Phosphorylation-induced Conformational Changes in a Mitogen ...

THE JOURNAL OF BIOLOGICAL CHEMISTRY © 2002 by The American Society for Biochemistry and Molecular Biology, Inc.

Vol. 277, No. 49, Issue of December 6, pp. 47653–47661, 2002 Printed in U.S.A.

Phosphorylation-induced Conformational Changes in a Mitogenactivated Protein Kinase Substrate IMPLICATIONS FOR TYROSINE HYDROXYLASE ACTIVATION* Received for publication, August 27, 2002, and in revised form, September 26, 2002 Published, JBC Papers in Press, October 1, 2002, DOI 10.1074/jbc.M208755200

Collin M. Stultz‡§¶, Andrew D. Levin‡, and Elazer R. Edelman‡§ From the ‡Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 and the §Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 02115

Mitogen-activated protein (MAP) kinase-mediated phosphorylation of specific residues in tyrosine hydroxylase leads to an increase in enzyme activity. However, the mechanism whereby phosphorylation affects enzyme turnover is not well understood. We used a combination of fluorescence resonance energy transfer (FRET) measurements and molecular dynamics simulations to explore the conformational free energy landscape of a 10-residue MAP kinase substrate found near the N terminus of the enzyme. This region is believed to be part of an autoregulatory sequence that overlies the active site of the enzyme. FRET was used to measure the effect of phosphorylation on the ensemble of peptide conformations, and molecular dynamics simulations generated free energy profiles for both the unphosphorylated and phosphorylated peptides. We demonstrate how FRET transfer efficiencies can be calculated from molecular dynamics simulations. For both the unphosphorylated and phosphorylated peptides, the calculated FRET efficiencies are in excellent agreement with the experimentally determined values. Moreover, the FRET measurements and molecular simulations suggest that phosphorylation causes the peptide backbone to change direction and fold into a compact structure relative to the unphosphorylated state. These results are consistent with a model of enzyme activation where phosphorylation of the MAP kinase substrate causes the N-terminal region to adopt a compact structure away from the active site. The methods we employ provide a general framework for analyzing the accessible conformational states of peptides and small molecules. Therefore, they are expected to be applicable to a variety of different systems.

Phosphorylation of specific amino acids near the surface of a protein is an almost universal mechanism of protein activation that has been long appreciated (1, 2). Yet the mechanism whereby phosphorylation modifies the activity of the protein is not well understood (1). Analysis of phosphorylation sites from

* This work was supported in part by National Institutes of Health Grants GM/HL 49039 and HL 60407, by the Fannie and John Hertz Foundation, and by Brigham and Women’s Hospital. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. ¶ To whom correspondence should be addressed: Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Bldg. 16-343, 77 Massachusetts Ave., Cambridge, MA 02139 Tel.: 617-694-1692; Fax: 617-253-2514; E-mail: cmstultz@ partners.org. This paper is available on line at http://www.jbc.org

different enzymes reveals common themes. In eukaryotes, phosphorylation typically occurs at tyrosine, threonine, or serine side chains, and these phosphorylated residues often form a network of hydrogen bonds with adjacent positively charged arginine residues (1, 3– 6). The network of hydrogen bonds and salt bridges that form can then communicate phosphorylation to distant areas of the protein (6). In the case of yeast glycogen phosphorylase, phosphorylation occurs at a threonine residue located near the N terminus, a region that overlies the active site of the enzyme (3). The enzyme, which normally exists as a homodimer, contains two active catalytic sites, one in each monomer (3). Phosphorylation at this site causes the N-terminal region to fold into a compact structure that wedges between the dimer interface. This structural change helps to reorient the active site in a manner that facilitates enzymatic activation (3). Examples such as this suggest that large scale movements of flexible regions within a protein are an important component of phosphorylation-induced protein activation (3, 4). The aromatic amino acid hydroxylase superfamily comprises a set of enzymes that require phosphorylation at specific sites to become activated (7–9). The three enzymes within this class, tryptophan hydroxylase, phenylalanine hydroxylase, and tyrosine hydroxylase, share significant sequence homology within their catalytic domains and are expected to have similar tertiary structures in this region (8, 10). Unfortunately, complete x-ray crystallographic identification of the structure of all three enzymes has been limited as these compounds are particularly difficult to purify and crystallize (8, 9). The structure of one member of this superfamily, phenylalanine hydroxylase, was recently presented in both the phosphorylated and unphosphorylated state (8). The structure is composed of an N-terminal regulatory domain and a C-terminal catalytic domain (10). The N-terminal domain contains a 15-residue sequence (residues 19 –33) that extends across the active site in the catalytic domain. However, phenylalanine hydroxylase is phosphorylated at Ser-16, and unfortunately, the first 18 residues in the structure were not identified in the electron density in either the phosphorylated or the unphosphorylated structures (8). This suggests that this region does not have a well defined three-dimensional structure in the absence of the substrate, phenylalanine. As a result, the two structures are quite similar. Because the structure of the phosphorylated region remains unknown, the structures of the C-terminal domains do not immediately yield information on the mechanism of phosphorylation-induced enzyme activation. Tyrosine hydroxylase, another member of this superfamily, catalyzes the rate-limiting step in the biosynthesis of the catecholamines dopamine, norepinephrine, and epinephrine, which are molecules involved in the regulation of heart rate,

