In Silico Study of Recognition between Aβ40 and Aβ40 Fibril Surfaces

Research Article Cite This: ACS Chem. Neurosci. 2018, 9, 935−944

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In Silico Study of Recognition between Aβ40 and Aβ40 Fibril Surfaces: An N‑Terminal Helical Recognition Motif and Its Implications for Inhibitor Design Xuehan Jiang,‡ Yang Cao,‡ and Wei Han* Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China

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S Supporting Information *

ABSTRACT: The recent finding that the surface of amyloid-β (Aβ) fibril can recruit Aβ peptides and convert them into toxic oligomers has rendered fibril surfaces attractive as inhibition targets. Through extensive simulations with hybrid-resolution and all-atom models, we have investigated how Aβ1−40 recognizes its own fibril surfaces. These calculations give a ∼2.6−5.6 μM half-saturation concentration of Aβ on the surface (cf. experimental value ∼6 μM). Aβ was found to preferentially bind to region 16−24 of Aβ40 fibrils through both electrostatic and van der Waals forces. Both terminal regions of Aβ contribute significantly to binding energetics. A helical binding pose of the N-terminal region of Aβ (Aβ3−14) not seen before is highly preferred on the fibril surface. Aβ3−14 in a helical form can arrange side chains with similar properties on the same sides of the helix and maximize complementary interactions with side chain arrays characteristic of amyloid fibrils. Helix formation on a fibril surface implies a helix-mediated mechanism for Aβ oligomerization catalyzed by fibrils. We propose an Aβ3−14 analogue that can exhibit enhanced helical character and interactions with Aβ fibrils and may thus be used as a template with which to pursue potent inhibitors of Aβ−fibril interactions. KEYWORDS: Alzheimer’s disease, amyloid-beta peptide, protein−protein interaction, molecular dynamics simulation, free energy calculation, binding affinity

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INTRODUCTION Aggregation of amyloid-β (Aβ) peptides into β-sheet rich amyloid structures is a hallmark of Alzheimer’s disease. The process of Aβ aggregation is sensitive to various environmental conditions such as pH, temperature, and the presence of surfaces,1 and a growing number of surface types are known to change the course of Aβ aggregation.2−5 Common examples of such surfaces include water−air interface, self-assembled monolayers, and membranes with varying lipid compositions.2−5 Depending on their chemical nature, these surfaces can either expedite or impede the aggregation. It has been suggested that the impact of the surfaces on Aβ aggregation may originate in their ability to modulate structural properties of bound Aβ, enriching or eliminating certain structural elements of Aβ that are required by the aggregation process.2−5 Recent studies have shown that the surface of Aβ fibrils can also recruit Aβ monomers from solution and catalyze their conversion into aggregation-prone oligomers.6−10 This process, termed secondary nucleation, is much more efficient than homogeneous nucleation in solution in production of arguably the most toxic form of Aβ, and it has therefore been regarded as an attractive inhibition target.11 Multiple small molecules,12,13 a molecular chaperone,10 and several antibodies14 have been found to compete with Aβ for the fibril surface, intervening © 2017 American Chemical Society

particularly in the secondary nucleation of Aβ. Despite these advances, little is known about the molecular basis of recognition of Aβ by its fibril surface. Questions as to whether the recognition process relies on specific structural motifs of Aβ peptides and their specific interactions with the fibril surface remain unanswered.15 In particular, as the surface of Aβ fibrils is composed mainly of one-dimensional arrays formed by the same type of amino acid side chains,16 it is crucial to determine how this unique structural feature governs the recognition process.17 Addressing these questions would provide valuable guidance for inhibitor design. Experimental structural characterization of binding between Aβ and fibrils, however, remains difficult. Molecular dynamics (MD) simulations are capable of revealing atomic details that often cannot be observed directly by experiments. In a previous MD study, the binding of Aβ42 peptides to Aβ42 fibrils was investigated using microsecond conventional simulations,18 and this shed light on the general role of hydrophobic interactions in the binding. However, the detailed mechanism of the recognition process between Aβ and Received: September 15, 2017 Accepted: December 27, 2017 Published: December 27, 2017 935

DOI: 10.1021/acschemneuro.7b00359 ACS Chem. Neurosci. 2018, 9, 935−944

Research Article

ACS Chemical Neuroscience

Figure 1. (a) Illustration of simulation system. (b) Distribution of (x,y) positions of centroids of Aβ bound to the fibril. (c) Interaction energies between Aβ and regions 16−24 and 29−36 of the fibril. (d) Interaction energies between different parts of Aβ and the fibril.

