Binding in Copper Chaperones Atox1 and CopZ - American Chemical

J. Phys. Chem. B 2008, 112, 4583-4593

4583

Structure and Dynamics of Cu(I) Binding in Copper Chaperones Atox1 and CopZ: A Computer Simulation Study Agustina Rodriguez-Granillo† and Pernilla Wittung-Stafshede*,†,‡,§ Department of Biochemistry and Cell Biology, Department of Chemistry, and Keck Center for Structural Computational Biology, Rice UniVersity, 6100 Main Street, Houston, Texas 77251 ReceiVed: December 15, 2007; In Final Form: February 6, 2008

Copper chaperones deliver reduced copper (i.e., Cu(I)) to metal-binding domains of P-type ATPases in the cytoplasm of a range of organisms. Both chaperones and target domains have a ferredoxin-like fold and metal-binding motifs involving two Cys residues. Here, we investigated the Cu-binding geometry and structural dynamics of two homologous Cu(I) chaperones, Homo sapiens Atox1 and Bacillus subtilis CopZ, using a combination of quantum mechanical-molecular mechanics (QM-MM) and classical molecular dynamics (MD) methods. Our QM-MM optimized geometries for the holo- proteins suggested that Cu(I) in Atox1 favors a linear Cys(S)-Cu-Cys(S) arrangement but that this angle is close to 150° in CopZ. Classical MD simulations suggest that both Atox1 and CopZ apoforms have an increased conformational flexibility as compared to the respective holo- forms. This difference is most pronounced in CopZ and correlates with a lower in vitro thermal stability. Both average fluctuation (i.e., rmsd) and radius of gyration data demonstrate that the effects of Cu(I) coordination extend throughout the proteins. Distinct deviations between the two homologues were found in protein-solvent interactions, entropy of Cu(I) binding, and apo-protein Cys-Cys distance distributions. Our in silico results provide new insights into copper chaperone behavior with direct implications for copper transport mechanisms in vivo.

1. Introduction Copper (Cu) is one of the most prevalent transition metals in living organisms;1 its biological function is intimately related to its redox properties. Many proteins that participate in cellular respiration, antioxidant defense, neurotransmitter biosynthesis, connective-tissue biosynthesis, and pigment formation use Cu as their prosthetic active group.2-5 Since free Cu is toxic, Cu homeostasis in living organisms is tightly controlled by subtle molecular mechanisms.6-8 In eukaryotes, before cellular uptake via high-affinity Cu transporters of the CTR family,5 Cu(II) ions are reduced to Cu(I). During the past decade, an important class of proteins, termed Cu chaperones, has been identified in the cytoplasm that binds Cu(I) with two Cys coordination.5,9-12 These small, soluble proteins guide and protect the Cu ions within the cell, delivering them to the appropriate functional protein receptors. P-type ATPases are membrane spanning receptor proteins with cytoplasmic metal-binding domains that transfer Cu(I) through membranes from one cellular compartment to another. In humans, the Cu chaperone Atox1 (also termed Hah1 in some literature) delivers Cu(I) to the P-type ATPases ATP7A (i.e., Menkes disease protein) and ATP7B (i.e., Wilson disease protein) proteins,5,10,13 and ATP7A/B then translocates the Cu from the cytoplasm into the Golgi lumen for insertion into enzymes in the secretory pathway,5 such as apo-ceruloplasmin.14 Several diseases are related to an imbalance in Cu homeostasis; for example, Menkes and Wilson diseases15,16 and aceruloplasminemia.6,17 In bacteria, the CopZ chaperone receives Cu(I) from the cytoplasmic Cu-binding * To whom correspondence should be addressed. E-mail: pernilla@ rice.edu; tel.: (713) 348-4076; fax: (713) 348-5154. † Department of Biochemistry and Cell Biology. ‡ Department of Chemistry. § Keck Center for Structural Computational Biology.

domains of the P-type ATPase, CopA, located in the cell membrane. CopZ then delivers the metal to transcriptional repressors.5,18-20 One hallmark of the Cu chaperones is the similarity of the fold between the chaperone and the target metal-binding domains of the ATPases.2,21,22 Solution NMR and X-ray crystal structures21-29 demonstrate that these proteins possess a βRββRβ ferredoxin-like fold with the metal ion bound by the two Cys in a MXCXXC motif in a surface-accessible loop toward the N-terminus. Since these proteins switch between apo- and holoforms as part of their functional cycle, biophysical properties of both states are of biological relevance and may explain how vectorial Cu transport is achieved despite a shallow thermodynamic gradient.29-31 We recently reported the first in vitro unfolding study of Cu chaperones, focusing on the Bacillus subtilis CopZ and Homo sapiens Atox1 proteins.32 Both these homologues have a ferredoxin-like fold (rmsd of 2.2 Å) and a conserved Cu(I)-binding motif (Figure 1), but they share only 22% sequence identity. We found, through a range of spectroscopic measurements on purified samples, that both proteins unfold in two-state equilibrium reactions in vitro.32 Atox1 is much more resistant than CopZ to thermal perturbations in both apo- and holo-forms (pH 7). For both Atox1 and CopZ, the coordination of Cu(I) stabilizes the folded state toward chemical (20 °C and pH 7) and thermal perturbations.32 To assess the molecular origin of these in vitro differences,32 and to learn more about the role of Cu in chaperone structure and dynamics, we performed a set of classical molecular dynamics (MD) simulations on Atox1 and CopZ with and without Cu(I). MD is a useful method to explore the potential energy surfaces of large systems and may give residue-specific information not accessible by experimental techniques.33 Recently, this approach was used to study the role of the conserved

10.1021/jp711787x CCC: $40.75 © 2008 American Chemical Society Published on Web 03/25/2008

4584 J. Phys. Chem. B, Vol. 112, No. 15, 2008

Figure 1. Superimposition of NMR structures of apo-Atox1 (orange, 1TL5) and apo-CopZ (blue, 1P8G). The Met and Cys residues of the Cu-binding MxCxxC motif are shown in CPK in Atox1 and in stick representation in CopZ.