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blood pressure, and behavior (11). The structure of the Cterminal catalytic domain has also been solved recently and bears remarkable similarity to the corresponding domain from phenylalanine hydroxylase (10, 12). However, no depiction of the structure of the regulatory domain currently exists. Because all of the phosphorylation sites of the protein are located within the regulatory domain, it is difficult to determine how phosphorylation affects the activity of the enzyme based on an analysis of the catalytic domain alone. In situ, tyrosine hydroxylase is phosphorylated at four distinct serine residues, all located in the N-terminal autoregulatory region (Ser-8, Ser-19, Ser-31, and Ser-40) (13, 14). However, only phosphorylation of Ser-19, Ser-31, and Ser-40 has been associated with an increase in enzyme activity (14, 15). Phosphorylation of Ser-40 results in an increase in activity even in the presence of known feedback inhibitors (16). By contrast, recent data (17) suggest that phosphorylation of Ser-19 affects the activity of tyrosine hydroxylase by facilitating the phosphorylation of Ser-40, i.e. the rate constant for Ser-40 phosphorylation is ⬃2–3-fold higher when Ser-19 is phosphorylated first. It has been suggested that upon phosphorylation at Ser-19, tyrosine hydroxylase adopts a more open conformation that enables kinases to have greater access to Ser-40 (17). Phosphorylation of Ser-19 and Ser-40 are mediated by calmodulin-dependent protein kinase II and cAMP-dependent protein kinase, respectively (13, 14). The phosphorylation of Ser-31 is unique in that it is accomplished by the mitogenactivated protein (MAP)1 kinases, ERK1 and ERK2 (13, 18, 19). Although MAP kinase-mediated phosphorylation of Ser-31 is associated with a modest 2-fold increase in enzyme activity, phosphorylation at this site appears to be a physiologically important mechanism for the stimulation of catecholamine production in a variety of different tissues (18, 19, 20). Therefore, deciphering the structural basis of enzyme activation by phosphorylation of Ser-31 is of particular interest. Haycock et al. (13) have identified a 10-residue peptide from tyrosine hydroxylase that contains the Ser-31 phosphorylation site, i.e. residues 24 –33. An analysis of the structure of phenylalanine hydroxylase and its sequence alignment to tyrosine hydroxylase suggests that this peptide sequence forms a flexible region that overlies the catalytic site (13). Because the structure of the regulatory domain of tyrosine hydroxylase is not known, we explored the structural consequences of phosphorylating this peptide to gain insight into the mechanism of MAP kinase-induced enzyme activation. Comparison with the homologous phenylalanine structure suggests that this peptide forms a disordered region that does not form stable contacts with any other residues within the protein (8). Therefore, an analysis of the accessible conformations of the peptide alone may yield insights into how phosphorylation of this region affects the structure of the protein. In this work we employed a combination of fluorescent resonance energy transfer (FRET) (21, 22) experiments and molecular dynamics simulations (25) to determine how phosphorylation affects the accessible conformational states of the peptide. Such an approach has broad appeal because it allows one to obtain the equilibrium distribution of states using FRET without the need to compute and analyze donor decay profiles (26). Furthermore, the two methods are complementary and yield a wealth of information on the equilibrium distribution of conformations for this biologically relevant peptide.

1 The abbreviations used are: MAP, mitogen-activated protein; FRET, fluorescence resonance energy transfer; dansyl, 5-dimethylaminonaphthalene-1-sulfonyl.

TABLE I Synthesized peptides utilized in this study with their corresponding abbreviations Peptide

Abbreviation

O-KQAEAVTSPR-W O-KQAEAVTS*PR-W O-KQAEAVTSPR KQAEAVTSPR-W O-AEAVTSPR-W

OpW Op*W Op pW FC

Function

Unphosphorylated peptide Phosphorylated peptide Dansyl standard Tryptophan standard Positive control

EXPERIMENTAL PROCEDURES

Peptide Design and Synthesis—The MAP kinase substrate used in this study corresponds to amino acids 24 –33 of rat tyrosine hydroxylase (13), KQAEAVTSPR. FRET measures the energy transferred between two fluorescent pharmacophores that have been added to the peptide/ protein of interest (24). In this study, a dansyl group (denoted by the letter O) is used as the acceptor group, and the indole side chain of tryptophan is used as the donor group. A total of five peptides was utilized in this study (see Table I). Note that the letter “p” denotes the peptide KQAEAVTSPR, and S* denotes phosphoserine. Peptide synthesis was carried out using a semi-manual process with a Wang resin (Genemed Synthesis, Inc.). For dansyl conjugation to the ␣-amine of the N terminus, dansyl chloride was dissolved in dimethylformamide with pyridine added as the base during conjugation. Cleavage was carried out using trifluoroacetic acid. Once the peptides had been synthesized, ⬃1–2 mg of each peptide was dissolved in 1 ml of phosphate-buffered saline at pH 7.4 and diluted to a final concentration of 5.0 ␮g/ml for analysis. Peptides were purified by reverse-phase high pressure liquid chromatography to greater than 90% purity and characterized by mass spectrometry before UV absorption spectrophotometry and fluorescence spectroscopy were conducted. Normalizing the Absorbance and Emission Profiles to Account for Concentration Differences—FRET measurements are expected to be strongly concentration-dependent (25), i.e. small differences in the concentrations of the different peptide solutions may lead to significant errors. Inaccuracies and limitations of the scale in weighing small quantities may therefore lead to significant errors in the calculated FRET efficiencies. To account for such concentration-dependent effects, the different optical profiles were adjusted as described below. First, the individual absorbance profiles of the Op and pW peptides were used as standards to calculate the adjustment to the optical profile of the OpW peptide. The concentrations of the Op and pW solutions can be calculated using Beer’s Law since the extinction coefficient and path length are known. If COp and CpW denote the concentrations of the Op and pW peptides, respectively, then the two absorbance profiles can be “normalized” to the same concentration. More precisely, if AOp denotes the absorbance profile of the Op peptide, then (CpW/COp) AOp corresponds to the absorbance profile when the concentration of the Op solution equals the concentration of the pW peptide. Once the absorbance profile of the Op and pW profiles were normalized, the absorbance profile for the OpW peptide must also be normalized to the same concentration. For this we used Equation 1, OpWA共␭兲 ⫽ k1 䡠 OpA共␭兲 ⫹ k2 䡠 pWA共␭兲

