Probing Amyloid-Beta Fibril Stability by Increasing Ionic Strengths

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Probing Amyloid-Beta Fibril Stability by Increasing Ionic Strengths Jernej Zidar and Franci Merzel* National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia ABSTRACT: Previous experimental studies have demonstrated changing the ionic strength of the solvent to have a great impact on the mechanism of aggregation of amyloid-beta (Aβ) protein leading to distinct fibril morphology at high and low ionic strength. Here, we use molecular dynamics simulations to elucidate the ionic strength-dependent effects on the structure and dynamics of the model Aβ fibril. The change in ionic strength was brought forth by varying the NaCl concentration in the environment surrounding the Aβ fibril. Comparison of the calculated vibrational spectra of Aβ derived from 40 ns all-atom molecular dynamics simulations at different ionic strength reveals the fibril structure to be stiffer with increasing ionic strength. This finding is further corroborated by the calculation of the stretching force constants. Decomposition of binding and dynamical properties into contributions from different structural segments indicates the elongation of the fibril at low ionic strength is most likely promoted by hydrogen bonding between N-terminal parts of the fibril, whereas aggregation at higher ionic strength is suggested to be driven by the hydrophobic interaction.

’ INTRODUCTION Amyloid and other threadlike aggregates have been the focus of intense research over the last years because of their importance in understanding amyloid diseases1,2 as well as their possible applications in nanotechnology.3 In particular, the onset of Alzheimer disease (Aβ) has been shown to be associated with the deposition of the amyloid-beta protein (Aβ) aggregates, called senile plaques, in the localized regions of the brain.4,5 The Aβ protein is a 39-42 amino acid polypeptide, which is derived from amyloid precursor protein.6 Aggregation propensity and stability of oligomeric and fibrillar forms of Aβ are aspects of fundamental importance to biophysics and medicine as well. For example, fibril destabilization is suggested as one of the potential therapeutic strategies.7 Despite the progress in experimental characterization of the nanoscale properties of amyloid fibrils, including their dependence on external parameters,8-10 a detailed knowledge of how the solution parameters influence amyloid structure and dynamics is still lacking. Moreover, description of these phenomena on a molecular level is incomplete.11 Aggregation of Aβ is not only affected by hydrophobic interactions but also by electrostatic forces. Electrostatic interactions are known to play a dominant role in the early stages of the aggregation.12 For example, occasionally aggregation may be reduced by introducing point mutations that increase charge repulsion between the protein molecules at low ionic strength.13 Protein complexes and aggregates stabilized mainly by electrostatic interactions become rapidly weakened with increasing salt concentration because protein charges become neutralized by counterions. However, proteins with large net charges may be often stabilized by salts that suppress the intramolecular charge repulsion. In turn, studies probing the stability of amyloid fibrils can reveal which noncovalent interactions are important for the formation and maintenance of the fibril structure.14 r 2011 American Chemical Society

Previous experimental studies have demonstrated that an increase of the salt concentration with a concomitant increase in ionic strength greatly impacts the mechanism of Aβ aggregation.9,10 Different fibril morphologies were observed at high or low ionic strength conditions by fluorescence and atomic-force microscopy measurements but have not yet been elucidated by atomic detail simulations. Though the current state of the art does not allow the process of amyloid fiber growth to be captured by all-atom simulations, such an approach can nevertheless be used to elucidate the principles responsible for the process of amyloid growth on the longer time-scale. Here, we use all-atom molecular dynamics simulations in explicit solvent to study the behavior of a model amyloid fibril in environments containing different molar concentrations of NaCl. We assume that by adjusting the number of randomly distributed ions in the simulation cell we can reliably mimic the corresponding ionic strength of the solvent when the system reaches a thermodynamic equilibrium. Our goal is to analyze structural and dynamical properties of the Aβ fibril depending on ionic strength of the solvent thus revealing the propensity for amyloid fibril growth. We compare vibrational density of states of Aβ at different ionic strengths and focus on the low-frequency region of the calculated spectra, which is responsible for the mechanistic properties of the system. Further, we developed a means to characterize the solvent-exposed surface of the fibril and a method to derive fibril stretching force constants.

’ METHODS Preparation and Molecular Dynamics (MD) Simulations. Following other simulation studies15,16 our choice of an initial Aβ Received: September 21, 2010 Revised: January 19, 2011 Published: February 17, 2011 2075

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Figure 1. Side view of the Aβ fibril: (a) structural view, (b) schematic view.