Met10 residue in Atox134 and, in combination with quantum mechanical (QM) calculations, to study the preferred Cu(I) coordination in the Cu-bridged Atox1 homo dimer.35 We first optimized the Cu geometries by quantum mechanics-molecular mechanics (QM-MM) schemes using the SIESTA program.36-39 Similar QM-MM calculations on the Cu(I) site geometries in the yeast homologue, Atx1, recently were reported.40 The QMMM optimized structures of the holo-forms were used for the classical parametrization and the starting points of the classical MD simulations. Our in silico QM-MM and MD results on apoand holo-forms of Atox1 and CopZ can be used to explain the in vitro unfolding/stability findings on a molecular level; moreover, the information obtained from our computations provides new insights into the physical behavior of Cu chaperones and possible Cu transport mechanisms. 2. Computational Methods 2.1. QM-MM Calculations. Simulations were performed starting from the NMR structures of both holo-proteins (Atox1: 1TL441 and Copz: 1K0V25). The protonation state of the titratable residues corresponds to the stable form at pH 7. Hydrogen atoms were added, favoring H-bonds.42-45 Na+ atoms were added to neutralize the systems by computing the total electrostatic potential on a grid.42-45 The systems were immersed in a pre-equilibrated truncated octahedral cell of TIP3P explicit water molecules.46 Water molecules extended at least 9 Å from the surface of the proteins. The systems contained the protein (1043 atoms), 2724 water molecules, and one Na+ atom for Atox1 and the protein (1072 atoms), 2926 water molecules, and nine Na+ ions for CopZ. These structures were generated using the “leap” module of Amber9.42-45 To obtain correct QM-MM starting structures, the NMR-based solvated structures were optimized and equilibrated by performing 500 ps of classical MD simulations at 300 K prior to QMMM geometry optimization. These short MD simulations were

Rodriguez-Granillo and Wittung-Stafshede performed only to equilibrate the environment (i.e., protein and solvent) that surrounds the QM subsystem active site (Cu(I) atom and coordinating Cys). These short MD simulations were performed using Amber9.42-45 Because the active site has not yet been parametrized, these Cu(I) atom and coordinating Cys residues (that will become the QM subsystem) were maintained as fixed during these equilibrating short MD simulations. The simulation parameters used for these equilibrating short MD simulations are the ones described in the following MD section. QM-MM calculations were carried out to explore the effect of the environment on Cu(I) binding in both Atox1 and CopZ. We employed a QM-MM implementation37-39 in which the QM subsystem was treated at the DFT level using the efficient SIESTA (Spanish Initiative for the Electronic Structure of Thousands of Atoms) program.36 The use of standard normconserving pseudo-potentials47 avoids the computation of core electrons, smoothing the valence charge density at the same time. In the present study, the nonlinear partial-core correction48 was applied to the Cu(I) atom. For all atoms, basis sets of double-ζ plus polarization quality were employed, with a pseudo-atomic orbital energy shift of 25 meV and a grid cutoff of 150 Ry.36 All calculations were performed with the spin-unrestricted approximation and using the generalized gradient approximation functional proposed by Perdew et al.49 This combination of functional, basis sets, and grid parameters was validated for the isolated model systems [Cu(I)(CH3S-)2]-1 and [Cu(I)(CH3S-)3]-2. Relevant geometrical and energetical parameters are reported in Table 1, together with those from previous similar calculations. Because both model systems favor the low-spin configuration with ∆EHS-LS for [Cu(I)(CH3S-)2]-1 equal to 60.26 kcal/mol and EHS-LS for [Cu(I)(CH3S-)3]-2 equal to 43.22 kcal/mol (Table 1) and because the Cu(I) electronic configuration is 3d10 with ∆EHS-LS equal to 179.38 kcal/mol, all QM-MM calculations were performed in the low-spin state. The Cu(I) atom plus the coordinated side chains (CH3S-) of residues Cys12 and Cys15 in Atox1 and Cys13 and Cys16 in CopZ were selected as the QM-subsystem, which is comprised of 11 atoms. Only the Cys were included in the QM subsystem since it was reported that these are the intrinsic Cu(I) coordinating residues.25,40,41 The rest of the protein, counterions, and water molecules were treated classically. We allowed free motion for QM atoms and for the MM atoms located inside a sphere of 12 Å from the QM-subsystem center of mass. The frontier between the QM and the MM portions of the system (Cys CR-Cβ bond) was treated by the SPLAM method.50 The MM subsystem was treated using the Amber9 force field parametrization51 for protein and counterions atoms and TIP3P for water molecules.46 The QM-MM SIESTA implementation showed an excellent performance for medium and large systems and also proved to be appropriate for studying chemical reactivity in biomolecules, in particular, for copper coordinated systems.38 To determine the optimal Cys(S)-Cu-Cys(S) angle configuration in both holo-proteins, restrained energy optimizations along this angle were computed. Obtaining accurate free energy profiles requires extensive sampling, which is computationally very expensive and difficult to achieve at the DFT QM-MM level. For these reasons, potential energy profiles were determined using restrained energy minimizations along the reaction coordinate.37 For this purpose, an additional term was added to the potential energy according to V(ξ) ) k(ξ - ξ0),2 where k is an adjustable force constant, ξ is the value of the reaction coordinate in the system particular configuration, and ξ0 is the

Cu(I) Binding in Copper Chaperones Atox1 and CopZ

J. Phys. Chem. B, Vol. 112, No. 15, 2008 4585

TABLE 1: Structural and Energetic Parameters and Mulliken Charges of [Cu(I)(CH3S-)2]-1 and [Cu(I)(CH3S-)3]-2 Isolated Model Systemsa [Cu(I)(CH3S-)2]-1 our study Cu-S (Å) S-Cu-S (deg) S-S-Cu-S (deg)d qCu (e) qS (e) ∆EHS-LS (kcal/mol)e

ref 55b

[Cu(I)(CH3S-)3]-2

ref 40c

2.16/2.16 177.21

2.12/2.12 179.9

2.11/2.14 166.0

0.116 -0.536/-0.538 60.26

0.150 -0.600

0.074 -0.340/-0.530

ref 35

our study

ref 35

2.23/2.23 180.0

2.28/2.28/2.29 115.95/118.13/125.92 179.42/179.43/179.48 0.326 -0.642/-0.656/-0.670 43.22

2.31/2.35/2.41 114.6

a Results from previous calculations also are shown for comparison.35,40,55 b Charges were calculated using the Merz-Kollman routine followed by a simplex algorithm. c Based on QM-MM calculations of the yeast Cu chaperone Atx1. d S-S-Cu-S: improper torsion that defines the angle between one S-Cu-S plane and remaining Cu-S bond. e ∆EHS-LS: energy difference between high-spin and low-spin configurations.