(Eq. 1)

where OpWA(␭), OpA(␭), and pWA(␭) denote the optical absorbances of the OpW, Op, and pW peptides at wavelength ␭. Equation 1 holds because FRET only affects the emission and not the absorption profiles. Because the OpW peptide contains both a dansyl and tryptophan moiety, its optical absorbance is the sum of the absorbances of the individual Op and pW absorbance profiles. The constants k1 and k2 are weighting coefficients that ensure that all three peptide solutions, OpW, Op, and pW, have the same concentration. Because the labeling of dansyl to tryptophan is 1:1, and the absorbance profiles of these peptides have already been normalized, we expect k1 to be equivalent to k2. Once the various profiles were obtained, the constants k1 and k2 were determined using a least square algorithm. Values for k1 and k2 differed by at most 0.02 units, suggesting that the Op and pW absorbance profiles had been appropriately normalized. Once the constant k1 is known, the absorption profiles for the peptide (in both the phosphorylated and unphosphorylated forms) and the positive control (peptide FC) can be normalized in a manner similar to that described above for the Op and pW peptides. Determining FRET Efficiencies for Flexible Peptides—With scaling of the emission profiles of all peptides to the same concentration for excitation frequency 290 nm and emission at 310 –560 nm, FRET effi-

Phosphorylation-induced Conformational Changes ciency in the OpW, Op*W, and FC peptides can be determined. The overall intensity of the emission profile of each peptide can be expressed (26) as shown in Equation 2, I共␭兲 ⫽ Fda 䡠 D共␭兲 ⫹ Fa 䡠 A共␭兲

(Eq. 2)

where I is intensity of the OpW, Op*W, or FC emission profiles, Fda is the weighted contribution of the donor emission curve D(␭) (corresponding to peptide pW) in the presence of the acceptor, and Fa is the weighted contribution of the acceptor emission curve A(␭) (corresponding to peptide Op). The constants Fda and Fa were determined using a least square algorithm. The FRET efficiency, E, is defined as the number of energy transfer events divided by the number of photons absorbed by the donor (22). Once the constant Fda is known, the FRET efficiency is calculated using the relationship (22) shown in Equation 3, E⫽1⫺

Fda Fd

(Eq. 3)

where Fd is the relative contribution of the donor when the acceptor is not present (i.e. Fd ⫽ 1). If the donor and acceptor are separated by a fixed distance, r, then the FRET efficiency can also be written (22) as Equation 4, E⫽

R06 R06

⫹ r6

(Eq. 4)

where R0 denotes the Fo¨ rster critical distance, i.e. the distance at which E is equal to 0.5. For the dansyl/tryptophan pair, R0 equals 23.6 Å (22). Therefore, when the donor and acceptor are at a fixed distance apart, solving for R yields the distance between the two fluorophores. In the case of flexible peptides, the interpretation of the FRET efficiency is not straightforward. Because most peptides can adopt a number of distinct conformational states in solution, the observed emission spectra correspond to an average of the different spectra from all of the accessible conformational states of the peptide. Thus for peptides, the statistical mechanical expression for the FRET efficiency becomes Equation 5, E⫽

冓

R06

R06

冔

⫹ r6

(Eq. 5)

where the average is taken over the canonical distribution of all possible end-to-end distances. As a result, if the distribution of different conformational states is not known a priori, as is usually the case, one cannot calculate the average end-to-end distance in a straightforward manner from the experimentally determined transfer efficiency alone. In this work we augment the FRET calculations with molecular dynamics simulations. The calculated potential of mean force agrees with the data obtained from the FRET measurements and gives additional insight into the equilibrium distribution of end-to-end distances. Molecular Dynamics Simulations—An extended carbon polar-hydrogen model of the peptide KQAEAVTSPR peptide was constructed using the CHARMM program (27). The FRET calculations utilized peptides that were labeled with a dansyl group on the N terminus and a tryptophan residue on the C terminus. Because the CHARMM parameter set does not contain parameters for a dansyl group, we placed a tryptophan residue on both the N and C termini of the peptide. Because both dansyl and tryptophan contain large aromatic moieties, this substitution was expected to yield results that would be comparable with the data obtained in the FRET measurements. Coordinates for the unphosphorylated and phosphorylated peptides, KQAEAVTS*PR, were built using CHARMM (27). Parameters for the phosphate moiety on phosphoserine were derived from the CHARMM parameter set for nucleotides (28) and the MCSS parameter set (29). The unphosphorylated peptide is denoted by WpW, and the phosphorylated peptide is henceforth denoted by Wp*W. The calculations began with the fully extended conformation of the peptide. Because the potential of mean force calculations sample all possible end-to-end distances, the starting conformation of the peptide is not expected to influence the final results. Each model peptide was minimized for 100 steps of steepest descent minimization using a distancedependent dielectric to minimize any unfavorable atomic overlaps within the structure. The extended structure of each peptide was then overlaid with a set of 1000 equilibrated TIP3 water molecules (27), and waters that overlapped with the structures were removed. The remaining water molecules were equilibrated in the field of the fixed protein structure with 100 steps of steepest descent minimization using a dielectric constant of 1, followed by 5 ps of standard molecular dynamics