Figure 2. Top view of the layer containing chains A and B in the Aβ fibril: (a) structural view, (b) schematic view. Key: green, hydrophobic residues; red, charged residues; blue, polar residues.

fibril model was the model proposed by Petkova et al.,17 which is based on experimental solid-state NMR data. The model is composed of six layers each containing two amyloid protein chains interacting via their respective C-terminal portions. The model is shown in Figures 1 and 2. Each individual Aβ protein chain contains 32 amino-acid residues, corresponding to Aβ9-40. Using the Internet portal CHARMMing,18 the PDB file was converted and prepared for simulations using the CHARMM19,20 molecular modeling package together with the available all-atom force field, CHARMM22.21 The amyloid fibril model was solvated with TIP3P22,23 water molecules by submerging it in a hexagonal cell with the initial side of 80 Å and the height set to 56 Å. Water molecules overlapping with the amyloid fibril’s atoms or closer than 2.5 Å to the amyloid fibril were deleted. Overall, in the simulation cell the fibril was surrounded with a layer of water molecules that was thicker than 5.0 Å. At the end of the preparation phase, Naþ and Cl- ions were added to the solvent by randomly replacing some water molecules with either a Naþ ion or a Cl- ion while taking into account the desired salt concentration. Five solvated amyloid fibril systems in environments containing the following molar concentrations of NaCl: 0.0, 0.077, 0.154, 0.231, and 0.308 were constructed. Note that a 0.154 M concentration of NaCl corresponds to the physiological concentration. Thermodynamical equilibrium of each system was achieved by carrying out minimization of the initial structure using the Steepest Descents and Adopted-base Newton-Raphson algorithms followed by four cycles of Verlet (NVE) and constant pressure and temperature (CPT) molecular dynamics simulations.20 Initially the protein part of the system was fixed, whereas the unit cell was allowed to adopt its optimal volume. Then, the restraints fixing the fibril structure were released and the whole system was again minimized and equlibrated by running four cycles of NVE and CPT MD simulations. Finally, a 10 ns CPT simulation was used to further equilibrate each system. Although the particle mesh Ewald24 is the preferred way to treat long-range electrostatic interactions it was not used here

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since the overall charge of the solvated system had a nonzero value (-12 au). Also, the counterions, usually, for example, Naþ or Cl-, added to neutralize the system would interfere with the ionic strength-dependent effects we were studying. The nonbonded interactions were treated using a set of cutoffs. Distance cutoff in generating the list of pairs was set to 12.0 Å. At 11.0 Å, the switching function eliminated all contributions to the overall energy from pairwise interactions. At 10.0 Å, the smoothing function began to reduce a pair’s contribution. The CPT MD production run for each system was 40 ns long. During the simulations data were collected and trajectories were saved every 2 ps. The SHAKE19,20 algorithm was used to fix lengths of the covalent bonds involving hydrogen atoms, which allows the integration time step to be 2 fs. The temperature of the system was set to 300 K. Simulations were run at constant pressure with the piston mass equal to 500 amu and the reference pressure of 1 atm. All protein units (Aβ chains) were allowed to move freely during the MD production run. All simulations were ran at the CROW25 computer cluster located at National Institute of Chemistry in Ljubljana, Slovenia. The simulated systems were visualized using Visual Molecular Dynamics.26 Vibrational Density of States g(ω) and Vibrational Entropy. It is common to analyze molecular vibrations when studying internal dynamics of molecular systems. We were interested in the vibrational density of states g(ω), which is usually extracted from the shape of the potential energy surface of the atomic system using normal-mode analysis as a frequency distribution function of vibrational modes.27 In this case, vibrational modes are represented as eigenvectors of the Hessian matrix, which is the matrix of mass-weighted second derivatives of potential energy with respect to atomic displacements. An alternative way to derive g(ω) is to apply the quasiharmonic approximation (QHA) on the MD trajectory of the system.28 In QHA, the fluctuations observed during the system motions are described by a multivariate Gaussian probability distribution. Using this assumption, a temperature-dependent effective potential resulting in a Gaussian probability distribution can be defined. The quasiharmonic modes can then be computed from the mass-weighted Hessian matrix defined in terms of its elements as kBT/σij, where kB is the Boltzmann constant and T is the absolute temperature. Consequently, σij are the elements of the covariance matrix. Those elements are defined as dynamicsdependent variances (diagonal elements) and covariances (offdiagonal elements) of the fluctuations of the Cartesian coordinates, ð1Þ σij ¼ Æðxi - Æxi æÞðxj - Æxj æÞæ that are obtained from MD simulation and Ææ denoting time average over MD trajectory. Solving the secular equation kB T 1=2 1=2 det M σM - 2 I ¼ 0 ð2Þ ω where M is the mass matrix and I is the identical matrix, one obtains the quasi-harmonic frequencies ωi, which are used to compute vibrational density of states: D 1 X δ0 ðω - ωi Þ ð3Þ gðωÞ ¼ DΔω i ¼ 1 where D is the total number of degrees of freedom, that is, D = 3N (N is the number of atoms exposed to analysis), Δω is the width 2076