reference value of the reaction coordinate. By varying ξ0, the system is forced to follow the minimum reaction path along the given coordinate. In this case, the Cys(S)-Cu-Cys(S) angle was changed from 110 to 180°, and the force constant was 40 kcal/(mol deg2), taken from ref 40. To investigate as to whether Met11 is able to coordinate Cu(I) in CopZ, similar QM-MM optimizations were performed including residue 11 as part of the QM subsystem. We also performed QM-MM optimizations on the in silico generated Met11Ala mutant, treating residue 11 classically or as part of the QM subsystem. 2.2. Classical MD Simulations. Protein structures were obtained from the PDB: holo-Atox1: 1TL4,41 holo-Copz: 1K0V,25 apo-Atox1: 1TL5,41 and apo-Copz: 1P8G.52 To perform MD simulations of the holo-proteins, the Cu(I) centers were parametrizated using the QM-MM optimized geometries. For Atox1, the QM-MM optimized Cu(I) geometry is similar to that obtained from the isolated model system, yielding a similar set of parameters. However, because the QMMM optimized Cu(I) geometry differs from the isolated model system in CopZ, a classical parametrization based on this model system geometry would yield unrealistic results. The atomic charges of Cu(I) coordinated to two methylthiolate (CH3S-) groups were determined using restricted electrostatic potential (RESP)53 and HF/6-31G(d) single-point wave functions (qCu(I) ) 0.4124 e and qS ) -0.7525 e for holo-Atox1 and qCu(I) ) 0.3527 e and qS ) -0.7316 e for holo-CopZ) with Gaussian 03, revision D.01,54 following the protocol recommended in the Amber web page (amber.scripps.edu). The equilibrium parameters were taken from the QM-MM optimized structures (Cu-S ) 2.15 Å and S-Cu-S ) 178.6° for holo-Atox1 and Cu-S ) 2.19 Å and S-Cu-S ) 154.4° for holo-CopZ). The van der Waals parameters for Cu(I) were taken from ref 55. The bond and angle force constants involving the Cu(I) atom were taken from ref 40. This combination of parameters was similar to those recently reported.35 Classical MD simulations were performed for the different proteins using Amber9.42-45 The initial structures were generated as described in the previous section for both holo-and apo-forms. Protein atoms were described with the parm99SB force field parametrization.56 In both apo-systems, the relevant Cys residues were simulated in their protonated physiological form. Simulations were performed in the NPT ensemble (constant pressure of 1 atm and temperature of 300 K was maintained using the Berendsen coupling scheme57), employing periodic boundary conditions. A SHAKE algorithm was employed to keep bonds involving hydrogen atoms at their equilibrium length,58 which allowed us to employ a 2 fs time step for the integration of Newton’s equations. The optimized systems were heated to 300 K and equilibrated for 200 ps. The resulting structures were the starting points of the production MD simulations. Atox1

was simulated for 20 ns. To ensure a proper equilibration of the -eight to nine Na+ atoms required to neutralize CopZ, we simulated 100 ns. For consistency, only the last 20 ns was used for data analysis. Previous simulations in the presence of a high number of explicit counterions were shown to be equilibrated after 50 ns.59-63 Root mean square deviations (rmsd), rms fluctuations per residue, protein/solvent radial distribution functions, and radii of gyration were calculated for each of the systems using the ptraj module of Amber9. The entropy change upon Cu(I) binding was estimated by calculating the quasi-harmonic entropy64-66 using the ptraj module of Amber9. In this approach, the atomic fluctuation matrix was calculated as the mass-weighted covariance matrix obtained from the snapshots of the MD simulation. The structures obtained from the simulations were spatially superimposed to a common reference structure to exclude all translational and rotational motions exhibited by the molecule.67 The entropy was estimated every 250 ps from the last 20 ns of the MD simulations. The snapshots were collected every 2.5 ps, yielding a total of 8000 frames. The calculation was performed only for the backbone heavy atoms (C, O, CR, and N) as a reduced representation of the system. This selection avoided estimating the entropy from the fluctuations of atoms within a residue, which would be on different timescales from the important structural changes that would mainly contribute to the entropy.67 Finally, the distance distribution between the S atoms of Cys residues that coordinate Cu(I) was calculated for the last 20 ns of the simulations of both apo-proteins. The distance distribution is the probability of being at a certain S-S distance, or the S-S g(r), and thus, the Helmholtz free energy (A) can be estimated as a function of the S-S distance by ∆A(r) ) -RT ln g(r), yielding a free energy profile or a potential of mean force.68 3. Results and Discussion 3.1. Cu(I) Coordination and Nearby Environment: QMMM Optimizations. The QM-MM optimized geometrical parameters (distances and angles) and Mulliken charges of the Cu(I)-binding center are reported in Table 2. In holo-Atox1, the (Cys)S-Cu distance is 2.15 Å, typical of diagonal Cu,69,70 and agrees with that determined by EXAFS (2.16 Å)71 and the isolated linear model system (Table 1). In holo-CopZ, the (Cys)S-Cu distance is 2.19 Å, which is shorter than that observed by EXAFS in the presence of DTT (2.25 Å).72 This optimized Cu-S distance is between the linear and the trigonal model systems (Table 1). The optimized S-Cu-S angles differ significantly: 178.6 and 154.4° for Atox1 and CopZ, respectively (Figure 2A). This results in a linear Cu(I) coordination in Atox1, in agreement with EXAFS studies71 and the angle of


Rodriguez-Granillo and Wittung-Stafshede

TABLE 2: Relevant Structural Parameters and Mulliken Charges of QM-MM Optimized Holo-Forms of Atox1 and CopZ and Comparison with Experiments (NMR and EXAFS25,41,71,72)a holo-CopZ QM-MM holo-Atox1 QM-MM Cys(S)-Cys(S) (Å) Cys(S)d-Cu (Å) Cys(S)e-Cu (Å) Cys(S)d-Cu-Cys(S)e (deg) Met(S)f-Cu (Å) qCu (e) qSd (e) qSe (e)

4.29 2.15 2.15 178.6 7.76 0.084 -0.616 -0.732

EXAFS/NMRb 2.16 160 ( 25

WT

Met11Ala

MM

QM

MM

QM

4.27 2.19 2.19 154.4 5.70 0.288 -0.804 -0.772

4.25 2.20 2.18 152.3 5.87 0.296 -0.834 -0.774

4.34 2.21 2.21 156.6

4.32 2.21 2.21 156.2

0.306 -0.826 -0.768

0.292 -0.820 -0.750

EXAFS/NMRc 2.25 115 ( 26

a For CopZ, Met11 wild-type (WT) or the Met11Ala mutant were either treated classically (MM) or as part of the QM subsystem (QM). b NMR data from ref 41 and EXAFS data from ref 71. c NMR data from ref 25 and EXAFS data from ref 72. d Cys12 and 13 in Atox1 and CopZ, respectively. e Cys15 and 16 in Atox1 and CopZ, respectively. f Met 10 and Met11 in Atox1 and CopZ, respectively.