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at 300 K. Molecular dynamics simulations employed a nonbonded cutoff of 13 Å. van der Waals interactions were switched to 0 between 10 and 12 Å, and electrostatic interactions were shifted to 0 at a distance of 12 Å. This process was repeated until no additional water molecules could be added. Three solvent overlays were performed. A total of 1649 water molecules was added to the unphosphorylated structure, and 1593 water molecules were added to the phosphorylated structure. These were the only simulations that employed a fixed peptide structure. All subsequent molecular simulations were performed with fully flexible peptides. Water molecules were restrained to lie within a sphere of radius 30 Å surrounding the peptide of interest using a stochastic boundary potential (30). For these calculations the end-to-end distance for each peptide was defined as the distance between the C␣ carbons of each tryptophan residue located at each end, i.e. the reaction coordinate. For the unphosphorylated peptide, the reaction coordinated is denoted by ␨, and for the phosphorylated peptide, the reaction coordinated is denoted by ␨*. To sample all possible end-to-end distances for the peptide, a series of simulations were performed where the reaction coordinate was restrained to a predefined value. Each separate simulation is referred to as a window. At each window, the reaction coordinate was restrained to a specified distance using a harmonic biasing potential with a force constant of 25 kcal/mol/Å2. The first window began at the fully extended state that corresponds to ␨ ⫽ 36.4 Å for the WpW peptide and 35.6 for the Wp*W peptide. Each subsequent window began with an end-to-end distance that was 0.5 Å less than the prior value, e.g. windows for WpW were centered at 36.4, 35.9, 35.4, 34.9, 34.5 . . . 6.9 Å. A total of 60 windows was run for the WpW peptide and 63 windows for the Wp*W peptide. Each window consisted of 20 ps of equilibration followed by 20ps of production dynamics. The value of the reaction coordinate during the simulations was saved every 0.01ps; this yielded 2000 data points per window. The potential of mean force at each window i, Gi(␨), was calculated from the resulting frequency distribution, pi, and the biasing potential, Vi(␨), using the using the equation (see Ref. 25) Gi(␨) ⴝ ⫺kT ln pi(␨) ⫺ Vi(␨) ⫹ Ci where Ci is a constant that is a function of the temperature, T, and the biasing potential, Vi(␨). One continuous potential of mean force was generated with a program, SPLICE, which automatically overlapped regions of individual potentials at a common point where the potentials had similar slopes (31). The final potential of mean force was smoothed using the moving average window method with a window span of 3. Smoothing did not affect the positions of saddle points and energy minima for either potential of mean force. The lowest energy point of each potential of mean force was set to zero (25, 31). The resulting potential of mean force represents the free energy of each conformation relative to that of the lowest energy state. Representative average structures for each window were generated using the COOR facility in CHARMM (27). Average structures were energyminimized for 100 steps of steepest descent minimization to relieve any poor van der Waals overlaps within the molecule. FRET efficiencies were calculated from the potential of mean force as described under “Appendix.” RESULTS

Determination of FRET Efficiencies—Emission data (excitation 290 nm and emission 328 –560 nm) for the OpW, Op*W, and the FC peptides were normalized to the donor peak. Upon excitation of the donor, energy is transferred to the acceptor, and the amount of energy transferred is a function of the donor-acceptor distance. As expected, the shift in the emission spectra for the shortest peptide, FC, is greater than that of peptide OpW (Fig. 1). Surprisingly, although peptides OpW and Op*W contain the same number of amino acids, the acceptor peak of the phosphorylated peptide, Op*W, is largest, suggesting that phosphorylation significantly alters the end-to-end distance distribution of the peptide. We note that since the molecular structures of the OpW and Op*W peptides differ, it is possible that the phosphate group itself interacts with either fluorophore to affect its fluorescence. If this occurs, then the difference in the height of the peaks may represent the effect of the added phosphate and not an actual change in the end-toend distance. To demonstrate that the phosphate group does not directly interfere with the either the donor or acceptor emission spectra, we measured the emission spectra for the

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FIG. 1. Emission spectra (excitation 290 nm and emission 330 –550 nm) for the OpW, FC, and Op*W peptides. Each spectra has been normalized to the donor peak. Each profile is labeled with its corresponding peptide.

