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In Silico Study of Recognition between A# and A# Fibril Surfaces: An NTerminal Helical Recognition Motif and Its Implications for Inhibitor Design Xuehan Jiang, Yang Cao, and Wei Han ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.7b00359 • Publication Date (Web): 27 Dec 2017 Downloaded from http://pubs.acs.org on December 28, 2017

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ACS Chemical Neuroscience

In Silico Study of Recognition between Aβ40 and Aβ β40 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

‡

These authors contribute equally to this work.

*Corresponding author: email: [email protected]; phone: +86-755-26032949

TOC Graphics

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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 ~2.6-5.6µM half-saturation concentration of Aβ on the surface (cf. experimental value ~6µM). 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 sidechains with similar properties on the same sides of the helix and maximize complementary interactions with sidechain 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.2,3,4,5 Common examples of such surfaces include water-air interface, self-assembled monolayers and membranes with varying lipid compositions.2,3,4,5 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.2,3,4,5

Recent studies have shown that the surface of Aβ fibrils can also recruit Aβ from solution and catalyze their conversion into aggregation-prone oligomers.6,7,8,9,10 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 chaperone10 and several antibodies14 have been found to compete with Aβ for the fibril surface, intervening 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 sidechains,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 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


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In the present study, we combined replica exchange molecular dynamics (REMD) sampling techniques 20 , 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 peptides19,24,25 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 experiments,9 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 peptide-based 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.

Results and Discussion

We first conducted extensive simulations of the binding of Ab40 to its fibrils. A single filament of the experimentally-determined structures of Aβ40 fibrils (PDB code: 2lmn) was used as the fibril template (see Methods). Using periodic boundary conditions (see 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 Methods). The sampling of binding equilibria between Aβ40 and its fibril was achieved through 64 1.4-µs replica simulations conducted at temperatures of 330K-690K (Figure S1a). The population ratios of unbound and bound configurations sampled from simulations at low temperatures can be fitted to the two-state model described in 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 kJmol-1 and -105±27 Jmol-1K-1, respectively. This model also predicts a standard binding free energy of ∆° = -33±1 kJmol-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 2.6µM-5.6µM. This result agrees reasonably well with the reported value (~6µM) measured at the same temperature through kinetic experiments of Aβ40 aggregation.9


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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.

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 sidechains 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 sites 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 Nand C-terminal segments of Aβ are both ~-110 kJmol-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 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


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to our calculations (Figure 1b), region 29-36 harbors only the 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 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 C-terminus 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.30,31,32 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).


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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 sidechain 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.

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 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.



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.34,35,36,37 The studies with solution nuclear magnetic resonance (NMR) spectroscopy have revealed a 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 sidechain 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 sidechains of E3, H6, Y10 and H13 were found rarely to contact the fibril surface while the sidechains 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


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the N-terminal helix is aligned parallel to the fibril axis. The negatively charged sidechains of D7 and E11 on one side of the helix interact with the K16 array of the fibril and the positively charged sidechain of R5 on the opposite side of the helix interacts with the E22 array of the fibril. The sidechains 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 , 40 , 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).



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 < 0.05, **: p-value < 0.01, ***: p-value < 0.001).

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 structures could be reinforced by introducing mutations that may enhance electrostatic interactions between the peptide and the fibril. The mutations tested include G9R, V12R and H14E, all of which introduce an additional like charge at the i+4th or i+7th position on the same side of the helical structure. All these mutations except for H14E which occurs at the fraying end of the 14 ACS Paragon Plus Environment

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helix are able to increase considerably the stability of the bound helical segment. Finally, we also attempted to replace S8 with hydrophobic amino acids with the aim of stabilizing the bound helix. The stability of the bound helix is however not sensitive to these hydrophobic mutations but nonetheless, it cannot be ruled out that a hydrophobic mutation at this position could increase the affinity of the peptide for the fibril.

