, ensuring that denaturation curves measured with different techniques (e.g., circular dichroism and fluorescence) are coincident. This rules out any potential contributions from intermediates to the folding free energy, allowing accurate quantification. The model peptide should also have a welldefined structure to facilitate a structure-based interpretation of 1876

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research

The destabilization energies of amide-to-ester mutations (which reflect backbone H-bond strengths) in Pin WW ranges from 1.4

the energetic contributions from individual residues. Furthermore, the peptide should be short enough to permit an easy chemical synthesis of the desired variants. However, since the cooperativity of folding is directly proportional to polypeptide length,29 the model peptide cannot be too short. The semiconserved WW domains, present in many proteins involved in regulatory or signaling processes, satisfy all these requirements. WW domains are 30 to 65 amino acids long and comprise three antiparallel β-strands. The most extensively characterized WW domain is that from the human peptidylprolyl cis/trans-isomerase Pin1 (Pin WW), for which extensive kinetic, thermodynamic, and structural data are available.30 Pin WW is an ideal miniprotein for structure−folding studies because it is an independently folding domain with adequate thermodynamic stability and fast folding kinetics.31−33 We have used the 34-residue Pin WW domain and, to a lesser extent, other small protein domains to study the energetics of backbone H-bonding, kinetics of β-sheet folding, turn formation propensities, and the thermodynamics of carbohydrate−aromatic interactions in N-glycosylated proteins.

3. ENERGETICS OF PROTEIN MAIN CHAIN H-BONDING BY BACKBONE MUTAGENESIS H-bonds, especially those formed between backbone amides, are among the most prevalent noncovalent interactions in proteins. The energetics of backbone H-bonding, however, has been difficult to study. This is mainly because traditional sitedirected mutagenesis cannot be used for this purpose. Pin WW, due to its amenability to chemical synthesis, provided us with a platform to experimentally scrutinize the nature of backbone amide H-bonds. Among the simplest backbone alterations that can be chemically introduced into a peptide structure are amide-toester and amide-to-olefin mutations. We have used perturbations caused by these mutations to assess the contextdependent contribution of backbone H-bonding to β-sheet and α-helix folding.34,35 The effect of an amide-to-ester mutation on protein folding is often deduced from the ΔΔGf values between the wild-type protein and amide-to-ester mutants. However, there are issues involved with the extraction of H-bond energies from amide-to-ester mutations that must be corrected in the thermodynamic analysis. Amide-to-ester mutations eliminate amide NH groups, which can leave amide carbonyls without H-bonding partners. This is especially thermodynamically unfavorable if the unsatisfied amide carbonyl is buried. Therefore, a correction inversely proportional to the accessible surface area of the amide should be implemented. Amide-to-ester mutations also can introduce Coulombic repulsion between the ester O (which replaces the amide NH) and neighboring amide carbonyl oxygens. Although this repulsion does not usually cause significant structural changes, energetic correction for this effect should be included in the thermodynamic analysis of amide-to-ester mutations. These corrections, however, usually are not large enough to change the qualitative interpretation of the data. For example, by comparing the ΔΔGf of the amide-to-ester and the amideto-olefin mutants in a β-sheet context, taking into account the transfer free energy differences, we estimated that the O−O repulsion term is only +0.3 kcal/mol.36 We used double mutant cycle analysis, employing a combination of amide-to-ester backbone and traditional side chain mutagenesis, to further understand the effect of microenvironment polarity on backbone H-bond strength.

Figure 2. Strength of backbone H-bonding is context dependent in Pin WW. (a) The color-coding of the perturbed H-bonds depicts the experimentally quantified destabilization effect of amide-to-ester mutations. The adjusted ΔΔGf from ref 34 is used. Large, positive ΔΔGf values indicate strong H-bonds. (b) Histogram of ΔΔGf/b (ΔΔG of folding or binding) values from mutants in which backbone−backbone H-bonds were perturbed by amide-to-ester mutations.37

to 4.9 kcal/mol (Figure 2a), which is in agreement with results in other proteins (Figure 2b) and comparable to the strength of side chain H-bonds.37 The protein is most destabilized by perturbation of backbone H-bonds that are surrounded by hydrophobic side chains.38 Further studies confirmed that Hbonds could be more favorable by up to −1.2 kcal/mol when they are buried in a hydrophobic environment compared to when they are solvent exposed.39 Because N−H···O H-bonds are largely driven by electrostatic and polarization effects, which are sensitive to the local electric field and the dielectric 1877