phosphorylated Op and pW peptides and compared this to the emission spectra of the unphosphorylated peptides, and no difference was observed (data not shown). The transfer efficiency for the FC peptide is greater than that of the OpW peptide and less than Op*W (Table II). Peptide FC has two fewer amino acids than peptide OpW and should have a greater efficiency as the transfer efficiency is related to the distance between the fluorophores. However, although the Op*W peptide contains more amino acids, its transfer efficiency is larger than that of the FC peptide. This suggests that the distance distribution for the Op*W peptide favors conformations with smaller end-to-end distances. Calculation of the Free Energy Profiles and FRET Efficiencies—To determine the structural basis underlying such a significant change in the emission spectra, we conducted detailed molecular dynamics simulations with explicit solvent for both the unphosphorylated and phosphorylated peptides. The free energy profile for both the unphosphorylated and phosphorylated peptides are shown in Fig. 2. Each profile, or potential of mean force, corresponds to the free energy of each conformation as a function of the end-to-end distance. For each peptide, only a few low energy states exist among the different possible end-to-end distances. In particular, for peptide WpW three local energy minima are present and are located at ⬃9 (state N3), 11 (state N2), and 24 Å (state N1). The state of lowest free energy for the system is located at 24 Å. Fig. 2B shows the free energy profile for the phosphorylated peptide, Wp*W. It has two local energy minima located at 9 (state N*2) and 15 Å (state N*1). Unlike the free energy profile shown in Fig. 2A, the potential of mean force for the phosphorylated peptide is not dominated by one conformation, i.e. it has two distinct minima with very similar energies. Furthermore, it has no low energy states with end-to-end distance greater than 20 Å, suggesting that the phosphorylated peptide preferentially adopts conformations that are more compact. FRET efficiencies were calculated using Equation 5, as described under “Experimental Procedures” and the “Appendix,” and they are listed in Table III. The calculated values are in excellent agreement with the experimentally determined results. The relatively small divergence between the calculated and experimental results may be due, in part, to the fact that the molecular dynamics simulations were performed on a peptide that had a tryptophan residue at its N terminus instead of a dansyl group. However, the fact that the calculated FRET efficiencies are within 10% of the experimentally determined

TABLE II Measured FRET efficiencies Peptide

OpW FC Op*W

FRET efficiency

0.46 0.76 0.92

values suggests that the calculated free energy profiles capture important aspects of the conformational free energy landscape of the experimentally studied peptides. The Structural Consequences of Phosphorylation—A comparison of the free energy profiles for both the unphosphorylated and phosphorylated peptides reveals the etiology of the shift in the average end-to-end distances. As mentioned previously, for both peptides, only a few conformational states are preferred at equilibrium. For the unphosphorylated peptide the dominant state is located at 24 Å, whereas for the phosphorylated virtually no conformations with end-to-end distances greater than 20 Å are observed at equilibrium (see Fig. 2). The average end-to-end distances for both the unphosphorylated and phosphorylated peptide are also listed in Table III. Phosphorylation induces a 10-Å change in the end-to-end distance of the MAP kinase substrate. In order to decipher the intramolecular interactions that are responsible for this dramatic shift in the density of states, and in the average end-toend distances, we examined representative conformations from the equilibrium distribution of both the unphosphorylated and phosphorylated peptides. A representative conformation from state N1 is shown in Fig. 3A. In this structure, a salt bridge forms between the side chains of Asp-5 and Arg-11. As the end-to-end distance increases above 24 Å, the side chain of Asp-5 and Arg-11 separate, and the salt bridge breaks and hence the free energy of the system increases. Fig. 3B depicts a representative structure from state N3, the low energy state with the shortest end-to-end distance. Although not shown, we note that the structure corresponding to state N2 is very similar to the structure representing state N3. In state N3, the salt bridge involving Asp-5 and Arg-11 is preserved, and a new hydrogen bond is formed that involves the side chain of Lys-2 and the backbone carbonyl of Arg-11. By contrast, in state N1, the side chain of Lys-2 remains fully solvated and does not form any electrostatic interactions with other atoms within the peptide. Instead it is free to hydrogenbond to the surrounding water molecules. In state N3 this


FIG. 2. A, free energy profile (potential of mean force) for the unphosphorylated peptide, WpW. The state of lowest free energy is labeled N. B, potential of mean force for the phosphorylated peptide Wp*W. All plots made with MATLAB (© Mathworks). TABLE III Comparison of energy transfer efficiencies using FRET and MD simulations Peptide

Experimentally measured FRET efficiency

Calculated FRET efficiency from MD simulations

Calculated average end-toend distance

Unphosphorylated

0.460

0.502

22.9

Phosphorylated

0.920

0.960

12.5

Å

entropically favored side chain conformation is replaced with a hydrogen bond between a charged species, Lys-2, and an uncharged moiety, the CO group of Arg-11. The fact that the free energy of state N3 is significantly less favorable than that of state N1 suggests that the contributions from this hydrogen bond are insufficient to compensate for the loss of the entropically favored side chain conformation. Phosphorylation at Ser-9 introduces a negatively charged moiety that disrupts the salt bridge between Asp-5 and Arg-11. Fig. 4A shows the conformation corresponding to state N*1. The side chain of Arg-11 forms a salt bridge with the phosphate group of phosphoserine. Because Asp-5 is no longer involved in

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FIG. 3. A, representative structure corresponding to the state of lowest free energy for the unphosphorylated peptide, state N1. (Average structure from the window centered at 24 Å.) The side chains of arginine 11 and aspartate 5 form a salt bridge that is denoted with a dotted line. B, representative structure corresponding to the state N3. (Average structure from the window centered at 9 Å.) The side chains of arginine 11 and aspartate 5 form a salt bridge that is denoted with a dotted line. In addition, the side chain of Lys-2 forms a hydrogen bond with the backbone carbonyl of arginine 11. In both figures charged residues are illustrated as “licorice” models, and the N and C termini are explicitly labeled. All molecular figures made with QUANTA (© Molecular Simulations).