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of the sampling interval, and ( 1 if - Δω=2 e ω - ωi < Δω=2 0 δ ðω - ωi Þ ¼ 0 otherwise To decompose the total g(ω) into contributions originating from different parts of the system, it is convenient to define the partial atomic density of states gR(ω), referring to the individual atomic contribution. Using weighting by the square of the corresponding atomic displacements, partial atomic density of states is given by: D 1 X jB e i ðRÞj2 δ 0 ðω - ωi Þ ð4Þ gRðωÞ ¼ DΔω i¼7

where B e i(R) is a 3D vector, with the x, y, and z components of the atom R displacement within the normal mode i: B e i(R) = (eix(R), eiy(R)eiz(R)). Note that we skipped the first 6 modes in eq 4, which correspond to translational and rotational motion of the system. To asses the relative flexibility of the system under different conditions, it is useful to compare its vibrational entropies,29,30 provided we know the vibrational properties of the system. A stiffer system will be characterized by lower vibrational entropy relative to a softer system. If we want to calculate vibrational entropy for a given part of the molecule, we define the partial vibrational entropy for a list of atoms, that is, {R1, R2}: 2 0 1 R2 Z ¥ 6 C X 1 6 pω B B C B C SfR1 , R2 g ¼ kB D dωgR ðωÞ6 4kB T @ A pω R ¼ R1 0 exp -1 kB T

#

pω - ln 1 - exp kB T

ð5Þ

where gR(ω) is the partial atomic density of states. Accordingly, the value of entropy per atom within selected part of a molecule would be given by the simple normalization: 1 s¼ sfR , R g ð6Þ NR2 - NR1 1 2 Amyloid Fibril Stretching Force Constants. If an arbitrary object is displaced from its equilibrium position, then the resilience of its environment is reflected by the force exerted on that object: the smaller the force, the softer the environment. The resultant force can be expressed in terms of a force constant attributable to its environment. When discussing the stability of the Aβ fibril, the force constant characterizing the stretching motion of the fibril along the fibril axis (axis perpendicular to the six layers) is of prime interest. As the stability of the system is intimately related to its dynamics we shall be able to derive corresponding force-constants from system dynamics. Using coarse-graining of the allatom model, we represent each of the 6 layers by a dimensionless bead located in the mass center of the corresponding layer. Then we analyze the motion of the beads along the fibril axis in terms of bead displacements from their average positions, which we project onto the corresponding covariance matrix σ0 as defined by eq 1. The matrix σ0 represents the 1D ball-and-spring model of Aβ fibril composed of 6 beads. By definition, the force constant matrix K is given by the inverse of the covariance matrix σ0

K ¼ kB Tσ 0-1

ð7Þ

Figure 3. Schematic view of a molecule superimposed on the uniform grid.