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research

Figure 3. A β-turn mimic that strongly nucleates folding of Pin WW loop 1. (a) Structure of the dipeptide β-turn mimic. (b) Comparison of the structure of Pin WW with a type 1′ β-turn (gray) or the olefin β-turn mimic in loop1 (pink). Only side chains in loop 1 are shown for clarity.

that it matches the right-handed twist of the Pin WW β-sheet (Figure 3b).43

constant, the context-dependence of H-bonds in proteins is not unexpected.40 We also measured the folding kinetics of amide-to-ester mutants to examine the formation of secondary structure during the folding of the Pin WW domain. We found that the H-bonds of loop 1 amides are almost fully formed in the folding transition state and that this loop assumes a native-like secondary structure in the transition state.30,38 These results also showed that the β-strands and loop 2 are only partly structured in the folding transition state. It should be noted that, since the backbone H-bonds in loop 1 are among the weakest H-bonds in Pin WW in the ground state or in the transition state, factors other than H-bond strength, such as conformational propensity and side chain interactions, likely drive structure formation in the transition state.

5. PARSING THE CONTRIBUTIONS TO CARBOHYDRATE−AROMATIC INTERACTIONS: CH−π INTERACTIONS AND THE HYDROPHOBIC EFFECT IN ENHANCED AROMATIC SEQUONS N-Glycosylation, glycosylation on the amide side chain of asparagine, can enhance both the thermodynamic and kinetic stability of glycoproteins. It has been speculated that N-glycans diminish the local conformational fluctuations in the unfolded state of proteins to which they are attached through their bulk and the excluded volume effect. If so, then this mechanism of stabilization would have minimal dependence on the site of glycosylation. Using Pin WW to explore the energetic effect of N-glycosylation on protein stability, we found instead that the effect of N-glycosylation was context dependent. Incorporation of N-linked N-acetylglucosamine (GlcNAc) at various positions of Pin WW destabilized the native state unless the glycan was installed in certain positions of loop 1 and 2.44 N-Glycans in Phei−2-Xxxi−1-Asn(glycan)i-Glyi+1-Thri+2 sequences that are in a type 1 β-bulge turn conformation can reliably stabilize proteins.45,46 The stabilizing effect of this structural module, which we termed the enhanced aromatic sequon (EAS), is largely due to the interaction of the first GlcNAc on Asni with the side chain of Phei−2 (Figure 4).46 We hypothesized that other reverse turns, such as those in Pin WW loop 1, could host such stabilizing interactions by positioning Phe and Asn(GlcNAc) differently (cf. Figure 4a,b). We observed significant, yet different, levels of native state stabilization when loop 1 (residues 16−21) of Pin WW was converted to EASs with five (Phe-Ala-Asn-Gly-Thr) or six (Phe-Arg-Ser-Asn-Gly-Thr) residue loops comprising Asn Nglycosylated with GlcNAc.46−48 In these EASs, thermodynamic cycle analysis indicated that most of the glycosylationassociated stabilization arises from interactions between the Phe aromatic ring and the GlcNAc.47 (A four-residue loop based on a type 1′ β-turn (Phe-Asn-Gly-Thr) was also stabilized by N-glycosylation, but the effect did not appear to be mediated by direct interaction between GlcNAc and Phe.47) The solution NMR structures of the glycosylated variants confirmed that the EAS facilitates face-to-face monosaccharide−aromatic interactions (Figure 4).48 Taken together, these factors suggested that enhanced aromatic sequons within Pin WW could be used to experimentally parse the energetics of intramolecular carbohydrate−aromatic interactions in aqueous solution. Since Pin WW is amenable to chemical synthesis, a wide variety of unnatural amino acids can be incorporated at the