a salt bridge that lies between the two ends of the molecule, it is free to form a second salt bridge with the side chain of Lys-2. In this conformation, there are no residues lying between the ends of the peptide. As a result, the two ends of the molecule can approach one another without breaking any favorable electrostatic contacts. Fig. 4B depicts a representative conformation corresponding to state N*2. In this state the two ends of the peptide are closest, ⬃9 Å apart. Although a hydrogen bond is formed between the phosphate moiety and the backbone carbonyl of Lys-2, no salt bridge is formed when the peptide adopts this conformation. Interestingly, all three charged side chains are fully solvent-exposed. In this fully solvent-exposed state, the charged side chains are free to adopt different conformations as they make interactions with the surrounding water molecules. Because the free energy of any given state is the sum of both entropic and enthalpic contributions, this favorable entropic contribution must compensate for the lack of favorable electrostatic interactions such as the salt bridges found in state N*1. Therefore, whereas the energy of state N*1 appears to be dominated by favorable enthalpic interactions, the energy of state N*2 is dominated by favorable entropic

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FIG. 4. A, representative structure corresponding to state N*1. (Average structure from the window centered at 15 Å.) As before, charged residues are displayed as licorice figures. Two salt bridges are formed. One involves the phosphate group and the side chain of arginine 10, and the other involves the side chain of aspartate 5 and lysine 2. B, representative structure corresponding to state N*2. (Average structure from the window centered at 9 Å.) All charged side chains are solventexposed, and no salt bridges are formed. The hydrogen atom from the phosphate group hydrogen bonds to the backbone carbonyl of lysine 2.

terms. In both states, the presence of a phosphate group causes a rearrangement in the structure of the peptide that leads to the disruption of the central Asp-5, Arg-11 salt bridge. When this intervening interaction is removed, the N and C termini of the molecule are free to approach one another at significantly smaller distances. In order to determine how conformational changes in this MAP kinase substrate could affect the structure of tyrosine hydroxylase, we compared the main chain conformations of low energy structures from both the unphosphorylated and phosphorylated states. Fig. 5A shows the superimposed backbone traces from both the phosphorylated state, N*1 (red) and the lowest energy state for the unphosphorylated peptide N1 (green). Both structures were aligned to minimize the root mean square distance between all backbone heavy atoms. Because the MAP kinase substrate is part of a larger N-terminal regulatory domain, the potential path of the proximal portion of the N-terminal region is depicted with an arrow in each case. In state N*1, the N-terminal regulatory arm can take an alternate path that differs from the potential path in the unphosphorylated state by almost 60°. Fig. 5B depicts the superimposed backbone traces from state N*2 (blue) and state N1 (green).

FIG. 5. A, superimposed backbone traces of state N from the unphosphorylated peptide (green) and state N*1 from the phosphorylated peptide. The N and C termini are explicitly labeled. Both structures were aligned using the COOR ORIENT facility of CHARMM (27). The final backbone root mean square deviation between both peptides was 4.8 Å. B, superimposed backbone traces of state N from the unphosphorylated peptide (green) and state N*2 from the phosphorylated peptide (blue). The final backbone root mean square deviation between both peptides was 4.9 Å. A and B, the potential pathway of the remainder of the N-terminal regulatory arm is shown with arrows.

Once again, the N-terminal region of the peptide is in a different orientation relative to that of the unphosphorylated state, and the potential paths of the N-terminal regulatory region are schematically shown with an arrow. This illustrates how conformational changes in this MAP kinase substrate could reorient the N-terminal regulatory arm such that it adopts a new position away from the catalytic site. DISCUSSION

The regulation of cellular function is a complex process. Reversible phosphorylation of specific sites in a given protein is an often-utilized mechanism for short term protein activation and/or deactivation (1). As such, phosphorylation can be thought of as a molecular switch that modifies the function of a protein for as long as the protein remains phosphorylated. Although the mechanism whereby phosphorylation results in protein activation has been elucidated in only a handful of proteins (1, 3, 4, 6), it is likely that phosphorylation mediates protein activation by inducing important conformational changes within the tertiary structure of the protein. Such structural modifications can be achieved in a variety of differ-