Our results suggested the possibility of finding short peptides that can compete with Aβ for binding sites on fibrils by mimicking the helical structure of Aβ’s N-terminus. We conceived such a peptide (Ac-AFRADVRAERAE-NMe) on the basis of the following considerations. The mutations G9R and V12R that, as shown above, can stabilize the bound helix need to be incorporated into Aβ3-14. Inclusion of H14E may also be beneficiary by providing additional electrostatic interactions with the K16 array on the fibril despite the fact that this mutation appears not to enhance significantly the helicity of bound Aβ3-14. For a similar reason, a replacement of S8 with a hydrophobic residue was also considered. Finally, amino acids at positions 3, 6, 10 and 13 that are not involved in the binding interface could be replaced with alanine residues which arguably have the highest intrinsic helical propensity among naturally occurring amino acids.42 Also, alanine substitutions at these positions could help avoid interactions with fibrils that otherwise may be incompatible with the helical binding pose. For instance, our calculation showed that although the original E3 at the N-terminus of the peptide should in general enhance helical propensity of the



peptide,43 the peptide retaining this residue instead has reduced helicity and weaker interactions with the fibril surface owing to the interactions between E3 and the fibril surface that interfere with the formation of helical conformations in the peptide (Figure S6).

To test the potency of the designed peptide, we simulated its binding to the Aβ40 fibril filament through 67-µs REMD simulations with PACE (Figures 4a and 4b). We found that this peptide binds mainly to region 16-24, with a probability of ~99%. The average helical content of the peptide in the complex with the Aβ40 fibril is about ~45%, about three times as high as that of this peptide in solution and ~25% higher than that of the N-terminus of Aβ40 when bound to the fibril.

The analysis of the thermodynamics of the binding (Figure S2b in SI) showed that the enthalpy change upon binding of the designed peptide to the fibril (-50±12 kJmol-1) is ~65% of that of the binding of Aβ40 to the fibril, suggesting that this peptide

can interact with the fibril almost as strongly as the entire Aβ40 chain even though it is much shorter than Aβ40. In addition, the peptide loses less entropy than does Aβ40 during the binding process (-64±49 vs. -105±27 Jmol-1K-1) owing to its shorter peptide length. Overall, the standard free energy change associated with the binding of the peptide to the fibril is -30±3 kJmol-1 at 310 K, only about 3 kJmol-1 less than that of the binding of Aβ40 to the fibril.


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Figure 4. Binding of the designed peptide (Ac-AFRADVRAERAE-NMe) to Aβ40 fibrils characterized by REMD simulations using the PACE (a and b) and the OPLS/AA force fields (c and d). The last 700 ns of the PACE simulation at 330K and the last 50 ns of the all-atom simulation at 310 K were used for the analysis. Shown in panels a and c are the distributions of centroids of the designed peptide around an Aβ40 fibril in the xy-plane. The distributions are overlaid with the fibril structures. The peptide units in the fibrils are shown as cyan ribbons. Sidechains of K16, V18, F20, E22, I31 and M35 in the fibrils are shown as ellipsoids. The sidechains of K16 and E22 are colored in blue and red, respectively, while the sidechains of the other residues are colored in white. Shown in panels b and d are the representative helical structures of the designed peptide bound to region 16-24 of the fibril surface. The peptide backbone is shown as a purple helical ribbon. The sidechains of the peptides are shown as balls and sticks. O, N, C and H atoms are shown in red, blue, orange and white, respectively.

To confirm the above results, we carried out further two types of simulations. We first examined the binding of the designed peptide to the Aβ40 fibril filament through multiple 200-ns atomistic simulations with the REMD techniques (Table 1). These types of simulations become computationally feasible owing to far fewer degrees of freedom to sample in the inhibitor peptide than in Aβ40. Again, the three atomistic 17 ACS Paragon Plus Environment


force fields mentioned earlier were employed to test the robustness of the results.39,40,41 Although the atomistic simulations conducted are relatively short and unable to provide affinity data due to insufficient sampling of unbound states at low temperatures, with all the three force fields, we indeed observed a preferential binding (52-58%) of the peptide to region 16-24 of the fibril filament with a helical content of the bound peptide between 25% and 35% (Figures 4c, 4d and S7a-d). In particular, the binding pose of the helical form of the peptide on this region is almost identical to that observed from the PACE simulations.

In addition, we examined also the binding of the inhibitor peptide to Aβ40 fibrils using a realistic model (PDB code: 2lmn) of Aβ40 fibrils that contains two filaments. During REMD simulations with PACE (Table 1), the peptide was found to preferentially bind (>90%) to region 16-24 of either filament (Figure S7e). The bound peptide was mostly (~50%) found in a helical form and its binding poses agree with those observed from the simulations of the systems containing a single filament (Figure 4b and S7f).