4. CONFORMATIONAL PROPENSITIES OF NATURAL AND UNNATURAL β-SHEET FOLDING NUCLEATORS To study the conformational propensities of β-turn-containing loops, loop 1 of Pin WW can be replaced with a variety of naturally occurring and rationally designed turns. Side-chain and backbone mutagenesis of Pin WW in combination with laser temperature-jump relaxation experiments determined that the rate-determining step in the folding of Pin WW is the nucleation of loop 1, a six-residue loop harboring an internal type 2 β-turn.30,32,38 Many sequences have much higher turn propensities, potentially accelerating Pin WW folding. We replaced the wild-type loop 1 with shorter sequences that either have a high propensity to fold into a type 1′ β-turn conformation or a type 1 β-turn with a G1 β-bulge (hereafter referred to as a type 1 β-bulge turn), the latter being common in other WW domains.41 These substitutions accelerate Pin WW folding by almost an order of magnitude and increase its thermodynamic stability. However, structural, thermodynamic, kinetic, and ligand-binding studies on loop 1 Pin WW variants indicate that folding rate is not the only selection pressure operating on WW domains. Functional factors have contributed to the evolution of this region of the protein as well.41 Conformational propensities of unnatural β-turn mimics can also be evaluated by placing them in loop 1 of Pin WW. We have shown that loop 1 can be replaced with a number of small molecule β-turn mimics (e.g., the dipeptide isostere shown in Figure 3a).42,43 Some of these β-turn mimics maintain the thermodynamic stability but modulate the folding rate of WW domain.42,43 The solution structure of the variant with an Eolefin-based β-turn mimic revealed that this turn mimic’s ability to strongly nucleate Pin WW folding is likely tied to the fact 1878

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research

of residues at the interactor position explains about 30% of total variation of ΔΔGglyc in the five-residue EAS series (Figure 5a),

Figure 4. Structures of enhanced aromatic sequons in loop 1 of Pin WW. (a) The N-linked GlcNAc packs tightly against Phei−2 and Thri+2 in a five-residue enhanced aromatic sequon. (b) The N-linked GlcNAc packs similarly against Phei−3 and Thri+2 in a six-residue enhanced aromatic sequon. Only the H atoms at positions 5 and 6 of GlcNAc are shown for clarity.

aromatic site to perturb the carbohydrate−aromatic interaction. Mutation of the Phe in the five- and six-residue EASs to a series of natural and unnatural amino acids, including nonaromatic amino acids as well as electron-rich and -poor analogs of Phe, modulated the extent to which N-glycosylation of the EAS stabilized Pin WW. This stabilization was quantified by ΔΔGglyc = ΔGfold,glyc − ΔGfold,nonglyc, where ΔGfold,glyc and ΔGfold,nonglyc are the folding free energies of the N-glycosylated and nonglycosylated forms of a Pin WW variant, respectively. In a simplistic model, ΔΔGglyc can be decomposed into three energy components. The first component, ΔΔGint, describes the intrinsic stabilizing effect of glycosylating the Asn in the EAS. This energy component accounts for the conformational preference of glycosylated versus the nonglycosylated Asn and the interaction of glycan with parts of the protein outside the EAS. The second energy component, ΔΔGphob, describes the stabilization arising from the hydrophobic burial of the α-face of GlcNAc by the side chain in the i − 2 position of the fiveresidue EAS or the i − 3 position of the six-residue EAS (hereafter, we will refer to this as the “interactor position”). The transfer free energy from nonpolar media to water, ΔGtr, has been found to correlate with hydrophobic burial energies like ΔΔGphob.49−51 The correlation between ΔΔGglyc and ΔGtr

Figure 5. (a) ΔΔGglyc representing the interaction of GlcNAc with the residue in the interactor position of the five-residue EAS in Pin WW domain correlates weakly (solid line, R2 = 0.32) with the hydrophobicity (ΔGtr) of side chains in the interactor position. Hydrophobicity of nonaromatic residues (gray circles) shows a stronger correlation with ΔΔGglyc (dashed gray line) than that of aromatic residues (red circles and red dashed line). (b) ΔΔGglyc exhibits a strong correlation with the polarizabilities of side chains in the interactor position. Polarizabilities were calculated using the DFT method (B3LYP) as implemented in Gaussian 09.

but the correlation is much stronger between ΔΔGglyc and ΔGtr for nonaromatic residues than for aromatic residues (Figure 5a, dashed lines). Thus, hydrophobic burial is more determinative of ΔΔGglyc when there are nonaromatic amino acids in the interactor position. In other words, while the hydrophobic effect can be considered as a major contributor to the interaction between GlcNAc and nonaromatic residues in the interactor position, factors other than the hydrophobic effect must be contemplated in order to explain the stronger interaction between GlcNAc and aromatic side chains. The third contribution to ΔΔGglyc comes from CH−π interactions. The importance of this interaction is suggested by 1879