Phosphorylation-induced Conformational Changes ent ways, and one common mechanism involves the phosphorylation of a site located within a flexible, solvent-exposed region of the protein (3, 4). In this work, changes in the emission spectra of the peptide of interest upon phosphorylation were determined using FRET experiments. These data were supplemented with results obtained from detailed molecular dynamics simulations to determine how phosphorylation affects the accessible conformational states of the peptide. A great body of literature exists on applying FRET to rigid molecules that have well defined structures in solution (22). By contrast, in this work we apply FRET to small peptides that can adopt a number of different conformations in aqueous solvent (25). Typically, FRET is used in conjunction with time-resolved donor decay measurements to obtain the distribution of distances between a given donor and acceptor within a flexible macromolecule (32–35). This is possible because time-resolved decay spectra are a function, in part, of the donor-acceptor distance distribution (22). However, the interpretation of these spectra is complicated by the fact that different conformational states relax at different rates. Because FRET measures the rate of energy transfer between an energy donor and acceptor that are in relatively close proximity of one another, conformations with small donor-acceptor distances relax at a fast rate relative to conformations with large donor-acceptor distances (32). This introduces some uncertainty into measurements of the distance distribution because spectroscopic decay experiments record spectra on time scales that are longer than the average decay time of the sample. As a result, only conformations with large donor-acceptor distances will contribute to points on the emission spectra that are obtained at later times (32), creating a phenomenon that is equivalent to a spectroscopic illusion. Although the equilibrium distribution of conformations does not change, at later time points it appears as if only the states with long donor-acceptor distances are present since these conformations are the only ones that continue to contribute to the decay emission spectra (32). Therefore, the spectroscopically observed distribution of conformations changes with time although the true equilibrium distribution does not. In previous studies (32, 35), this artifact of time-dependent FRET measurements has been accounted for by modeling the spectroscopically observed change in the distribution of states as a diffusive process. However, using this method to obtain the equilibrium distribution of states is problematic because it requires the numerical solution of a complex differential equation (32, 35). Given the difficulty in obtaining accurate numerical solutions, one must assume a specific form(s) of the probability distribution of states (32). By contrast, in this work we employ detailed molecular simulations to derive the distribution of conformational states for the peptides of interest, and we compare these data to the experimentally obtained FRET transfer efficiencies. In addition, we demonstrate how transfer efficiencies can be calculated from the equilibrium distribution of states or, equivalently, the free energy profile. If the calculated transfer efficiency agrees with the experimental result, as in this case, we assume that the calculated potential of mean force is an accurate representation of the free energy landscape of the peptide. In this work, both the fluorescent resonance experiments and the molecular dynamics simulations suggest that phosphorylation induces a dramatic change in the equilibrium distribution of conformational states. The combination of these techniques not only adds to the understanding of this family of enzymes but also may be extended to other as yet undefined problems in structural biochemistry. One limitation of the approach is that it is most useful when the data from the simulations agrees with the spectroscopic

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measurements. Comparison of FRET emission spectra with data derived from molecular simulations provides a mechanism for the validation of dynamic simulations performed on the system of interest. If the spectroscopic measurements are correct, then discrepancies between the calculated and measured FRET efficiencies can be used to make improvements in the simulation methodology and force field. Conversely, shortcomings in the experimental protocol for measuring FRET efficiencies may be responsible for differences between the calculated and measured result. Discrepancies between the two values should prompt a reexamination of both the experimental protocol and the simulation methodology. In this work, the calculated FRET transfer efficiencies were in excellent agreement with the experimentally determined values. Moreover, the structures obtained from the molecular simulations provide a window into the detailed molecular interactions that are responsible for the shift in the emission spectra. In the unphosphorylated state, a salt bridge forms between the side chain of Arg-11 and Asp-5. At closer end-toend distances, additional electrostatic interactions are formed that are entropically unfavorable relative to the lowest energy state. By contrast, in the phosphorylated state, the Arg-11 side chain hydrogen-bonds to the side chain of the phosphorylated serine residue in a manner that is similar to what has been observed in other systems (3). In its new position, the Arg side chain is removed from its original orientation, and this allows other charged residues to form additional salt bridges. These interactions result in dramatic changes in the conformational free energy landscape of the peptide. An examination of the main chain conformation from both peptides suggests that phosphorylation could result in a dramatic change in the direction of the path of the N-terminal autoregulatory sequence. This change in direction could affect protein activation in a number of ways. First and foremost, since the N-terminal domain is believed to lie over the catalytic site, the new direction of the N-terminal region may remove steric hindrances that would prevent the substrate from entering the catalytic site. Second, since an additional tyrosine hydroxylase phosphorylation site, Ser-19, is located within the proximal portion of the N-terminal regulatory domain, this conformational change may help to reorient this residue so that it can interact with other residues near the surface of the protein. Prior work suggests that phosphorylation of Ser-19 promotes phosphorylation of Ser-40 (17). This raises the interesting possibility that phosphorylation of Ser-31 may actually facilitate the interaction between Ser-19 and Ser-40. This notion is consistent with recent work (19) that suggests that inhibiting MAP kinase-induced phosphorylation of Ser-31 also leads to an increase in Ser-40 phosphorylation. Because the structure of the N-terminal domain of tyrosine hydroxylase is not known, the orientations shown in Fig. 5 remain speculative. We cannot rule out that the N-terminal regulatory domain makes interactions with other residues within the protein in the phosphorylated state. However, the fact that the phosphorylated N-terminal region in the related protein, phenylalanine hydroxylase, is disordered in both the unphosphorylated and phosphorylated structures suggests that, in the absence of the substrate, the phosphorylated autoregulatory region does not interact with any other residues in the molecule. Another possibility is that the phosphorylated autoregulatory region interacts with the substrate, and as a result, this region adopts an unexpected main chain conformation in the phosphorylated state. In this scenario, activation would be a two-step process that involves both phosphorylation of specific sites and the binding of tyrosine to the active site. Similar two-step mechanisms have been proposed for pheny-

47660


lalanine hydroxylase as well as other systems (8, 36). Nevertheless, the work presented here forms a viable, and testable, model for the structural consequences for MAP kinase-mediated phosphorylation of Ser-31. Both FRET and molecular dynamics simulations are methods that can be used to probe the free energy surface of molecules. In this study, we demonstrated how the two complementary methods could be used to study significant conformational changes in a biologically relevant peptide. These data are used to derive the equilibrium distribution of conformations. Unlike prior methods, this distribution is obtained without measuring donor decay profiles (32). Furthermore, no assumptions about the particular form of the distribution of states are needed to obtain the free energy profile (32). Overall, the method presented in this work is a potentially powerful approach for performing detailed conformational analyses on peptides and small molecules of biological interest. Acknowledgments—We thank Peter So for helpful discussions and Shuguang Zhang for computational resources. APPENDIX