Instead of directly evaluating eq 7, we use the eigenvalue decomposition method, also proposed by Moritsugu et al.,31 to calculate individual force constants: 6 X Kk ¼ kB T Eki λi - 1 E ki ð8Þ i¼2

where i runs over all modes omitting translation of the entire system, E is the eigenvector matrix E=(eB1, B e 2, ... B e 6), and λi is the ith eigenvalue of σ0 . Characterization of the Fibril Surface. As the physical properties of the fibril surface are expected to vary with the changing ionic strength of the solvent, it might be convenient to provide a basis for their cumulative description. For example, the net electrostatic potential caused by protein atoms at a given distance from the protein surface atoms could serve as a probe for the characterization of hydrophobicity of the fibril surface. The easiest approach to determine the surface of a fibril is to map the fibril structures onto a uniform 3D grid and attributing to each grid point the shortest distance to the fibril as depicted in Figure 3. Let us assume a 3D grid with the spacing d between the neighboring grid points. We can select grid points B r i(D) that are located at distances (D - d/2, D þ d/2) from the nearest fibril atoms. If D is chosen to be larger than the longest bond between two bonded fibril atoms, then, in principle, all grid points B r i(D) lie in the solvent phase. The selected grid points can thus be thought to form reference points of the fibril envelope at distance D from the fibril. If the number of selected grid points satisfying the distance criterium is NS, the surface of the envelope S can be estimated to be S = ∼NSd2. The formula is a good approximation of the envelope surface if d,F, where F is the minimum curvature radius of the surface. In addition to the fibril surface, we can define the average electrostatic potential US(D) at a distance D, and also the average absolute surface potential at distance D, US(D): NS X NF ej 1 X , US ðDÞ ¼ NS i ¼ 1 j ¼ 1 4πε0 j B r i ðDÞ - B r jj NS X NF ej 1 X ð9Þ US ðDÞ ¼ NS i ¼ 1 j ¼ 1 4πε0 j B r i ðDÞ - B r j j and the sum over j runs over all NF fibril atoms. Here, ej is atomic r i(D) denotes a partial charge, B r j is atomic position vector, and B 2077



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Table 1. Fibril Stretching Force Constants K Obtained from a 1D Coarse-Grained Model Using eq 8; Δz is the Average Distance between Two Neighboring Fibril Layers cNaCl [M]

Δz [Å]

κ [N/m]

0.000

5.15 ( 0.07

127.1 ( 30.0

0.077

5.07 ( 0.06

144.2 ( 21.2

0.154

5.02 ( 0.09

172.3 ( 25.9

0.231

4.93 ( 0.05

187.8 ( 27.2

0.308

4.89 ( 0.06

204.9 ( 31.1

Figure 4. Vibrational density of states, g(ω), obtained from the QHA of the CR motions of Aβ fibril at different ionic strength.

grid point at distance D from the fibril. The quantity US(D) accounts for the sum of the absolute values of electrostatic potential caused by the fibril atoms, that is, by ignoring the presign of the corresponding point charge: contributions from negatively and positively charged atoms are not weighted oppositely. This way US(D) is a direct measure for the presence of any charged atoms/polar groups at the surface of the fibril. Higher values of US are thus indicative of a more hydrophilic fibril surface, whereas lower values of U are characteristic of a more hydrophobic fibril surface. Hydrogen Bonding Analysis. Hydrogen bonding analysis was done using geometry criteria as implemented in the molecular modeling package CHARMM. The in-built routine looks at every possible hydrogen bond donors and acceptors and selects the valid pairs. The selection of valid pairs is determined by length of the hydrogen bond, where 2.4 Å is the default value.32 The angle between hydrogen bond donors and acceptors is not evaluated. The output of the analysis gives the average number of hydrogen bonds, NHB. By definition, NHB of a given donor/ acceptor is the number of HB formed by this atom summed over all trajectory frames and divided by the number of frames. Hydrogen bonding patterns were first determined for the outermost layers, containing chains A, B, K, L (Figure 1) and the internal layers that contain the rest of the amyloid fibril chains. Later, we determined the hydrogen bonding patterns between each layer of the amyloid fibril as well.

’ RESULTS All-atom molecular dynamics simulations of a solvated amyloid fibril model in aqueous solution of 0.0 M, 0.077 M, 0.154 M, 0.231, and 0.308 M of NaCl were used to elucidate the structural and dynamical response of the amyloid fibril upon changing the ionic strength. Quasi-harmonic analysis was performed on 40 ns long trajectories taking into account the dynamics of CR atoms. For each system, the density of states was calculated using eq 1. The results are shown in Figure 4. Whereas the low-frequency modes describe the motion of the whole Aβ units, motions corresponding to the frequency range of 600 to 800 cm-1 present coupled motion of CR atoms to the Aβ backbone. We observe a slight shift of the whole spectrum to the right toward higher values of ω

Figure 5. Calculated vibrational entropy of the fibril as a function of NaCl concentration, CNaCl, using eq 5.