fibrils and the structural elements essential for this process have been largely unaddressed. The main technical challenge arises from the computational cost associated with exploration of a vast conformational space of bound Aβ whose structural transitions are hindered by large transition barriers. Our previous study of structures of Aβ bound at fibril tips showed that both enhanced sampling techniques and sub-millisecond simulations are necessary for sufficient sampling of bound structures.19 In the present study, we combined replica exchange molecular dynamics (REMD) sampling techniques20,21 with a hybrid-resolution force field termed PACE to simulate the recognition of Aβ40 by its fibril.16 PACE uses a simplified representation of solvent but retains atomic details of proteins.22 It has been applied to simulate protein folding,22,23 to predict structures of aggregation-prone peptides,19,24,25 and in investigation of fibril growth.19 With this approach, we are able to examine the binding equilibrium between Aβ40 and its fibrils through hundreds of microseconds of hybrid-resolution simulations and tens of microseconds of all-atom simulations. These simulations reproduce accurately the binding thermodynamics observed in experiments9 but also reveal the molecular details of Aβ−fibril interactions. In particular, we identified a helical motif of Aβ40 that can recognize the fibril surface through a special pattern of interactions. We further show that this helical motif could be used to construct potent peptidebased inhibitors of Aβ−fibril interactions. The findings reported here will be invaluable for understanding Aβ oligomerization catalyzed by amyloid fibril surfaces and for targeted inhibition of this process.

unbound and bound configurations sampled from simulations at low temperatures can be fitted to the two-state model described in Models and Methods. The correlation coefficient R2 of the fitting is ∼0.91 (Figure S2a in SI). The standard binding enthalpy and entropy, according to the fitted model, were calculated to be −65 ± 7 kJ mol−1 and −105 ± 27 J mol−1 K−1, respectively. This model also predicts a standard binding free energy of ΔG° = −33 ± 1 kJ mol−1 at 310 K. According to the Langmuir absorption isotherm, this affinity indicates that the surface is half saturated with Aβ40 at peptide concentrations of 2.6−5.6 μM. This result agrees reasonably well with the reported value (∼6 μM) measured at the same temperature through kinetic experiments of Aβ40 aggregation.9 Figure 1b shows the probability distribution of the centroids of Aβ40 around the fibril. There appear to be two Aβ binding regions on the Aβ40 fibril, one located at region 16−24 in the N-terminal β-sheet of the fibril and the other at region 29−36 in the C-terminal β-sheet region. The probabilities of finding Aβ40 peptides in contact with these two regions are ∼87% and ∼13%, respectively. Interaction energy analysis reveals that both regions stabilize the bound Aβ40 mainly through van der Waals (vdW) forces (Figure 1c). Region 29−36 forms a slightly stronger vdW interaction with the bound Aβ40 than does region 16−24. Region 16−24, however, harbors the exposed side chains of K16 and E22 and can also interact electrostatically with the bound Aβ40. Overall, the binding between region 16− 24 and the bound peptide is energetically more favorable. Hence, the combination of favorable electrostatic and vdW interactions renders this region the major Aβ binding site on Aβ40 fibrils. A further decomposition of interactions between different regions of bound Aβ and region 16−24 of the fibril revealed that while all parts of Aβ40 participate in direct interaction with the fibril, its N- and C-terminal segments interact with the fibril more strongly than does its central segment (Figure 1d). The interaction energies of the N- and C-terminal segments of Aβ are both approximately −110 kJ mol−1, about twice of that of the central segment. Notably, the N-terminal segment contributes the most to electrostatic interactions with the fibril. The foregoing observations regarding the binding between Aβ40 and its fibril arose from the simulations containing only a single filament of Aβ40 fibrils. In the experimental structures, Aβ40 fibrils are actually formed by a bundle of two or three filaments (Figure S3a).26,27 In the present study, a single filament rather than a filament bundle was investigated because simulations of the latter system, even with our efficient