160 ( 25° estimated from NMR.41 In CopZ, our results indicate the presence of a distorted linear Cu(I) coordination, whereas EXAFS72 and NMR estimations (115 ( 26°)25 suggested a trigonal coordination. On the basis of these earlier findings, it was proposed that Cu(I) in CopZ has a third non-protein ligand, possibly an exogenous thiol (such as DTT) present in the buffer.72 In vacuum, we found that two methylthiolate groups and a Cu(I) favor a linear coordination (177.21°, Table 1). However, in the protein, the geometry and particular environment can change this preference, and this seems to be the case in CopZ. The fact that CopZ is optimized in a nonlinear coordination in the absence of a third ligand suggests that the protein structure imposes this Cu(I) coordination. The S-Cu-S angle discrepancy between the two holo-forms was further corroborated by performing restrained energy optimization along the Cys(S)-Cu-Cys(S) angle (Figure 2B). By this approach, we found that the optimal angle configuration in CopZ (∼150°) differs from that in Atox1 (∼180°). This observation also suggests that the QM-MM optimized angles are independent of the initial values. This is important as the QM-MM calculations do not sample all energy space but only regions around local minima. The Mulliken charges of Cys(S) and Cu are larger in holoCopZ than in holo-Atox1 (Table 2). Whereas qCu(I) in Atox1 is closer to the value in the linear isolated model, in CopZ, this value is between the linear and the trigonal models (Tables 1 and 2). Thus, the bonds, angles, and charge densities support that Cu(I) in Atox1 is linearly coordinated, whereas the Cu(I) coordination in CopZ lies between that of the linear model and that of the trigonal model. In holo-Atox1, the conserved Met10 is buried, far away from the active site (Table 2), and in close contact with Val40 located in β3 allowing for van der Waals interactions. Both Cys are solvent exposed, and Cys12 forms a hydrogen bond (HB) with Thr11 in loop 1 (H-S ) 3.07 Å and O-H-S ) 173.4°) and one water molecule (H-S ) 2.23 Å and O-H-S ) 169.4°), whereas Cys15 interacts with Lys60 in loop 5 (H-S ) 2.12 Å and N-H-S ) 153.3°) (Figure 2A). In holo-CopZ, Met11 is solvent exposed, closer to the active site (Table 2), and interacts with Cys13. In holo-CopZ, only Cys13 forms HBs with two waters (H-S ) 2.11 and 2.08 Å and O-H-S ) 168.1 and 162.1°). The conserved Tyr65 in loop 5 (corresponding to Lys60 in Atox1) points to the solvent. To further investigate the molecular basis of the particular CopZ Cu(I) coordination and to test as to whether Met11 is capable of binding Cu(I), we included the Met11 side chain as part of the QM subsystem, as opposed to before when we treated

it classically. In this new simulation, the electronic description of Met11 allows Cu(I) binding as a possibility. Furthermore, we mutated in silico this conserved residue to an alanine and analyzed the variant (Met11Ala) classically and as part of the QM subsystem. As shown in Table 2 and Figure 2C, there are no major geometrical or charge density changes between the different models, suggesting that Met11 is not a Cu(I) ligand. When this residue is included in the QM subsystem, instead of moving closer to the Cu(I) center, as expected if this was a Cu(I) ligand, the residue moves further away from the active site (Table 2 and Figure 2C). The S-Cu-S angle does not change significantly between the WT and mutant proteins, suggesting that the particular orientation of Met11 in CopZ is not responsible for the deviation of linearity in the S-Cu-S angle. To gain insight into differences in the active site (i.e., the Cu(I)-binding loop) that may cause a difference in Cu(I) coordination between Atox1 and CopZ, we superimposed the structures and analyzed only the active sites (residues nine to 18 in Atox1 and residues 10-19 in CopZ) (Figure 3). There is clearly a different structural arrangement of this region in the two proteins. This quantitatively can be represented in a Ramachandran plot of the residues in the conserved metalbinding motif (MX1C1X2X3C2). The plot reveals that residues M, C1, X1, and X3 have significantly different torsion angles in the two proteins, allowing for different conformations of this peptide stretch in the proteins. In Atox1, all residues lie within allowed energy regions, whereas in CopZ, S12 and N14 lay within disallowed energy regions. This may suggest that in CopZ, the residues in the conserved motif collectively induce steric hindrance that results in the distorted S-Cu-S angle. Moreover, the Ramachandran plots of the QM-MM optimized structures of CopZ WT and the M11A mutant overlap, suggesting that Met11 is not responsible for the particular loop conformation (data not shown). 3.2. MD Simulations of Apo- and Holo-Proteins. 3.2.1. Backbone Fluctuations. Next, apo- and holo-forms were subjected to classical MD simulations to assess structural dynamics. After 20 ns of MD simulations, both apo- and holo-forms of Atox1 were found to be stable in this time period, as shown by the backbone rmsd time evolution (Figure 4), with mean values and a standard deviation of 1.4 ( 0.1 and 1.2 ( 0.1 Å, respectively, suggesting no major conformational changes. Because of the presence of -eight to nine Na+ ions in CopZ, we simulated 100 ns and analyzed the last 20 ns, to be consistent with the Atox1 results. Holo-CopZ is equilibrated in the first 20 ns, whereas apo-CopZ is equilibrated after approximately



Figure 2. (A) Cu(I) center of the optimized QM-MM structures for holo-Atox1 (left) and holo-CopZ (right). The protein backbone is shown as a ribbon model, the QM subsystem (Cu and Cys) is in CPK, and relevant residues in the MM layer (Met10, Thr11, and Lys60 in Atox1 and Met11 and Tyr65 in CopZ) are shown in stick representation. (B) Restrained energy optimization profile along the Cys(S)-Cu-Cys(S) angle for holoAtox1 (blue) and holo-CopZ (red). The reaction coordinate was varied from 110 to 180°, but only the 130-180° relevant range is shown. (C) Cu(I) center of the optimized QM-MM structures for holo-CopZ WT (left) and Met11Ala mutant (right). The protein backbone is shown as a ribbon model, the QM subsystem (Cu, Cys, and residue 11) is in CPK, and Tyr65 in the MM layer is shown in stick representation.

60 ns (Figure 4 inset). However, data analysis from the first 20 ns (data not shown) and last 20 ns of the 100 ns run yielded qualitatively similar results. In the last 20 ns, both apo- and holo-CopZ were stable with a mean rmsd and standard deviation of 1.3 ( 0.2 and 0.9 ( 0.2 Å, respectively. However, apoCopZ exhibited greater flexibility as seen in the mean rmsd and standard deviation for the whole 100 ns run: 1.9 ( 0.6 and 2.2 ( 0.2 Å for the apo- and holo-forms, respectively. The greater flexibility of apo-CopZ is more likely due to an intrinsic property

of the protein than to a lack of equilibration of the Na+ ions because holo-CopZ already was equilibrated in the first 20 ns. The distribution of structures obtained from the last 20 ns MD simulations was compared to the initial structures by computing the rmsd per residue (Figure 5A). As expected, the R-helices and β-sheets have more restricted backbone motion than less structured regions, such as loops. The Cu(I)-binding loop (residues 10-14 in Atox1 and -nine to 15 in CopZ) is flexible and relatively unstructured in both chaperones, and the



Figure 3. Left: superimposition of the Cu(I)-active site (residues nine to 18 in Atox1 and residues 10-19 in CopZ) of the QM-MM structures for holo-Atox1 (orange) and holo-CopZ (blue). Right: Ramachandran plot for the residues of the Cu(I)-binding motif (MX1C1X2X3C2) of Atox1 (circles) and CopZ (squares). The corresponding residues in Atox1 and CopZ are depicted with the same color.