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research the solution structures of Pin WW with the five- and six-residue EASs, which show that H5 of GlcNAc and its neighboring axial hydrogens are positioned toward the polarizable π-electron cloud of the aromatic ring in the interactor position (Figure 4).48 In addition, CH−π interactions are thought to have substantial electrostatic components.52 However, we observed a negligible correlation between Hammett’s σm and σp values of the side chains and ΔΔGglyc.48 This result suggests that energy components arising from the partial charges or permanent dipoles (Coulombic, Keesom, and Debye interactions) at the interactor position do not contribute to the net interaction energy in this system. We note that this finding does not imply that CH−π interactions do not have electrostatic components. The “net interaction energy” is the difference between the energies of the state in which the GlcNAc and the aromatic in the interactor position are solvated and the state in which they are interacting with each other. Therefore, our result simply suggests that the electrostatic components of the CH−π interaction cancel the electrostatic components of the OH−π interaction when the aromatic in the interactor position is solvated. Another major contribution to CH−π interactions comes from London dispersion interactions, which are directly proportional to the polarizability of the interacting entities.53−55 Consistent with other computational and experimental observations,56 ΔΔGglyc correlates strongly (R2 = 0.72) with the polarizability of side chains at the interactor position (Figure 5b). Thus, most of the variation of ΔΔGglyc in this system is explained by the polarizability of the side chains at the interactor position. This observation is consistent with London dispersion interactions driving the CH−π interactions in this system rather than interactions due to static charge distributions, possibly because the α-face of GlcNAc is not very strongly electropositive.57 Varying the sugar attached to the Asn in the EAS while keeping the aromatic residue in the interactor position constant as Phe enabled us to evaluate how the glycan structure influences the carbohydrate−aromatic interaction energy in Pin WW glycovariants.58 Our thermodynamic and corresponding structural data show that the relative strengths of carbohydrate−aromatic interactions depend on the stereochemistry and identity of the substituents on the sugars’ pyranose rings. The interaction energy of the monosaccharides with the phenyl ring in the interactor position decreases in the following order: allose > mannose > GlcNAc, xylose > glucose > Nacetylgalactosamine > galactose > L-idose. Peracetylation of the monosaccharides uniformly increases the strength of these interactions, as has been observed previously59 but changes the order only slightly.58 The solution NMR structures of the Pin WW glycovariants show that their structures are not significantly altered by changes in the carbohydrate ring. However, the exocyclic hydroxymethyl group protruding from C5 of the pyranose rings of the hexoses populates both gauche− and gauche+ rotamers. In the NMR structures of the glycovariants, only one of the two diastereotopic hydrogens attached to C6 is pointed toward the Phe aromatic ring in the gauche+ rotamer while almost burying the OH group. In contrast, both of the C6 hydrogens are turned toward the aromatic ring in the gauche− rotamer. The more stabilized glycovariants (e.g., allose) have a higher propensity to populate the gauche− conformation than the less stabilized glycovariants (e.g., galactose). The very small

stabilizing effect of L-idose is explained by the unusually poor packing of L-idose against the Phe aromatic ring.58

6. CONCLUSIONS Peptides can be used as macromolecular hosts for measuring interaction energies and conformational propensities if they (1) are cooperative two-state folders, (2) have structures that are robust to mutational perturbation, and (3) are easily synthesized. Interaction energies or conformational propensities can then be measured in such peptides by introducing mutations that perturb an interaction (e.g., a H-bond) or a conformational propensity (e.g., β-turn propensity) and measuring the difference in the folding free energies of the wild-type and the mutant peptides using a variety of easily accessed biophysical methods. With the proper controls, this difference can provide an incisive measure of the quantity of interest, showing the power and utility of cooperatively folded peptides. Moreover, this methodology will become even more powerful as more unnatural amino acids become commercially available and peptide synthesis strategies continue to improve.