with increasing ionic strength. This means that low-frequency dynamics of the fibril is softer in conditions of low ionic strength and stiffer at conditions of high ionic strength. To check whether 40 ns MD simulations are long enough to provide reliable information of the low-frequency dynamics of Aβ and their dependence on ionic strength of the environment, we carried out two separate QHA on each system: using the first 30 ns of the trajectory and last 30 ns, respectively. The observed trend of the gradual stiffening with increasing ionic strength was clearly reproduced in both cases proving the system to be in thermodynamic equilibrium. A direct measure of the fibril’s stiffness is obtained by calculating the fibril stretching force constants. Using a bead representation of the fibril described in the Methods section and eq 8, we calculated individual force constants Kk along fibril axis for each bead. The overall fibril P stretching force constant is obtained as an average κ = 1/4 5k = 2Kk. The results in Table1 indicate a significant stiffening of the fibril (increasing force constants) with increasing ionic strength. The stiffening of the fibril with increasing ionic concentration is associated by a gradual decrease of the average distance between the fibril surfaces, Δz, as can be seen in the second column of the Table 1. Plotting the total vibrational entropy of the fibril calculated using eq 5 as a function of salt concentration in Figure 5 shows a clear decrease of vibrational entropy with increasing salt concentration, consequently with increasing ionic strength. This means the system has less configurational freedom at high ionic strength, consistent with the stiffer structure demonstrated also by larger force constants. 2078



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Table 2. Average Entropy Per Residue s in kcal/mol/K from Different Regions of Aβ Peptide: N-terminal (Residues: 9-22), Loop (Residues: 23-29), and C-terminal (Residues: 30-40)

Figure 6. Surface properties of the fibril: a) average number of hydrogen bonds between fibril and solvent, b) sum of the absolute values of electrostatic potential due to the protein atoms, US(D), c) fibril envelope surface estimate (Methods section) and d) the cumulative electrostatic potential, US.

One of the reasons for the observed ionic strength-dependent stiffening of the fibril is to be sought in the increase of the surface tension of the aqueous solution of salt. According to literature data,33 the surface tension for aqueous solution-vapor interface ranges linearly from 72.75 10-3 N/m at 0.0 M NaCl to 73.38 10-3 N/m at 0.308 M NaCl, corresponding to the relative increase of 1%. This is much smaller than the relative change of the calculated vibrational entropy or the relative change of the stretching force constants of the fibril upon increasing the salt concentration. Thus, the increased surface tension of the solvent and its direct influence to the fibril structure cannot reproduce the whole scale of the stiffening effect of the fibril. However, it should be noted that adding ions to a solution decreases the effective concentration of water, that is, the number of water molecules that are able to interact with polar side chains in the amyloid fibril is decreased. Moreover, due to the presence of ions, electrostatic interactions between charged side chains on the fibril are screened, which leads to modified interaction patterns within the fibril itself. Because the fibril-solvent and intrafibril interactions are disturbed by increasing ion concentration (ionic strength), it is possible for fibril side chains to interact with each other in a different way, which might in turn lead to additional contributions to the increased stiffness of the fibril and to the observed decrease in vibrational entropy. The effect of ionic strength on the properties of the fibril surface and its interaction with the solvent is shown in parts a-d of Figure 6 as a function of salt concentration. The fibril envelope at distance D = 3 Å from the fibril atoms has been calculated using a grid with spacing d = 1 Å. As observed in part c of Figure 6, the overall size of the envelope is clearly decreasing with increasing ionic strength. We may explain this by packing of the solvent-exposed side chains away from the solvent into the fibril interior (burial of the side chains). The effect is further corroborated with decrease of the average number of HBs (part a of Figure 6) and with decrease of US(D) (part b of Figure 6). According to these results, the fibril surface is clearly getting more hydrophobic with increasing ionic strength. Consequently, the fibrils at high ionic strength will be prone to interact with each other via hydrophobic interaction. Therefore, the growth of the fibril at high ionic strength is expected to be driven by hydrophobic interaction.