Figure 4. rmsd (in angstroms, with respect to the first structure) of the backbone heavy atoms (N, CR, and C) as a function of the 20 ns MD production time for Atox1 (left) and CopZ (right) with (red) and without (blue) Cu(I). Inset: rmsd of the 100 ns MD for CopZ.

first Cys (Cys12 in Atox1 and Cys13 in CopZ) is more mobile than the second Cys (Cys15 in Atox1 and Cys16 in CopZ). Upon Cu(I) binding, in both cases, the loop becomes rigid, and both Cys residues show a restricted mobility. There is an overall reduced mobility upon Cu(I) binding that extends throughout the proteins; this effect is most dramatic for CopZ (Figure 5B). The radius of gyration (RG) data are consistent with the trend of both proteins becoming more compact (i.e., more rigid) in the presence of Cu(I), being that the change in CopZ is significantly larger than in Atox1 (Figure 5C). Interestingly, the loop between β3 and R2 in Atox1 and the same loop plus the R2 helix itself in CopZ show opposite trends: they become more dynamic when Cu(I) binds (red portion of Figure 5B). We propose that these regions may specify interfaces for proteinprotein interactions involved in Cu transport processes in bacterial and human cells. The simulation data are in accord with the reported solution NMR structures for both proteins. Major changes in hydrophobic interactions between secondary structure elements as a function of Cu(I) were demonstrated by NMR in both CopZ52 and yeast Atx1.27 Moreover, NMR experiments revealed that, in contrast to the other chaperones, there are only minor alterations in the Atox1 structure upon Cu(I) binding.41 Thus, it is reasonable that

the effects on conformational dynamics and RG due to Cu(I) binding are not as dramatic in Atox1 as in CopZ. In apo-CopZ, Cys16 is positioned on the boarder of loop 1 and R1, while Cys13 is part of this loop. In contrast, in holo-CopZ, R1 is extended so that Cys16 is part of this helix, and Cys13 is now located in the loop R1 border. This structural change due to Cu(I) binding in CopZ was revealed via NMR experiments.52 Our simulations support this trend as apo-CopZ shows a greater flexibility than holo-CopZ. 3.2.2. Entropic Cost of Cu(I) Binding to Atox1 and CopZ. To obtain a quantitative estimation of the effect of protein flexibility upon Cu(I) binding, the quasi-harmonic entropy as a function of the simulation time was calculated for the last 20 ns. The converged entropy values (Figure 6) were used to calculate ∆S, defined as the entropy of the holo-protein minus the entropy of the apo-protein at 300 K (see Computational Methods for details). Consistent with the observations, the change in entropy in both cases is negative, suggesting that the proteins lose vibrational entropy upon Cu(I) binding. For Atox1, T∆S ) -26.8 kcal/mol, whereas for CopZ, T∆S ) -44.3 kcal/ mol. These values suggest that CopZ becomes more ordered in the presence of Cu(I) than does Atox1. The greater reduction in internal motion of CopZ upon Cu(I) binding appears to



Figure 5. (A) Average fluctuations (rmsd in angstroms) of backbone heavy atoms (N, CR, and C) per residue (with respect to the first structure) for Atox1 (left) and CopZ (right) with (red) and without (blue) Cu(I). The secondary structure elements are indicated (R1, R2, and β1-β4). (B) ∆rmsd (holo-minus apo) is mapped in the apo-PDB structures (arbitrary scale), where the red color shows an increased disorder when Cu(I) binds, and the other colors show various levels of decreased disorder upon Cu(I) binding, with blue indicating the most rigid areas. (C) Histograms of the RG distribution for Atox1 (left) and CopZ (right) with (red) and without (blue) Cu(I).

originate from the higher flexibility of the apo-form as compared to apo-Atox1. 3.2.3. Protein-SolVent and Protein-Protein Interactions as a Function of Cu(I) Coordination. To determine the average proximity to solvent molecules of the Cys and Met residues in the metal-binding loop, protein-solvent radial distribution

functions were calculated (Figure 7). In addition, solvent HB networks for the same residues were studied from protein structures throughout the simulation. The HBs that were maintained through the production MD simulation were analyzed at the end of the simulation. In general, Cys(S) HBs are weaker in the apo-proteins than in the holo-proteins because



Figure 6. Entropy (cal/mol/K) as a function of simulation time (ns) for Atox1 (left) and CopZ (right) with (red) and without (blue) Cu(I).

Figure 7. Protein-solvent radial distribution functions g(r) of Atox1 (left) and CopZ (right) of the two Cys and Met residues of the metal-binding loop, with and without Cu(I). Dark blue, apo-1st Cys; red, apo-2nd Cys; green, apo-Met; light blue, holo-1st Cys; yellow, holo-2nd Cys; and purple, holo-Met.

the S partial charge of Cys is less negative in the apo-form than in the holo-form (parametrized qS ) -0.31 e for apo and -0.75 e and -0.73 e for holo-Atox1 and CopZ, respectively). In Atox1, Cys12 is solvent exposed regardless of the presence of Cu(I) (as seen in the holo-QM-MM optimization). However, it forms a weak HB with a water in the apo-form, whereas it forms a strong such bond in the holo-form (H-S ) 2.20 Å and O-H-S ) 174.7°). Cys15 is more buried in the apo-form although still capable of forming a weak HB with water. Upon Cu(I) binding, Cys15 becomes more solvent exposed and now forms a strong HB with the solvent (H-S ) 1.92 Å and O-H-S ) 162.1°). As expected, and because it is part of the hydrophobic core, Met10 is completely buried regardless of the presence of the metal and makes a stable hydrophobic contact with Val40 (as seen in the holo-QM-MM optimization). Exchange of this Met to a Ser in an earlier 5 ns MD simulation was found to have a large effect on the core conformational dynamics.34 The corresponding residues in CopZ show a somewhat different pattern. Notably, Met11 is entirely solvent exposed in the apo-form, although it is not able to form HBs because carbon divalent S is a poor HB acceptor.73,74 Upon Cu(I) binding, Met11 becomes completely buried and maintains hydrophobic contacts with Ile60 located in R2. This is in contrast to what is seen in the NMR structure, where Met11 points toward the metal and interacts hydrophobically with Tyr65.25 In the absence of Cu(I), Met11 appears to move around freely toward the solvent, but in the presence of Cu(I), it is forced to adopt a defined