■

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Jeffery W. Kelly: 0000-0001-8943-3395 Notes

The authors declare no competing financial interest. Biographies Maziar S. Ardejani graduated from the Sharif University of Technology with a B.Sc. in Chemistry. He received his M.Sc. and Ph.D. from Nanyang Technological University. He has received postdoctoral training at King’s College London and currenly is a research associate at The Scripps Research Institute. His research revolves around protein folding and design. Evan T. Powers received his B.S. in chemistry from Cornell University (1992) and his Ph.D. in organic chemistry from the Massachusetts Institute of Technology (1999) under the mentorship of Daniel S. Kemp. He was then an NIH postdoctoral fellow at The Scripps Research Institute with Jeffery Kelly (1999−2000), where he is currently an Associate Professor of Chemistry. His research interests are in protein folding and protein homeostasis. Jeffery W. Kelly received his B.S. in chemistry from the State University of New York College at Fredonia and his Ph.D. in organic chemistry from the University of North Carolina. After receiving postdoctoral training at the Rockefeller University under the mentorship of E. Thomas Kaiser, he joined the faculty of the Chemistry Department of Texas A&M University. In 1997, he moved to the Scripps Research Institute, serving as Dean of Graduate Studies (2000−2008) and Vice President of Academic Affairs (2000−2006) and is currently Chair of the Department of Molecular Medicine. His research interests are in chemical biology, protein folding, and cellular protein homeostasis.

■

ACKNOWLEDGMENTS We thank the many people in our laboratory who have contributed to our cited work with cooperatively folded 1880

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research

(21) Shirley, B. A.; Stanssens, P.; Hahn, U.; Pace, C. N. Contribution of hydrogen bonding to the conformational stability of ribonuclease T1. Biochemistry 1992, 31, 725−732. (22) Fersht, A. R.; Matouschek, A.; Serrano, L. The folding of an enzyme. 1. Theory of protein engineering analysis of stability and pathway of protein folding. J. Mol. Biol. 1992, 224, 771−782. (23) Carter, P. J.; Winter, G.; Wilkinson, A. J.; Fersht, A. R. The use of double mutants to detect structural changes in the active site of the tyrosyl transfer RNA synthetase (Bacillus stearothermophilus). Cell 1984, 38, 835−840. (24) Serrano, L.; Bycroft, M.; Fersht, A. R. Aromatic-aromatic interactions and protein stability: Investigation by double-mutant cycles. J. Mol. Biol. 1991, 218, 465−475. (25) Maxwell, K. L.; Wildes, D.; Zarrine-Afsar, A.; De Los Rios, M. A.; Brown, A. G.; Friel, C. T.; Hedberg, L.; Horng, J. C.; Bona, D.; Miller, E. J.; Vallee-Belisle, A.; Main, E. R.; Bemporad, F.; Qiu, L.; Teilum, K.; Vu, N. D.; Edwards, A. M.; Ruczinski, I.; Poulsen, F. M.; Kragelund, B. B.; Michnick, S. W.; Chiti, F.; Bai, Y.; Hagen, S. J.; Serrano, L.; Oliveberg, M.; Raleigh, D. P.; Wittung-Stafshede, P.; Radford, S. E.; Jackson, S. E.; Sosnick, T. R.; Marqusee, S.; Davidson, A. R.; Plaxco, K. W. Protein