Conclusion

In summary, through extensive multiscale molecular dynamics simulations, our computational study suggests that when Aβ40 is attached to its own fibril, the fibril surface tends to stabilize the N-terminus of Aβ40 in a helical conformation. Transient formation of helical structures in Aβ40 has been observed to expedite its


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aggregation,44,45 and it has been suggested that the transient helices in Aβ40 may accelerate the nucleation in solution by assembling the peptides and aligning them appropriately.46,47 Recent experiments have also suggested a similar role of transient helices in Aβ aggregation mediated by membrane surfaces 48 or by an air-water interface. 49 Considering the importance of Aβ-fibril binding with respect to the secondary nucleation process, our finding that the Aβ40 fibril surface can induce helical structures of bound Aβ40 raises the possibility that the secondary nucleation of Aβ40 might also involve a helix-mediated mechanism.

The interaction pattern between the N-terminal helix of Aβ40 and its fibril suggests a potential means of finding helical peptides that can recognize the surface of the Aβ40 fibril. A rule of thumb would be to put sidechains with similar properties on the same side of the helix and to ensure that different sides of the helix can all form interactions as favorable as possible with one-dimensional sidechain arrays formed on the fibril surface. Our results suggest that a helical peptide predicted by this rule is likely to exhibit a significantly improved helical character and interaction enthalpy compared to the N-terminal helix of Aβ40. While the affinity of the designed peptide for the fibril, according to our calculation, is still 1-2 kBT weaker than that of Aβ40, an experimentally testable prediction could be that increase of helical propensity of this peptide may further enhance its affinity for Aβ40 fibrils. One may use this peptide as a template to construct more potent helical peptides by introducing staples or terminal caps to significantly reduce the entropy cost associated with helix formation.50,51,52



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Whether these peptides can recognize Aβ fibril surfaces may be validated through a recently developed assay that has been employed to identify fibril-specific chaperones and antibodies.10,14

Models and methods

PACE force field. In the present study, a force field called the Protein with Atomic

details in Coarse-grained Environment (PACE, version 1.4) was employed to enhance sampling.23 In a simulation with the PACE force field, different components of a system can be represented at two resolutions. The PACE describes proteins at a united-atom (UA) level, retaining all heavy atoms and the hydrogen atoms on amide groups. Solvent, ions and membrane, on the other hand, are described by the MARTINI coarse-grained force field 53 in which each coarse-grained particle represents usually four atoms. The potential energy of the PACE is expressed as:

= + + + + ,, + + + +

!"#"

+ $%

!"#" ,

[1]

where the first four terms account for interactions between UA sites mediated through covalent bonds, ,, is used to correctly model backbone dihedral angles (&, ') when sidechains assume different rotamers ((1), accounts for nonbonded van der Waals (vdW) interactions, accounts for electrostatic interactions between charged sites, accounts for hydrogen bonding interactions,

!"#"

denotes

all the terms describing the potential energy of components represented by the


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MARTINI force field, and $%

!"#"

accounts for nonbonded interactions

between UA and MARTINI sites. The detailed explanation of each term and its parameterization can be found elsewhere.22

The PACE force field has been proved to be accurate in modeling structures and dynamics of proteins.19,23,25 It has been used for ab initio folding simulations of a series of small proteins of 20-73 amino acids, permitting the observation of correct folding of these proteins into their native structures.23 More recently, the PACE force field has also been employed to model Aβ aggregation, accurately reproducing experimental observables33,54 regarding structures of Aβ monomer19

and dimers,25

binding affinities55,56 between Aβ peptides and between Aβ peptides and tips of Aβ fibrils and kinetics57 of addition of Aβ peptides to tips of Aβ fibrils during fibril elongation.19 In the present study, we further show that this force field can be used to reproduce the affinity of Aβ peptides for surface of Aβ fibrils.9

Simulation of equilibrium structures of Aβ in solution. All simulations were

performed with the GROMACS 5 package.58 An Aβ40 in a full helical conformation was put into a dodecahedron box buffered in solution at 150 mM NaCl. The system was modeled with PACE. We first generated denatured structures of Aβ40 though a 7-ns NVT simulation at 700 K. The initial helical structures were completely disrupted after t=2 ns. Structures chosen at random after this point were used as the initial coordinates for subsequent simulations. For each selected structure, a 5000-step



energy-minimization followed by a 100-ps NPT simulation at 310 K and 1 atm was performed to equilibrate the system.