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RESULTS AND DISCUSSION We first conducted extensive simulations of the binding of Aβ40 to its fibrils. A single filament of the experimentally determined structure of Aβ40 fibrils (PDB code 2lmn) was used as the fibril template (see Models and Methods). Using periodic boundary conditions (see Models and Methods), we rendered the fibril template devoid of any fibril tips that may otherwise attract Aβ peptides, complicating the interpretation of simulation results regarding the binding of Aβ on the fibril surface, as was shown previously.18 To explore the complicated conformational space associated with Aβ binding, we employed REMD techniques to accelerate sampling (see Models and Methods). The sampling of binding equilibria between Aβ40 and its fibril was achieved through 64 1.4 μs replica simulations conducted at temperatures of 330−690 K (Figure S1a). The population ratios of 936

DOI: 10.1021/acschemneuro.7b00359 ACS Chem. Neurosci. 2018, 9, 935−944

Research Article

ACS Chemical Neuroscience

Figure 2. Formation of the N-terminal helix in Aβ40 when bound to fibrils. (a) Residual helicity of Aβ40 bound to region 16−24 in fibrils (upper panel) and the corresponding helicity change compared to that of the isolated monomer (lower panel). (b) Percentage populations of the top 10 conformers of segments 3−14, 15−28, and 29−40 of bound Aβ40. (c) Probability of side chain contacts between segment 3−14 and the fibril when the N-terminal helix arises in bound Aβ40. (d) Representative structure of the most populated conformer of segment 3−14 viewed from side (top) and top (bottom) of the fibril. The fibril and segment 3−14 of bound Aβ40 are shown in cyan and purple ribbons, respectively.

calculation, the side chain array of K16 appears essential for the binding of Aβ40 to its fibrils. Hence, the interaction of Aβ peptides with the N-terminal β-sheet of Aβ42 fibrils may not be as strong as that observed for Aβ40 fibrils in the present study. Furthermore, with two more hydrophobic residues at the Cterminus of Aβ42, the C-terminal β-sheet of Aβ42 fibrils could also form extra interactions with Aβ compared to the same region of Aβ40 fibrils. Together, this study and our study imply that Aβ40 and Aβ42 fibrils may exhibit different regional selectivity of Aβ binding. Next, we analyzed the structures of Aβ bound to region 16− 24 of the Aβ40 fibril. That Aβ is more extended on the fibril surface than in solution (Figure S4 in SI) is consistent with previous studies of binding of peptides to several types of surfaces.30−32 Apart from its extension, the bound Aβ40 exhibits also a lower β-sheet content (∼14%) than an unbound Aβ40 (∼23%), indicating that β-sheet structures become less favorable when bound Aβ40 is stretched by the fibril surface. Conversely, the α-helical content of Aβ40 increases from ∼11% to ∼19% upon the Aβ40 binding. In particular, a significant (20−35%) increase in helical character was observed for residues 3−14 in bound Aβ40 (Figure 2a). We also examined conformations of different parts of Aβ40 bound to region 16−24 of the Aβ40 fibril. A clustering analysis of segment 3−14 at the N-terminus was performed using a Cα distance cutoff of 1 Å. In the most abundant (∼25%) conformer, segment 3−14 assumes a full helical structure (Figure 2b); other conformers of this segment are populated no more than 6%. The same clustering analysis for segments 15− 28 and 29−40, on the other hand, failed to reveal any single conformer as dominant as the one observed for segment 3−14, indicating that these parts of Aβ40, when bound to the fibril, are structurally more disordered. Thus, the N-terminal helix could represent an important structural element for the binding of Aβ40 on the fibril surface.

computational approach, are still computationally too demanding for equilibrium sampling of the binding between Aβ40 and its fibrils. However, certain surfaces that are buried in the filament bundle now become exposed in a single filament. Any observation of the binding of Aβ40 on this surface of the single filament would thus be irrelevant. To test the relevance of our results, we compared the accessible surface areas of each amino acid of Aβ in a single filament with those associated with various known fibril structures. As shown in Figure S3b, region 29−36 becomes much less accessible in the filament bundles than in the single filament, which is consistent with the fact that in all the fibril structures, this region is involved in the interface between the filaments (Figure S3a). Nonetheless, according to our calculations (Figure 1b), region 29−36 harbors only minor Aβ binding sites. Conversely, the major binding sites contained in region 16−24 remain accessible both in a single filament and in various filament bundles. Hence, the conclusions regarding Aβ40 binding in region 16−24 of a single filament may also be extended to more realistic Aβ40 fibril systems. It should also be noted that the fibril template examined here contains only residues 10−40 of Aβ40. The first nine residues from the N-terminus have been shown by experiments to be essentially unstructured and are thus missing in most of the known structural models of Aβ40 fibrils.16,28 Consideration of these unstructured parts in our system will drastically increase the conformational space to be explored and thereby become computationally impractical. It remains an open question as to how the unstructured N-terminal part of Aβ40 could affect the association of fibril filaments with their binding partners. In the previous simulation study, Aβ42 was found to bind preferentially to the C-terminal β-sheet of Aβ42 fibrils.18 The construction of the fibril template used in that study was based on an experimentally determined structure of Aβ42 fibrils that contains only residues 17−42.29 The first 16 residues from the N-terminus, including K16, are disordered and not involved in any fibrillar structures.29 On the other hand, according to our 937