folding: defining a ″standard″ set of experimental conditions and a preliminary kinetic data set of two-state proteins. Protein Sci. 2005, 14, 602−616. (26) Chapman, E.; Thorson, J. S.; Schultz, P. G. Mutational analysis of backbone hydrogen bonds in Staphylococcal nuclease. J. Am. Chem. Soc. 1997, 119, 7151−7152. (27) Torrice, M. M.; Bower, K. S.; Lester, H. A.; Dougherty, D. A. Probing the role of the cation-π interaction in the binding sites of GPCRs using unnatural amino acids. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 11919−11924. (28) O’Neil, K. T.; DeGrado, W. F. A thermodynamic scale for the helix-forming tendencies of the commonly occurring amino acids. Science 1990, 250, 646−651. (29) Kouza, M.; Li, M. S.; O’Brien, E. P.; Hu, C.-K.; Thirumalai, D. Effect of finite size on cooperativity and rates of protein folding. J. Phys. Chem. A 2006, 110, 671−676. (30) Dave, K.; Jäger, M.; Nguyen, H.; Kelly, J. W.; Gruebele, M. High-resolution mapping of the folding transition state of a WW domain. J. Mol. Biol. 2016, 428, 1617−1636. (31) Kowalski, J. A.; Liu, K.; Kelly, J. W. NMR solution structure of the isolated Apo Pin1 WW domain: Comparison to the X-ray crystal structures of Pin1. Biopolymers 2002, 63, 111−121. (32) Jäger, M.; Nguyen, H.; Crane, J. C.; Kelly, J. W.; Gruebele, M. The folding mechanism of a β-sheet: the WW domain. J. Mol. Biol. 2001, 311, 373−393. (33) Jäger, M.; Dendle, M.; Kelly, J. W. Sequence determinants of thermodynamic stability in a WW domainAn all-β-sheet protein. Protein Sci. 2009, 18, 1806−1813. (34) Deechongkit, S.; Dawson, P. E.; Kelly, J. W. Toward assessing the position-dependent contributions of backbone hydrogen bonding to β-sheet folding thermodynamics employing amide-to-ester perturbations. J. Am. Chem. Soc. 2004, 126, 16762−16771. (35) Gao, J.; Kelly, J. W. Toward quantification of protein backbone−backbone hydrogen bonding energies: An energetic analysis of an amide-to-ester mutation in an α-helix within a protein. Protein Sci. 2008, 17, 1096−1101. (36) Fu, Y.; Gao, J.; Bieschke, J.; Dendle, M. A.; Kelly, J. W. Amideto-E-olefin versus amide-to-ester backbone H-bond perturbations: Evaluating the O−O repulsion for extracting H-bond energies. J. Am. Chem. Soc. 2006, 128, 15948−15949. (37) Powers, E. T.; Deechongkit, S.; Kelly, J. W. Backbone− backbone H-bonds make context-dependent contributions to protein folding kinetics and thermodynamics: Lessons from amide-to-ester mutations. Adv. Protein Chem. 2005, 72, 39−78. (38) Deechongkit, S.; Nguyen, H.; Powers, E. T.; Dawson, P. E.; Gruebele, M.; Kelly, J. W. Context-dependent contributions of backbone hydrogen bonding to β-sheet folding energetics. Nature 2004, 430, 101−105.