The structures obtained as above were used to launch replica exchange molecular dynamics (REMD) simulations.20,21 In REMD simulations, multiple simulations of the same system are conducted in parallel at a range of temperatures. The simulation replicas at low temperatures sample stable conformations and those at high temperatures explore conformational space rapidly through thermal motions. In a regular time interval, there is certain chance that simulations at different temperatures could swap their system coordinates. This allows the system to escape from trapped conformations at low temperatures, thereby enhancing sampling efficiency. Usually, the data sampled in the replica at the temperature of interest are collected for analysis.

In the present study, REMD simulations of the monomer system and the monomer-fibril system were both conducted at 330-690 K. Equilibrium properties of the systems were estimated using the results obtained at 330 K. Although this temperature is 20 K higher than the physiological temperature, it is within the temperature range (330 K~348 K) optimal for Aβ40 aggregation.59 Still, important quantiles like binding affinity at the physiological temperature can be obtained, as described later in this Methods section, through thermodynamic fitting of the results from REMD simulations.

With REMD, we performed simulations of 72 replicas of the monomer system,


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each running at constant NVT for 800 ns (Table 1). The Aβ40 structures sampled during the last 400 ns of the simulation at 330 K were used to construct the equilibrium ensemble of Aβ40 in solution.

The ensemble of a designed peptide that will be introduced below was constructed through a similar procedure. The details of the system are described in Table 1.

Simulations of binding Aβ to fibrils. A structural template of Aβ40 fibrils was

constructed based on available experimental structures. There are five high-resolution structures of Aβ40 fibrils that have been determined at high resolution through solid-state NMR experiments.16,60 Four of these structures were resolved for Aβ40 fibrils obtained in in-vitro experiments16 and the remaining one was obtained for Aβ40 fibrils originated from a patient’s brain.60 The structures of Aβ40 peptides are rather similar in the four in-vitro fibril structural models, exhibiting a U-shaped topology with two extended β-sheets, one containing regions 16-24 and the other containing regions 29-36 (Figure S3a). The structure of Aβ40 in the in-vivo fibrils, though it also has a U-shaped topology, contains bulges in the two β-sheet regions, leading to a sidechain arrangement different from that in in-vitro Aβ40 fibrils (Figure S3a). It is possible that the two types of fibrils may interact with Aβ40 peptides in distinct manners.

As biophysical investigation of in-vitro Aβ fibrils has been generally more



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common, we focused on the binding of Aβ40 to in-vitro Aβ40 fibrils and used one of the in-vitro Aβ40 fibril structures (PDB code:2lmn) to build the fibril template. We took a peptide unit in the center of the fibril structure and replicated it 12 times along the z direction of the simulation box. The spacing between neighboring replica was adjusted to 4.9 Å. The z-dimension of the box was set to 58.8 Å such that the fibril model can also form parallel, in-register β-sheet contacts with its images under the periodic boundary conditions. The fibril model was represented by the OPLS/AA all-atom force field39 and energy-minimized first in vacuum and then in a box of water molecules represented by the TIP4P model.61 The system was further relaxed through a 100-ps NPT simulation at 310 K and 1 atm. In the simulations above, harmonic restraints were introduced to all pairs of atoms that form cross-β contacts, fixing their distances at 4.9 Å. The force constant of the restraints is 5000 kJmol% nm%0 . Table 1. Summary of simulations conducted in the current study System

Sampling method/ Force field

System size (number of particles)

simulation time

Aβ40

REMD / PACE

2029

800ns/replica × 72 replicas = 57.6µs

Aβ40 + Aβ40 fibril

REMD / PACE

7640


designed peptide

REMD / PACE

1722


designed peptide + Aβ40 fibril

REMD / PACE

6203


A1E mutant of designed peptide + Aβ40 fibril

REMD / PACE

6204



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designed peptide + Aβ40 fibril (2-fold)

REMD / PACE

15361



REMD / OPLS-AA

29866



REMD / AMBER99SB-ildn

29898



REMD / CHARMM36

23413


63968

100ns × 12 = 1.2 µs

normal MD / Aβ3-14 + Aβ40 fibril

OPLS-AA

Aβ3-14

normal MD / OPLS-AA

9030

100ns × 9 = 0.9µs

Aβ3-14 + Aβ40 fibril

normal MD / CHARMM36

49899

100ns × 6 = 0.6µs

Aβ3-14

normal MD / CHARMM36

6980

100ns × 9 = 0.9µs

Aβ3-14 + Aβ40 fibril

normal MD / AMBER99SB-ildn

62916

100ns × 6 = 0.6µs

Aβ3-14

normal MD / AMBER99SB-ildn

9078

100ns × 9 = 0.9µs

mutants + Aβ40 fibril

normal MD / OPLS-AA

~64000

100ns × 63a = 6.3µs

summary a.