position into the hydrophobic core. In the simulation for the apo-form of CopZ, Cys16 is more buried than Cys13, although it still is accessible to the solvent, and both form weak HBs with water. In the NMR structure of apo-CopZ, Cys16 is completely buried and interacts with Leu37 in loop 3,25 but this interaction is not maintained in our MD simulation, and both residues (Cys16 and Leu37) flip toward the solvent during the MD run. Upon Cu(I) binding, both Cys residues become completely solvent exposed and form strong HBs with the solvent (H-S ) 2.10 and 2.12 Å and O-H-S ) 168.2 and 165.8° for Cys13 and Cys16, respectively). In holo-Atox1, Lys60 fluctuates near the metal-binding site, and forms HBs alternatively with Cys15 and Thr11. It has been proposed that the proximity of Lys60 to the Cu(I) site serves to stabilize the -1 net charge in holo-Atox1.41 In the apo-form, this interaction is not maintained and Lys60 shows an increased conformational flexibility. The corresponding residue in CopZ, Tyr65, is solvent-exposed throughout the simulation in both apoand holo- forms, as also found in the apo-NMR structure25 and in our holo- QM-MM optimization. 3.2.4. Cys(S)-Cys(S) Distance Distribution in the Apo-Forms. Finally, the distance distribution between the S atoms of Cys12 and Cys15 in apo-Atox1 and Cys13 and Cys16 in apo-CopZ was calculated for the last 20 ns of the simulations (Figure 8). In both proteins, there were mainly two populations of Cys(S)-Cys(S) distances, although there was a third one that also was visited in Atox1 but with a small probability. In general,



Figure 8. Left: histograms of the S-S distance distribution (in angstroms) between the two Cys residues in the Cu(I)-binding loop in the apoforms of Atox1 (blue) and CopZ (red). Right: free energy as a function of the Cys(S)-Cys(S) distance for apo-Atox1 (blue) and apo-CopZ (red). Inset: Cu-binding loop conformation of apo-Atox1 at the two minima (Cys shown in stick representation).

the S-S distance in CopZ was shorter. However, the shorter S-S distance population in both proteins centered between 4.5 and 5.0 Å, whereas the longer S-S distance population was found at ∼8 Å for apo-Atox1 but at ∼7 Å for apo-CopZ. If we assume that in the last 20 ns of simulation the interconversion between the conformations was sampled enough, this distance distribution is the probability of being at a certain S-S distance, or the S-S g(r), and thus, the free energy can be readily estimated as a function of the S-S distance (see Computational Methods) (Figure 8). In both cases, the energy profile is smooth, and the activation energy between the minima is low (between 0.5 and 1 kcal/mol), allowing for rapid interconversion between the two configurations. The lowest minimum is that for the short S-S distance (i.e., 4.5-5 Å). Since this distance is close to the sum of the parametrized S-Cu distances in the holo-forms, it appears that in this apo-conformation, Cu(I) could become incorporated without much perturbation. 3.3. Biological Relevance of in Silico Results. The computational data obtained here correlate well with the recently reported in vitro stability data.32 The higher conformational dynamics (i.e., rmsd and RG) found for apo-CopZ as compared to apo-Atox1 explains as to why apo-CopZ is significantly less stable than Atox1 toward thermal perturbation in vitro (i.e., Tm value of 45 vs 68 °C for apo-forms at pH 732). Moreover, the fact that CopZ is stabilized by ∼10 °C by Cu(I) coordination whereas the thermal curves are only shifted by a few degrees for apo- and holo-Atox1 (pH 7) is also in agreement with the in silico differences between apo- and holo-forms. The rmsd, RG, as well as entropic cost data all demonstrated larger effects for Cu(I) binding to CopZ than for Cu(I) binding to Atox1. We found several differences between the two proteins that cannot easily be detected by experimental methods. The position and interactions of the conserved Met (Met10 in Atox1 and Met11 in CopZ) residue differ between the two proteins. Whereas this Met is solvent exposed in apo-CopZ, it is always buried in Atox1. Most importantly, the QM-MM optimized Cu(I) geometry differs between CopZ and Atox1. Whereas Cu(I) favors a linear coordination in Atox1, it adopts a distorted linear coordination in CopZ. Notably, QM-MM optimization of the Cu(I) site in yeast Atx1 resulted in a Cys(S)-Cu-Cys(S) angle intermediate between those found here for Atox1 and CopZ of 166°.40 We propose that structural constraints within CopZ are responsible for this Cu(I) geometry, as the vacuum optimized structure for [Cu(I)(CH3S-)2]-1 is linear. This distortion is probably not due to the peculiar conformation of Met11 in CopZ

since mutation of this residue to an Ala followed by QM-MM optimization yielded similar results as for the WT. The overall arrangement of the conserved metal-binding motif in CopZ likely is due to protein steric effects within this region that deviate the S-Cu-S angle from linearity, as evidenced by the different backbone torsion angles for these residues between the proteins. We speculate that this distortion predisposes the Cu to readily pick up a third ligand, which may be a small thiol compound from the solvent (like DTT in vitro or glutathione in vivo) or Cys-S from a target metal-binding domain. Moreover, this distorted coordination may favor CopZ dimerization in solution19 as opposed to Atox1.41 Dimerization of CopZ may serve as an energetic compensation to overcome the high entropic cost of Cu(I) binding. The structural and dynamic differences observed between Atox1 and CopZ may be related to unique aspects of the bacterial versus the human cellular environments and their respective physiological partner proteins. Despite the divergences noted previously, there are trends that are similar between Atox1 and CopZ. We propose that these similarities represent features that may be shared by Cu chaperones in all organisms. For both Atox1 and CopZ, although the Cu(I) site is located in a loop at the protein surface, Cu(I) binding rigidifies and shrinks the whole protein. Interestingly, the loop between β3 and R2, which is located at the opposite side of the Cu(I)-binding loop in the protein, becomes more floppy upon binding of Cu(I) in both proteins. It is tempting to speculate that this region defines the interface for proteinprotein interactions in Cu transfer pathways that may be shared between bacterial and human cells. These structural and dynamic differences between apo- and holo-forms may provide a mechanism for in vivo target recognition. Specifically, chaperone plasticity may be required to form a transient Cu-bridged hetero complex.31 Whereas the chaperone holo-structure may interact favorably with the target protein, upon Cu release, the apo-conformation may prefer the dissociated state. Interestingly, we found that both apo-proteins exhibit biphasic distributions of Cys(S)-Cys(S) distances. One may speculate that the stretched structure (corresponding to the longer S-S distances) facilitates Cu uptake via a pull-in-type mechanism (while returning to the shorter S-S distances, which have the lowest energy). The heterogeneity in terms of S-S distances also may be a key to Cu transfer: to accommodate transient structures in the chaperone-target hetero complex, the chaperone may need to extend one of its Cys(S) residues (thereby adopting the stretched conformation) into the target’s Cu site. Future QM-