peptides. This work was funded by Grant GM51105 from the National Institutes of Health.

■

REFERENCES

(1) Kauzmann, W. Some factors in the interpretation of protein denaturation. Adv. Protein Chem. 1959, 14, 1−63. (2) Tanford, C. Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J. Am. Chem. Soc. 1962, 84, 4240−4247. (3) Wolfenden, R.; Andersson, L.; Cullis, P. M.; Southgate, C. C. Affinities of amino acid side chains for solvent water. Biochemistry 1981, 20, 849−855. (4) Rose, G. D.; Geselowitz, A. R.; Lesser, G. J.; Lee, R. H.; Zehfus, M. H. Hydrophobicity of amino acid residues in globular proteins. Science 1985, 229, 834−838. (5) Livingstone, J. R.; Spolar, R. S.; Record, M. T., Jr. Contribution to the thermodynamics of protein folding from the reduction in wateraccessible nonpolar surface area. Biochemistry 1991, 30, 4237−4244. (6) Schellman, J. A. The stability of hydrogen-bonded peptide structures in aqueous solution. C. R. Trav. Lab. Carlsberg Chim. 1955, 29, 230−259. (7) Klotz, I. M.; Franzen, J. S. Hydrogen bonds between model peptide groups in solution. J. Am. Chem. Soc. 1962, 84, 3461−3466. (8) Franzen, J. S.; Stephens, R. E. Effect of a dipolar solvent system on interamide hydrogen bonds. Biochemistry 1963, 2, 1321−1327. (9) Chakrabartty, A.; Kortemme, T.; Baldwin, R. L. Helix propensities of the amino acids measured in alanine-based peptides without helix-stabilizing side-chain interactions. Protein Sci. 1994, 3, 843−852. (10) Moreau, R. J.; Schubert, C. R.; Nasr, K. A.; Torok, M.; Miller, J. S.; Kennedy, R. J.; Kemp, D. S. Context-independent, temperaturedependent helical propensities for amino acid residues. J. Am. Chem. Soc. 2009, 131, 13107−13116. (11) Stanger, H. E.; Gellman, S. H. Rules for antiparallel β-sheet design: D-Pro-Gly is superior to L-Asn-Gly for beta-hairpin nucleation. J. Am. Chem. Soc. 1998, 120, 4236−4237. (12) Job, G. E.; Kennedy, R. J.; Heitmann, B.; Miller, J. S.; Walker, S. M.; Kemp, D. S. Temperature- and length-dependent energetics of formation for polyalanine helices in water: Assignment of wAla(n,T) and temperature-dependent CD ellipticity standards. J. Am. Chem. Soc. 2006, 128, 8227−8233. (13) Kemp, D. S. Construction and analysis of Lifson-Roig models for the helical conformations of α-peptides. Helv. Chim. Acta 2002, 85, 4392−4423. (14) Kim, E.; Paliwal, S.; Wilcox, C. S. Measurements of molecular electrostatic field effects in edge-to-face aromatic interactions and CHπ interactions with implications for protein folding and molecular recognition. J. Am. Chem. Soc. 1998, 120, 11192−11193. (15) Bhayana, B.; Wilcox, C. S. A minimal protein folding model to measure hydrophobic and CH-π effects on interactions between nonpolar surfaces in water. Angew. Chem., Int. Ed. 2007, 46, 6833− 6836. (16) Carroll, W. R.; Zhao, C.; Smith, M. D.; Pellechia, P. J.; Shimizu, K. D. A molecular balance for measuring aliphatic CH-π interactions. Org. Lett. 2011, 13, 4320−4323. (17) Yang, L. X.; Adam, C.; Nichol, G. S.; Cockroft, S. L. How much do van der Waals dispersion forces contribute to molecular recognition in solution? Nat. Chem. 2013, 5, 1006−1010. (18) Lypson, A. B.; Wilcox, C. S. Synthesis and NMR analysis of a conformationally controlled β-turn mimetic torsion balance. J. Org. Chem. 2017, 82, 898−909. (19) Kemp, D. S.; Boyd, J. G.; Muendel, C. C. The helical s constant for alanine in water derived from template-nucleated helices. Nature 1991, 352, 451−454. (20) Fersht, A. R.; Shi, J. P.; Knill-Jones, J.; Lowe, D. M.; Wilkinson, A. J.; Blow, D. M.; Brick, P.; Carter, P.; Waye, M. M.; Winter, G. Hydrogen bonding and biological specificity analysed by protein engineering. Nature 1985, 314, 235−238. 1881