DOI: 10.1021/acschemneuro.7b00359 ACS Chem. Neurosci. 2018, 9, 935−944

Research Article

ACS Chemical Neuroscience Although Aβ is generally believed to be unstructured in solution,33 there has been much experimental evidence that supports the existence of a helix at the N-terminal part of Aβ.34−37 The studies with solution nuclear magnetic resonance (NMR) spectroscopy have revealed an N-terminal helical conformation of Aβ in apolar environments.34,38 More direct evidence arises from recent crystallography studies in which atomic structures of Aβ40 in complexes with antibody 3D6 and bapineuzumab (humanized 3D6) were resolved.36,37 In these complexes, region 1−7 of Aβ40 adopts a helical conformation and binds to a cleft arranged by the complementarity determining regions of the antibodies. Together, these studies suggested that helical conformations could be a subpopulation of the N-terminus of Aβ in solution and can be recognized by certain binding partners of Aβ.37 Consistent with this notion, our results showed that the helical content of Aβ40 in region 3− 14 is less than 10% in solution but increases by about three times when Aβ binds to the fibril (Figure 2a). To understand how formation of the N-terminal helix of Aβ is promoted by the Aβ40 fibril surface, we evaluated the probabilities of side chain contacts between residues 3−14 of Aβ and the fibril. Snapshots containing an N-terminal helix in bound Aβ were analyzed (see SI), and as shown in Figure 2c, the side chains of E3, H6, Y10, and H13 were found rarely to contact the fibril surface, while the side chains of the other amino acids frequently (>60%) enjoy contacts with the fibril. This indicates that the fibril selectively binds to certain faces of this N-terminal helix. Inspection of the structural details of the representative helical binding pose (Figure 2d) reveals that the N-terminal helix is aligned parallel to the fibril axis. The negatively charged side chains of D7 and E11 on one side of the helix interact with the K16 array of the fibril and the positively charged side chain of R5 on the opposite side of the helix interacts with the E22 array of the fibril. The side chains of F4, S8, and V12 form contacts with the V18/F20 arrays of the fibril. Overall, this contact pattern permits the N-terminal helix to interact favorably with all four arrays that comprise the major Aβ binding sites. The stabilization of helical structures by the fibril surface was further validated through multiple 100 ns simulations with atomistic force fields, which account for all atomic details of systems and are arguably more accurate than the PACE model employed. Through these simulations, we examined the stability of the helical structures of segment 3−14 of Aβ40 (Aβ3−14) in solution or in the complex with the Aβ40 fibril. To ensure the robustness of the test, we employed three independently developed and widely used atomic force fields (OPLS-AA, AMBER99SB-ILDN, and CHARMM36).39−41 All the simulations showed consistently that the helical conformation of Aβ3−14 is indeed significantly more stable on the fibril surface than in solution (Figure 3a). In addition, the interaction pattern between the helical segment and the fibril is similar to that observed in the PACE simulations (Figure S5 in SI). Additional all-atom simulations of several mutations in Aβ3−14 were performed to assess further the importance of the special interaction pattern discussed above with respect to the stability of the observed helical structure (Figure 3b). The importance of electrostatic interactions was first tested through a R5E/E11R mutant in which mismatched electrostatic contacts between the peptide and the fibril were introduced. Simulations showed that the helical structure bound to the fibril is crippled by this mutation. We next tested if the helical