4592 J. Phys. Chem. B, Vol. 112, No. 15, 2008 MD computations on Cu-bridged hetero complexes will test this hypothesis directly. 4. Conclusion In this work, we reported the geometrical parameters of Cu(I) sites in two homologous Cu chaperones, human Atox1 and bacterial CopZ, using QM-MM methods. To assess conformational dynamics for the apo- and holo-forms, we also performed classical MD simulations. Surprisingly, despite the same ligands, we discovered that the optimized Cu(I) geometries differ between the two proteins. Both proteins become rigidified and more compact in the presence of Cu(I), although Cu(I) binding to CopZ has the largest effect. The exact solvent distributions around the Cu(I) sites depend on the presence of the metal or not, as well as on which protein is analyzed. We discovered a biphasic distribution of Cys(S)-Cys(S) distances in the apoforms that were separated by 0.5-1 kcal/mol barriers; we propose that conformations with long Cys(S)-Cys(S) distances play key roles in Cu uptake and release in this group of proteins. Acknowledgment. Dr. Alejandro Crespo is thanked for useful discussions. Support for this project was provided by the Robert A. Welch Foundation (C-1588) and the USAMRAA (Concept Award W81XWH-06-1-0572). References and Notes (1) O’Halloran, T. V.; Culotta, V. C. J. Biol. Chem. 2000, 275, 25057. (2) Huffman, D. L.; O’Halloran, T. V. Annu. ReV. Biochem. 2001, 70, 677. (3) Puig, S.; Rees, E. M.; Thiele, D. J. Structure 2002, 10, 1292. (4) Puig, S.; Thiele, D. J. Curr. Opin. Chem. Biol. 2002, 6, 171. (5) Harris, E. D. Crit. ReV. Clin. Lab Sci. 2003, 40, 547. (6) Kulkarni, P. P.; She, Y. M.; Smith, S. D.; Roberts, E. A.; Sarkar, B. Chemistry 2006, 12, 2410. (7) Lamb, A. L.; Wernimont, A. K.; Pufahl, R. A.; Culotta, V. C.; O’Halloran, T. V.; Rosenzweig, A. C. Nat. Struct. Biol. 1999, 6, 724. (8) Lamb, A. L.; Torres, A. S.; O’Halloran, T. V.; Rosenzweig, A. C. Nat. Struct. Biol. 2001, 8, 751. (9) Harrison, M. D.; Jones, C. E.; Solioz, M.; Dameron, C. T. Trends Biochem. Sci. 2000, 25, 29. (10) Hamza, I.; Schaefer, M.; Klomp, L. W.; Gitlin, J. D. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 13363. (11) Hung, I. H.; Casareno, R. L.; Labesse, G.; Mathews, F. S.; Gitlin, J. D. J. Biol. Chem. 1998, 273, 1749. (12) Klomp, L. W.; Lin, S. J.; Yuan, D. S.; Klausner, R. D.; Culotta, V. C.; Gitlin, J. D. J. Biol. Chem. 1997, 272, 9221. (13) Hung, I. H.; Suzuki, M.; Yamaguchi, Y.; Yuan, D. S.; Klausner, R. D.; Gitlin, J. D. J. Biol. Chem. 1997, 272, 21461. (14) Hellman, N. E.; Kono, S.; Mancini, G. M.; Hoogeboom, A. J.; De Jong, G. J.; Gitlin, J. D. J. Biol. Chem. 2002, 277, 46632. (15) Gitlin, J. D. Gastroenterology 2003, 125, 1868. (16) Tao, T. Y.; Gitlin, J. D. Hepatology 2003, 37, 1241. (17) Miyajima, H.; Takahashi, Y.; Kamata, T.; Shimizu, H.; Sakai, N.; Gitlin, J. D. Ann. Neurol. 1997, 41, 404. (18) Cobine, P.; Wickramasinghe, W. A.; Harrison, M. D.; Weber, T.; Solioz, M.; Dameron, C. T. FEBS Lett. 1999, 445, 27. (19) Kihlken, M. A.; Leech, A. P.; Le Brun, N. E. Biochem. J. 2002, 368, 729. (20) Solioz, M.; Stoyanov, J. V. FEMS Microbiol. ReV. 2003, 27, 183. (21) Arnesano, F.; Banci, L.; Bertini, I.; Ciofi-Baffoni, S.; Molteni, E.; Huffman, D. L.; O’Halloran, T. V. Genome Res. 2002, 12, 255. (22) Rosenzweig, A. C.; Huffman, D. L.; Hou, M. Y.; Wernimont, A. K.; Pufahl, R. A.; O’Halloran, T. V. Struct. Fold Des. 1999, 7, 605. (23) DeSilva, T. M.; Veglia, G.; Opella, S. J. Proteins 2005, 61, 1038. (24) Banci, L.; Bertini, I.; Ciofi-Baffoni, S.; Huffman, D. L.; O’Halloran, T. V. J. Biol. Chem. 2001, 276, 8415. (25) Banci, L.; Bertini, I.; Del Conte, R.; Markey, J.; Ruiz-Duenas, F. J. Biochemistry 2001, 40, 15660. (26) Pufahl, R. A.; Singer, C. P.; Peariso, K. L.; Lin, S. J.; Schmidt, P. J.; Fahrni, C. J.; Culotta, V. C.; Penner-Hahn, J. E.; O’Halloran, T. V. Science (Washington, DC, U.S.) 1997, 278, 853. (27) Arnesano, F.; Banci, L.; Bertini, I.; Huffman, D. L.; O’Halloran, T. V. Biochemistry 2001, 40, 1528. (28) Banci, L.; Bertini, I.; Ciofi-Baffoni, S.; Gonnelli, L.; Su, X. C. J. Mol. Biol. 2003, 331, 473.