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882

Article

Accounts of Chemical Research (39) Gao, J.; Bosco, D. A.; Powers, E. T.; Kelly, J. W. Localized thermodynamic coupling between hydrogen bonding and microenvironment polarity substantially stabilizes proteins. Nat. Struct. Mol. Biol. 2009, 16, 684−690. (40) Dannenberg, J. J.; Haskamp, L.; Masunov, A. Are hydrogen bonds covalent or electrostatic? A molecular orbital comparison of molecules in electric fields and H-bonding environments. J. Phys. Chem. A 1999, 103, 7083−7086. (41) Jäger, M.; Zhang, Y.; Bieschke, J.; Nguyen, H.; Dendle, M.; Bowman, M. E.; Noel, J. P.; Gruebele, M.; Kelly, J. W. Structure− function−folding relationship in a WW domain. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 10648−10653. (42) Kaul, R.; Deechongkit, S.; Kelly, J. W. Synthesis of a negatively charged dibenzofuran-based β-turn mimetic and its incorporation into the WW miniprotein-enhanced solubility without a loss of thermodynamic stability. J. Am. Chem. Soc. 2002, 124, 11900−11907. (43) Fuller, A. A.; Du, D.; Liu, F.; Davoren, J. E.; Bhabha, G.; Kroon, G.; Case, D. A.; Dyson, H. J.; Powers, E. T.; Wipf, P.; Gruebele, M.; Kelly, J. W. Evaluating β-turn mimics as β-sheet folding nucleators. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 11067−11072. (44) Price, J. L.; Shental-Bechor, D.; Dhar, A.; Turner, M. J.; Powers, E. T.; Gruebele, M.; Levy, Y.; Kelly, J. W. Context-dependent effects of asparagine glycosylation on Pin WW folding kinetics and thermodynamics. J. Am. Chem. Soc. 2010, 132, 15359−15367. (45) Hanson, S. R.; Culyba, E. K.; Hsu, T.-L.; Wong, C.-H.; Kelly, J. W.; Powers, E. T. The core trisaccharide of an N-linked glycoprotein intrinsically accelerates folding and enhances stability. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 3131−3136. (46) Culyba, E. K.; Price, J. L.; Hanson, S. R.; Dhar, A.; Wong, C.-H.; Gruebele, M.; Powers, E. T.; Kelly, J. W. Protein native-state stabilization by placing aromatic side chains in N-glycosylated reverse turns. Science 2011, 331, 571−575. (47) Price, J. L.; Powers, D. L.; Powers, E. T.; Kelly, J. W. Glycosylation of the enhanced aromatic sequon is similarly stabilizing in three distinct reverse turn contexts. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 14127−14132. (48) Chen, W.; Enck, S.; Price, J. L.; Powers, D. L.; Powers, E. T.; Wong, C.-H.; Dyson, H. J.; Kelly, J. W. Structural and energetic basis of carbohydrate−aromatic packing interactions in proteins. J. Am. Chem. Soc. 2013, 135, 9877−9884. (49) Zhou, H.; Zhou, Y. Quantifying the effect of burial of amino acid residues on protein stability. Proteins: Struct., Funct., Genet. 2004, 54, 315−322. (50) Wimley, W. C.; Creamer, T. P.; White, S. H. Solvation energies of amino acid side chains and backbone in a family of host−guest pentapeptides. Biochemistry 1996, 35, 5109−5124. (51) Pace, C. N.; Fu, H.; Fryar, K. L.; Landua, J.; Trevino, S. R.; Shirley, B. A.; Hendricks, M. M.; Iimura, S.; Gajiwala, K.; Scholtz, J. M.; Grimsley, G. R. Contribution of hydrophobic interactions to protein stability. J. Mol. Biol. 2011, 408, 514−528. (52) Nishio, M. The CH/π hydrogen bond in chemistry. Conformation, supramolecules, optical resolution and interactions involving carbohydrates. Phys. Chem. Chem. Phys. 2011, 13, 13873− 13900. (53) Zeinalipour-Yazdi, C. D.; Pullman, D. P. Correlation of polarizabilities with van der Waals interactions in π-systems. J. Phys. Chem. B 2006, 110, 24260−24265. (54) Williams, V. E.; Lemieux, R. P. Role of dispersion and electrostatic forces on solute−solvent interactions in a nematic liquid crystal phase. J. Am. Chem. Soc. 1998, 120, 11311−11315. (55) Meyer, E. A.; Castellano, R. K.; Diederich, F. Interactions with aromatic rings in chemical and biological recognition. Angew. Chem., Int. Ed. 2003, 42, 1210−1250. (56) Fujii, A.; Hayashi, H.; Park, J. W.; Kazama, T.; Mikami, N.; Tsuzuki, S. Experimental and theoretical determination of the accurate CH/π interaction energies in benzene-alkane clusters: correlation between interaction energy and polarizability. Phys. Chem. Chem. Phys. 2011, 13, 14131−14141.

(57) Hudson, K. L.; Bartlett, G. J.; Diehl, R. C.; Agirre, J.; Gallagher, T.; Kiessling, L. L.; Woolfson, D. N. Carbohydrate-aromatic interactions in proteins. J. Am. Chem. Soc. 2015, 137, 15152−15160. (58) Hsu, C.-H.; Park, S.; Mortenson, D. E.; Foley, B. L.; Wang, X.; Woods, R. J.; Case, D. A.; Powers, E. T.; Wong, C.-H.; Dyson, H. J.; Kelly, J. W. The dependence of carbohydrate−aromatic interaction strengths on the structure of the carbohydrate. J. Am. Chem. Soc. 2016, 138, 7636−7648. (59) Laughrey, Z. R.; Kiehna, S. E.; Riemen, A. J.; Waters, M. L. Carbohydrate-π Interactions: What Are They Worth? J. Am. Chem. Soc. 2008, 130, 14625−14633.

1882

DOI: 10.1021/acs.accounts.7b00195 Acc. Chem. Res. 2017, 50, 1875−1882