E⫽

冓

冔

R06 ⫹ ␨6

⫽ 具E共␨兲典 ⫽

冕

E共␨兲P共␨兲d␨

0

冕

e⫺G共␨兲/kT ⬁

It follows that the peptide FRET efficiency can be approximated by Equation 11, E⫽

(Eq. 7)

៮

៮

e⫺G共␨兲/kTd␨

0

e⫺C/kTe⫺G共␨兲/kT ⬁


0

冕

e⫺G共␨兲/kT ⬁

0

冕冕冕 ⬁

E共␨兲P共␨兲d␨

⬇

m


(Eq. 8)

(Eq. 11)

M


m

Equation 11 is used to calculate FRET efficiencies. These values are directly comparable with the experimentally determined FRET efficiencies. In addition, average end-to-end distances can be computed in a similar fashion from the probability density, P(␨), shown in Equation 12,

d␨

冕

(Eq. 10)


E共␨兲e⫺G共␨兲/kTd␨

e⫺G共␨兲/kT

e⫺C/kT

M

M

៮ (␨) denotes the absolute free energy of conformation ␨; where G k is Boltzmann’s constant, and T denotes the temperature in degrees Kelvin. However, in this work we calculate the relative free energy of each conformation, G(␨). Fortunately, the absolute free energy is related to G(␨), the relative free energy, in a ៮ (␨) ⫽ G(␨) ⫹ C, where C is straightforward manner as follows: G a constant. Therefore, we can express the probability density as shown in Equation 8,

⫽

冕

e⫺G共␨兲/kT

m

具␨典 ⬇

⬁

(Eq. 9)

where M denotes the end-to-end distance of the fully extended conformation, and m denotes the end-to-end distance of smallest conformation where significant van der Waals overlaps begin to form. The probability of a given conformation is then approximated by Equation 10,

(Eq. 6)

0

冕


m

៮ 共␨兲/kT ⫺G

e

M

0

៮

⫽

e⫺G共␨兲/kTd␨ ⬇

0

P共␨兲 ⫽

冕

⬁

⬁

where P(␨)d␨ is the probability that peptide WpW will adopt an end-to-end distance between ␨ and ␨ ⫹ d␨. For these calculations, we assume that the Fo¨ rster critical distance, R0, for a dansyl-tryptophan pair is also applicable to the simulated peptides that contain a tryptophan group on both ends. The probability density, P(␨), can be calculated in a straightforward manner from the absolute free energy profile as shown in Equation 7 (25, 29),

P共␨兲 ⫽

冕

P共␨兲 ⯝

Calculating FRET Efficiencies from the Potential of Mean Force—The FRET efficiency was calculated from the potential of mean force using Equation 5. More precisely, if E(␨) denotes the FRET efficiency of the peptide of interest, assuming the end-to-end distance is fixed at ␨, then by Equations 4 and 5 we can denote the FRET efficiency for the distribution of end-toend distances as in Equation 6, R06

⫺G(␵)/kT The integral 兰⬁e d␨, also referred to as the partition 0 function (23), goes over all possible end-to-end distances. Because the peptide cannot adopt a conformation longer than its fully extended state, the limits of the integral can be simplified to only include those states with lengths shorter than that of the fully extended state. Furthermore, very small end-to-end distances are unlikely to occur because they are associated with significant van der Waals overlaps. Such conformations make small contributions to the partition function. Therefore, the partition function can be approximated by Equation 9,

冕

M

␨P共␨兲d␨

(Eq. 12)

m

A comparison of the average end-to-end distances of the phosphorylated and unphosphorylated peptides provides a rough measure of the effect of phosphorylation on the overall structure of the peptide. REFERENCES 1. Graves, J. D., and Krebs, E. G. (1999) Pharmacol. Ther. 82, 111–121 2. Cohen, P. (2000) Trends Biochem. Sci. 25, 596 – 601 3. Lin, K., Rath, V., Dai, S., Fletterick, R., and Hwang, P. (1996) Science 273, 1539 –1541 4. Johnson, L. (1992) FASEB J. 6, 2274 –2282 5. Russo, A., Jeffrey, P., and Pavletich, N. P. (1996) Nat. Struct. Biol. 3, 696 –700 6. Feher, V. A., Yih-Ling, T., Hoch, J. A., and Cavanagh, J. (1998) FEBS Lett. 425, 1– 6 7. Fitzpatrick, P. (1999) Annu. Rev. Biochem. 68, 355–381 8. Kobe, B., Jennings, I., House, C., Michell, B., Goodwill, K., Santarsiero, B., Stevens, R., Cotton, R., and Kemp, B. (1999) Nat. Struct. Biol. 6, 442– 448 9. Jiang, G., Yohrling, G., Schmitt, J., and Vrana, K. (2000) J. Mol. Biol. 302, 1005–1017 10. Vrana, K. (1999) Nat. Struct. Biol. 6, 401– 402 11. Wevers, R. A., Andel, J., Brautigam, C., Geurtz, B., Heuvel, J., SteenbergenSpanjers, G., Smeitink, J., Hoffman, G., and Gabreels, F. (1999) J. Inherited Metab. Dis. 22, 364 –373 12. Goodwill, K., Sabatier, C., Marks, C., Raag, R., Fitzpatrick, P., and Stevens, R. (1997) Nat. Struct. Biol. 4, 578 –585 13. Haycock, J., Ahn, N., Cobb, M., and Krebs, E. (1992) Proc. Natl. Acad. Sci.

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