Rodriguez-Granillo and Wittung-Stafshede (29) Achila, D.; Banci, L.; Bertini, I.; Bunce, J.; Ciofi-Baffoni, S.; Huffman, D. L. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 5729. (30) Wernimont, A. K.; Yatsunyk, L. A.; Rosenzweig, A. C. J. Biol. Chem. 2004, 279, 12269. (31) Banci, L.; Bertini, I.; Cantini, F.; Felli, I. C.; Gonnelli, L.; Hadjiliadis, N.; Pierattelli, R.; Rosato, A.; Voulgaris, P. Nat. Chem. Biol. 2006, 2, 367. (32) Hussain, F.; Wittung-Stafshede, P. Biochim. Biophys. Acta 2007, 1774, 1316. (33) Banci, L. Curr. Opin. Chem. Biol. 2003, 7, 143. (34) Poger, D.; Fuchs, J. F.; Nedev, H.; Ferrand, M.; Crouzy, S. FEBS Lett. 2005, 579, 5287. (35) Holt, B. T.; Merz, K. M., Jr. Biochemistry 2007, 46, 8816. (36) Soler, J. M.; Artacho, E.; Gale, J. D.; Garcia, A.; Junquera, J.; Ordejon, P.; Sanchez-Portal, D. J. Phys.: Condens. Matter 2002, 14, 2745. (37) Crespo, A.; Scherlis, D. A.; Marti, M. A.; Ordejo´n, P.; Roitberg, A. E.; Estrin, D. A. J. Phys. Chem. B 2003, 107, 13728. (38) Crespo, A.; Marti, M. A.; Roitberg, A. E.; Amzel, L. M.; Estrin, D. A. J. Am. Chem. Soc. 2006, 128, 12817. (39) Crespo, A.; Marti, M. A.; Kalko, S. G.; Morreale, A.; Orozco, M.; Gelpi, J. L.; Luque, F. J.; Estrin, D. A. J. Am. Chem. Soc. 2005, 127, 4433. (40) Dalosto, S. D. J. Phys. Chem. B 2007, 111, 2932. (41) Anastassopoulou, I.; Banci, L.; Bertini, I.; Cantini, F.; Katsari, E.; Rosato, A. Biochemistry 2004, 43, 13046. (42) Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R.; Merz, K. M.; Ferguson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A. J. Am. Chem. Soc. 1995, 117, 5179. (43) Pearlman, D. A.; Case, D. A.; Caldwell, J. W.; Ross, W. S.; Cheatham, T. E., III; DeBolt, S.; Ferguson, D.; Seibel, G.; Kollman, P. Comput. Phys. Commun. 1995, 91, 1. (44) Case, D. A.; Cheatham, T. E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K. M.; Onufriev, A., Jr.; Simmerling, C.; Wang, B.; Woods, R. J. J. Comput. Chem. 2005, 26, 1668. (45) Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Merz, K. M.; Pearlman, D. A.; Crowley, M.; Walker, R. C.; Zhang, W.; Wang, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Wong, K. F.; Paesani, F.; Wu, X.; Brozell, S.; Tsui, V.; Gohlke, H.; Yang, L.; Tan, C.; Mongan, J.; Hornak, V.; Cui, G.; Beroza, P.; Mathews, D. H.; Schafmeister, C.; Ross, W. S.; Kollman, P. A. Amber9; University of California, San Francisco: San Francisco, 2006. (46) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926. (47) Troullier, N.; Martins, J. L. Phys. ReV. B: Condens. Matter Mater. Phys. 1991, 43, 1993. (48) Louie, S. G.; Froyen, S.; Cohen, M. L. Phys. ReV. B: Condens. Matter Mater. Phys. 1982, 26, 1738. (49) Perdew, J. P.; Burke, K.; Ernzerhof, M. Phys. ReV. Lett. 1996, 77, 3865. (50) Eichinger, M.; Tavan, P.; Hutter, J.; Parrinello, M. J. Chem. Phys. 1999, 110, 10452. (51) Wang, J.; Cieplak, P.; Kollman, P. A. J. Comput. Chem. 2000, 21, 1049. (52) Banci, L.; Bertini, I.; Del Conte, R. Biochemistry 2003, 42, 13422. (53) Bayly, C. I.; Cieplak, P.; Cornell, W. D.; Kollman, P. A. J. Phys. Chem. 1993, 97, 10269. (54) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A., Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian 03, revision D.01; Gaussian, Inc.: Pittsburgh, PA, 2004. (55) Fuchs, J. F.; Nedev, H.; Poger, D.; Ferrand, M.; Brenner, V.; Dognon, J. P.; Crouzy, S. J. Comput. Chem. 2006, 27, 837. (56) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Proteins 2006, 65, 712. (57) Berendsen, H. J.; Postma, J. P.; van Gunsteren, W. F.; Di Nola, A.; Haak, J. R. J. Chem. Phys. 1984, 81, 3684. (58) Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. J. C. J. Comput. Phys. 1977, 23, 327. (59) Partay, L. B.; Jedlovszky, P.; Sega, M. J. Phys. Chem. B 2007, 111, 9886.

Cu(I) Binding in Copper Chaperones Atox1 and CopZ (60) Pande, V.; Nilsson, L. Nucleic Acids Res. 2008, submitted. (61) Mukhopadhyay, P.; Monticelli, L.; Tieleman, D. P. Biophys. J. 2004, 86, 1601. (62) Walton, E. B.; Vanvliet, K. J. Phys. ReV. E: Stat., Nonlinear, Soft Matter Phys. 2006, 74, 61901. (63) Fogolari, F.; Moroni, E.; Wojciechowski, M.; Baginski, M.; Ragona, L.; Molinari, H. Proteins 2005, 59, 91. (64) Schlitter, J. Chem. Phys. Lett. 1993, 215, 617. (65) Andricioaei, I.; Karplus, M. J. Chem. Phys. 2001, 115, 6289. (66) Brooks, B. R.; Janei, D.; Karplus, M. J. Comput. Chem. 2004, 16, 1522. (67) Dixit, S. B.; Andrews, D. Q.; Beveridge, D. L. Biophys. J. 2005, 88, 3147.

J. Phys. Chem. B, Vol. 112, No. 15, 2008 4593 (68) Leach, A. R. Molecular Modeling; Addison Wesley Longman: New York, 1997. (69) Ralle, M.; Cooper, M. J.; Lutsenko, S.; Blackburn, N. J. J. Am. Chem. Soc. 1998, 120, 13525. (70) Pickering, I. J.; George, G. N.; Dameron, C. T.; Kurz, B.; Winge, D. R.; Dance, I. G. J. Am. Chem. Soc. 1993, 115, 9498. (71) Ralle, M.; Lutsenko, S.; Blackburn, N. J. J. Biol. Chem. 2003, 278, 23163. (72) Banci, L.; Bertini, I.; Del Conte, R.; Mangani, S.; Meyer-Klaucke, W. Biochemistry 2003, 42, 2467. (73) Gregoret, L. M.; Rader, S. D.; Fletterick, R. J.; Cohen, F. E. Proteins 1991, 9, 99. (74) Wierzejewska, M.; Saldyka, M. Chem. Phys. Lett. 2004, 391, 143.

Binding in Copper Chaperones Atox1 and CopZ - American Chemical

Recommend Documents