cNaCl [M]

s N-term

s loop

s C-term

0.000

0.091

0.094

0.099

0.077

0.088

0.091

0.097

0.154

0.087

0.090

0.094

0.231

0.086

0.087

0.092

0.308

0.086

0.086

0.090

The cumulative electrostatic potential US(D) at D = 3 Å from the fibril as a function of salt concentration in part d of Figure 6 displays two regimes: it is decreasing at low concentrations (0.00.154 M) and increasing at high concentrations (0.154-0.308 M). We explain this as follows. At 0.0 M NaCl, both negatively and positively charged side chains are optimally solvated and therefore exposed to the solvent. The potential is negative because of the negative net-charge of the fibril. The initial drop of US(D) with the increasing salt concentration is related to the fact that the balance between positively and negatively charged side chains contributing both to the potential is lost due to preferable packing of the positively charged side chains into the fibril interior. This leads to domination of the negative charge, that is, to a drop in US(D). Further increases of ionic strength cause the negatively charged side chains to pack to the fibril interior as well, consequently leading to less negative values of US(D). To enforce the relevance of our approach of calculating the fibril envelope and hydrophobic character of the surface, we calculated the solvent accessible surface area (SASA) using the built-in routine in CHARMM and setting the radius of the test-sphere equal to 3 Å. For increasing salt concentration we obtain SASA of 16 835, 16 601, 16 576, 16 186, and 15 877 Å2, showing exactly the same trend as results shown in part c of Figure 6. The agreement of the scale between both surface estimates would be clearly better if we assume that each box on the grid (Figure 3) contributes more than just one face (d2) to the envelope, that is ∼4. By comparing the values of vibrational entropy for different parts of the Aβ chain and from environments with different ionic strength shown in Table 2 it is clear the C-terminal part has always the highest vibrational entropy. Thus, in fibrilogenesis the contact via the C-terminal regions of the amyloid protein chains is entropically always favored over contacts via other regions of the protein. A similar finding was reported by Buchete and Hummer,35 who have shown that formation of the C-terminal β sheets is crucial to the stability of Aβ fibrils as well as to their dissociation and elongation. Figure 7 depicts the difference between the average number of hydrogen bonds for external (i.e., containing chains A and B, K, and L) and internal dimeric layers at different ionic strength. By varying the time frame of analysis from one-half to whole trajectory the corresponding values of NHB change by less than 1%, so the values can be assumed to converge well. The difference between the two is more pronounced for systems in environments with low ionic strength. We assume that the energy difference on binding of one layer of Aβ at an axial end of the fibril is proportional to the change in the average number of HB a layer forms with its environment, when one surface of the layer switches its contact from aqueous solution to protein. Such a difference is accounted for when comparing the total average number of HB between external and internal layer of Aβ at the 2079



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Our results suggest the mechanism of Aβ aggregation is the following: at conditions of low ionic strength, the electrostatic repulsion between the Aβ monomer and the rest of the fibril allows for possible polymerization, that is, the addition of a new Aβ monomer unit, to occur only at the axial ends of the fibrils, ultimately leading to formation of long and thin fibrils. At conditions of high ionic strength, the bonding between the monomeric unit of Aβ is driven by hydrophobic interaction, which is less specific and occurs both at the axial ends and the lateral sides of the fibril. This leads to short and thick fibrils. The proposed mechanism of Aβ aggregation is consistent with experimental data on different fibril morphologies observed at low and high ionic strength conditions.

’ AUTHOR INFORMATION Corresponding Author Figure 7. Average number of hydrogen bonds of external versus internal layer of Aβ dimer with the surrounding including solvent and protein environment.

same conditions. Because the number of HB in the internal layer is greater than that of the external layer, regardless of the surrounding environment, it is always energetically favorable for an Aβ chain to be buried and not exposed to the solvent. Moreover, the energy gain at Aβ layer binding to one of the axial fibril ends is significantly higher at conditions of low ionic strength compared to higher ionic strengths. This in turn means the binding at the axial ends of the fibril is dominating in conditions of low ionic strength potentially leading to a selective growth of the fibril. Consequently, the elongation process could be driven by hydrogen bonding. In contrast to this observation, our results in parts a-d of Figure 6 reveal that the fibril growth mechanism at high ionic strength is much less specific and can occur at either the axial or the lateral sides of the amyloid fibril due to hydrophobic interaction. The ionic strength-dependent mode of Aβ binding we observed would likely lead to amyloid fibrils with two distinct morphologies, a finding well in agreement with previous experimental data.9,10,34 Further experimental evidence of ionic strength-dependent effects on the Alzheimer’s disease-related peptides is given by Johansson et al.36 They found that at increased ionic strength protofibril fibrillization is driven by hydrophobic interactions.