Figure 3. (a) Comparison by all-atom simulations of helical content of Aβ3−14 in the presence (+) and absence (−) of fibrils. (b) Helical contents of Aβ3−14 mutants from simulations with the OPLS/AA force field. These data are compared with those of WT (means, dashed line; std, shade). For all the comparison, the statistical significance of difference was tested by the Mann−Whitney method. Shown above each bar is the corresponding p-value (*p-value 35 Å. In addition, the initial conformation of Aβ40 was drawn at random from the equilibrium ensemble of Aβ40. During the REMD simulations, harmonic restraints were applied to fix the positions of backbone atoms of the fibril and side chain atoms buried inside of the fibril. The force constant of the restraints is 5000 kJ mol−1 nm−2. Similarly, the binding of the designed peptide to the fibril was also simulated. The details of the simulations are shown in Table 1. The convergence of the REMD simulations was monitored with the binding affinities calculated from every 100 ns time period of the simulations at 330 K. As shown in Figure S1 in the Supporting Information (SI), the simulation convergence was achieved after t = 1 and 0.7 μs for the systems containing Aβ40 and the designed peptide, respectively, and the results after these points were used in the analysis. The binding of the designed peptide to the fibril was also simulated using three different atomistic force fields, including the OPLS-AA force filed with TIP4P water model,39,61 the AMBER99SB-ildn force field40 with the TIP4P-Ew water model62 and the CHARMM36 force field41 with the TIP3P water model.63 For computational efficiency, a smaller box containing a fibril model with eight peptide units, was used. The REMD simulation comprises 72 replicas conducted at temperatures of 310−480 K (Table 1). Simulations of Helical Structures of Bound and Unbound Aβ3−14 and Its Variants with All-Atom Models. The initial structures of all-atom simulations of the bound helix were taken from the representative frame of the REMD simulation with PACE in which segment 3−14 of Aβ40 is in a helical structure. This frame was obtained as described in the section below. Only segment 3−14 of Aβ40 was retained, and the N- and C-termini of the segment were capped with an acetyl group (Ac) and an N-methyl amide group (NMe), respectively. The representative helical structure of Aβ3−14 was also used to start all-atom simulations of an unbound helix. The simulations of the bound and unbound systems were performed with the three different atomistic force fields mentioned earlier. The systems of Aβ3−14 variants were constructed based on the helical structure of Aβ3−14. The mutated side chains were introduced with PyMol.64 All the variants were modeled with the OPLS-AA force field with the TIP4P water model. The systems were first energy-minimized and then relaxed through 100 ps simulations at 310 K and 1 atm with the positions of backbone atoms restrained. The force constant of the restraining force is 5000 kJ mol−1 nm−2. The simulations were further extended in the absence of

the restraints for another 100 ns. For each of the systems examined, at least six independent simulations with different initial atomic velocities were carried out to improve the statistics of results (Tables 1 and S1 in SI). Simulation Setup. For all the simulations conducted with PACE, nonbonded interactions were truncated at 12 Å and smoothed with a switching function.22 A time step was set to 3.5 fs, a typical value of PACE simulations.22 The Nose−Hoover thermostat65,66 and Berendsen barostat67 were used to maintain the temperature and pressure of systems, respectively. In REMD simulations, the attempts to exchange coordinates between replicas were made every 3.5 ps. The acceptance ratios of the REMD simulations performed in the present study are all >30%. For all of the all-atom simulations, nonbonded interactions were truncated at 10 Å, and long-range electrostatic interactions were treated with the PME method.68 Temperature and pressure were maintained with the v-rescale algorithm69 and the Berendsen algorithm,67 respectively. A time step of 2 fs was used. All bonds were constrained at their equilibrium lengths using the LINCS algorithm.70 In REMD simulations, the exchange between replicas was attempted every 2 ps. The exchange ratio was on average over 30%. Calculation of Free Energy Change upon the Binding of Peptides to Fibrils. The standard free energy change (ΔG°) of binding between two molecules can be defined as the difference between the free energy of unbound species and that of a binding complex when all the species are at the standard conditions in which they do not interact with each other and have a concentration of 1 M. The ΔG° can be derived from simulations of the equilibrium between unbound (U) and bound states (B) according to71 ΔG° = ΔG − R T ln

Vunbound Vref

(2)

where ΔG is the free energy difference between unbound and bound states obtained from the simulations and the second term is the free energy of transfer of a unbound species from a reference volume (Vref = 1660 Å3) corresponding to the standard concentration to the volume (Vunbound) of unbound states. To obtain ΔG, we first calculated ΔGsim, which is written as ΔGsim [U] = ln RT [B]