PACE: 382.4 µs, All atom: 54.6 µs

We performed 63 simulations in total for the mutants of Aβ3-14, the details of which are summarized in Table S1.

The last frame of the simulation described above was then employed to provide the initial coordinates of the fibril model for REMD simulations of binding of Aβ to fibrils using PACE. The REMD simulations include 64 replicas conducted at 330-690 K (Table 1). In each replica, the fibril was placed in the center of the box, aligned in 25 ACS Paragon Plus Environment


the z-direction and an Aβ40 peptide was randomly orientated and placed around the fibril. Attention was paid to ensure that the minimum atomic distance between Aβ40 and the fibril is >15 Å and that the minimum distance between the center of Aβ40 and the fibril axis is >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 sidechain atoms buried inside of the fibril. The force constant of the restraints is 5000 kJmol% nm%0 . 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 330K. As shown in Figure S1 in the supplementary information (SI), the simulation convergence was achieved after t=1 µs 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 310-480K (Table 1).


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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 Nand 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 kJmol% nm%0 . 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 (∆°) 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 1M. The ∆° can be derived from simulations of the equilibrium between unbound (U) and bound states (B) according to:71


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∆° = ∆ − 2T45

6789:78; 6

, [2]

where ∆ 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 (?@ = 1660 ÅD ) corresponding to the standard concentration to the volume (?E E ) of unbound states. To obtain ∆ , we first calculated ∆F that is written as: ∆GHIJ K!

[$]

= ln , [3] [N]

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

=

∆S K R

−

∆T K

, [4]

where ∆U and ∆V are the changes of enthalpy and entropy of the binding process, respectively. This equation assumes that ∆U and ∆V change little at temperatures where ∆ is fitted and interpolated. In the present study, we used all ∆F data obtained at T X\ ) between which the PMFs level off. Configurations with X between X[ and X\ were thought of as unbound states. According to our calculations (Figure S8 and S9 in SI), for the Aβ-fibril system, X[ , X\ and XY 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

?E E = ^ℎ(X[0 − X\0 ), [5]

where h is the length of the simulation box in the z-direction. ?E E was calculated to be 121.87 nm3 for the Aβ-fibril system and 128.47 nm3 for the system containing the designed peptide.


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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

&=

b c `=a =a b c d`=a =a

[6],

g where Ceq is the equilibrium concentration of the solutes and ef is the equilibrium

constant of association between a solute molecule and the surface and can be related to the binding affinity according to

g ef

=h

%

∆ij kl

. 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 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 sidechain atoms from the peptide and those of sidechains on the fibril is < 4.5 Å.

Supporting information

Table S1, summary of mutant simulations; Figure S1, convergence of free energy of peptides bound to Aβ40 fibril; Figure S2, fitting of binding free energy; Figure S3, structures of fibril models and accessibility of surface of each fibril model; Figure S4, radius of gyration of Aβ40; Figure S5, probabilities of residues of Aβ3-14 contacts with fibril surface in simulation with all atom force fields; Figure S6, free energy and helicity of A3E mutant of the designed peptide when binding on Aβ40 fibril surface; Figure S7, binding of the designed peptide to Aβ40 fibril models contain one or two filaments; Figures S8 and S9, PMFs of the binding between peptides and the Aβ40 fibril. This material is available free of charge at http://pubs.acs.org.

Author information

Corresponding Author

*E-mail: [email protected]. Phone: +86-755-26032949.


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ORCID

Wei Han: 0000-0003-0759-1766

Author Contributions

X.H.J. performed the simulations; X.H.J., Y.C., and W.H. analyzed the data; W.H. conceived and designed the research; W.H. wrote the paper.

Funding

We are grateful for financial support from the National Science Foundation of China (21673013)

and

the

Shenzhen

STIC

(JCYJ20160330095839867,

KQTD2015032709315529).

Notes

There are no conflicts of interest to declare.

Acknowledgements: Computer time was provided through Special Program for

Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No.U1501501.



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