’ CONCLUSIONS In the present study, we used molecular dynamics simulations of the model amyloid fibril to provide an atomic-detail analysis of its structural and dynamical response to environments containing progressively higher ionic strength. We calculated vibrational spectra, vibrational entropies, and stretching force constants of the fibril, which allowed us to demonstrate the fibril to be stiffer with increasing ionic strength. We have shown that increasing ionic strength of the solvent promotes the conformational changes of Aβ, which expose the hydrophobic residues of Aβ causing the fibril surface to become more hydrophobic. Therefore, the fibrils at high ionic strength are expected to interact with each other mainly via hydrophobic interaction. The differences in hydrogen bonding between external and internal dimeric layers at different ionic strengths show that the energy gain from binding a new layer to the axial fibril ends is significantly greater at conditions of low ionic strength, which suggests a possible driving force of fibril elongation.

*E-mail: [email protected].

’ ACKNOWLEDGMENT The financial support through grants P1-0002 and P1-0207 of the Ministry of Higher Education, Science, and Technology of Slovenia and the EN-FIST Center of Excellence is gratefully acknowledged. We thank Prof. R. Tycko for kindly providing the structure of Aβ fibril. ’ REFERENCES (1) Chiti, F.; Dobson, C. M. Annu. Rev. Biochem. 2006, 75, 333–366. (2) Self-Assembling Peptide Systems in Biology, Medicine and Engineering; Aggeli, A., Boden, N., Zhang, S., Eds.; Kluwer Academic: Dordrecht, The Netherlands, 2001. (3) Hartgerink, J. D.; Beniash, E.; Stupp, S. I. Science 2001, 294, 1684–1688. (4) Hardy, J.; Higgins, G. A. Science 1992, 256, 184–185. (5) Hardy, J.; Selkoe, D. J. Science 2002, 297, 353–356. (6) Kitaguchi, N.; Takahashi, Y.; Tokushima, Y.; Shiojiri, S.; Ito, H. Nature 1988, 331, 530–532. (7) Lemkul, J. A.; Bevan, D. R. Biochemistry 2010, 49, 3935–3946. (8) Smith, J. F.; Knowles, T. P. J.; Dobson, C. M.; MacPhee, C. E.; Welland, M. E. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 15806–15811. (9) Stine, W. B.; Dahlgren, K. N.; Krafft, G. A.; LaDu, M. J. J. Biol. Chem. 2003, 278, 11612–11622. (10) Nichols, M. R.; Moss, M. A.; Reed, D. K.; Lin, W. L.; Mukhopadhyay, R.; Hoh, J. H.; Rosenberry, T. L. Biochemistry 2002, 41, 6115–6127. (11) Ma, B.; Nussinov, R. Curr. Opin. Struct. Biol. 2006, 10, 445–452. (12) Yun, S. J.; Urbanc, B.; Cruz, L.; Bitan, G.; Teplow, D. B.; Stanley, H. E. Biophys. J. 2007, 92, 4064–4077. (13) Altobelli, G.; Nacheva, G.; Todorova, K.; Ivanov, I.; Karshikoff, A. Proteins-Struct. Funct. Gen. 2001, 43, 125–133. (14) Knowles, T. P.; Fitzpatrick, A. W.; Meehan, S.; Mott, H. R.; Vendruscolo, M.; Dobson, C. M.; Welland, M. E. Science 2007, 318, 1900–1903. (15) Takeda, T.; Klimov, D. K. Biophys. J. 2009, 96, 442–452. (16) Takeda, T.; Klimov, D. K. J. Phys. Chem. B 2009, 113, 6692– 6702. (17) Petkova, A. T.; Yau, W.-M.; Tycko, R. Biochemistry 2006, 45, 498–512. (18) Miller, B. T.; Singh, R. P.; Klauda, J. B.; Hodoscek, M.; Brooks, B. R.; Woodcock, H. L. CHARMMing: A New, Flexible Web Portal for CHARMM. J. Chem. Inf. Model. 2008, 48 (9), 1920–1929. (19) Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. J. Comput. Chem. 1983, 4, 187–217. (20) Brooks, B. R.; Iii, C. L. B.; A., D. M., Jr; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; 2080



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