(3)

where R is the gas constant, T is the simulation temperature in unit of K, and [U] and [B] are the fractions of configurations of unbound or bound species, respectively, in the simulation. To estimate ΔG(T) at temperatures other than the simulated ones, we fitted the ΔGsim data to the following equation:

ΔG ΔH 1 ΔS = − RT R T R

(4)

where ΔH and ΔS are the changes of enthalpy and entropy of the binding process, respectively. This equation assumes that ΔH and ΔS change little at temperatures where ΔG is fitted and interpolated. In the present study, we used all ΔGsim data obtained at T < 400 K to obtain ΔG at low temperatures. The fitting for the binding between Aβ and fibrils and between the designed peptide and fibrils yielded R2 values of 0.9100 and 0.9882 (Figure S2 in SI), respectively, indicating that the assumption of constant ΔH and ΔS is valid in both cases. The standard binding enthalpy and entropy of the binding can then be 1 obtained according to ΔH° = ΔH and ΔS° = T (ΔH ° − ΔG°) The calculations mentioned above require that both the unbound and bound states be defined. To this end, we first calculated the potential of mean force (PMF) profiles with respect to the distance (r) between the centroid of a peptide and the axis of the fibril. The PMFs obtained from the simulations at the lowest temperatures show in general a basin of attraction at short distances that levels off at long distances. We chose a distance, rc, at which the free energy is 5kBT higher than the lowest point of the basin, and all configurations with r < rc were considered to belong to bound states. We also chose two 941

DOI: 10.1021/acschemneuro.7b00359 ACS Chem. Neurosci. 2018, 9, 935−944

Research Article

ACS Chemical Neuroscience ORCID

distances ra and rb (ra > rb) between which the PMFs level off. Configurations with r between ra and rb were thought of as unbound states. According to our calculations (Figures S8 and S9 in SI), for the Aβ−fibril system, ra, rb, and rc are 46, 38, and 32 Å, respectively; for the system containing the designed peptide, the three distances are 40, 30, and 26 Å, respectively. Finally, the unbound volume was calculated according to

Vunbound = πh(ra 2 − rb2)

Wei Han: 0000-0003-0759-1766 Author Contributions

‡ X.J. and Y.C. contributed equally to this work. X.J. performed the simulations; X.J., Y.C., and W.H. analyzed the data; W.H. conceived and designed the research; W.H. wrote the paper.

Funding

(5)

We are grateful for financial support from the National Science Foundation of China (21673013) and the Shenzhen STIC (JCYJ20160330095839867, KQTD2015032709315529).

where h is the length of the simulation box in the z-direction. Vunbound was calculated to be 121.87 nm3 for the Aβ−fibril system and 128.47 nm3 for the system containing the designed peptide. According to the Langmuir absorption isotherm72,73 at equilibrium, the fraction (ϕ) of a surface covered by solute molecules that can be absorbed on the surface can be estimated according to

ϕ=

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Computer time was provided through Special Program for Applied Research on Super Computation of the NSFCGuangdong Joint Fund (the second phase) under Grant No. U1501501.

KeqACeq 1 + KeqACeq

(6)

where Ceq is the equilibrium concentration of the solutes and KAeq is the equilibrium constant of association between a solute molecule and the surface and can be related to the binding affinity according to KeqA = e−ΔG ° / RT . The concentration of the solute when the surface is half-saturated can thus be obtained through eq 6 with ϕ = 1/2. Identification of Helical Structures in Bound States. We first employed the Daura algorithm74 implemented in GROMACS to group in conformers the conformations in which either Aβ or the designed peptide is bound to region 16−24 of the fibril. The similarity between conformations was measured by root-mean-square distances (RMSD) between their Cα atoms of a segment to be examined. This segment includes residues 3−14 in the Aβ−fibril system and is identical to the entire peptide chain in the system containing the designed peptide. A distance cutoff of 1 Å was used for both systems. We next evaluated the helicity of each conformer obtained. The conformers with a helicity of >80% were regarded as helical conformers. All the helical conformers were then combined and the center structure of the combined ensemble was identified using the Daura algorithm again. This center structure was considered as the representative structure of the bound helix. Analysis of Other Structural Properties. We used the DSSP75 method implemented in the MDTraj package76 to calculate the secondary structure contents. In addition, a peptide was thought to be in contact with a particular region of the fibril if the minimum distance between any side chain atoms from the peptide and those of side chains on the fibril is