Pathways of amyloid-β aggregation depend on oligomer shape

Article Cite This: J. Am. Chem. Soc. 2018, 140, 319−327

pubs.acs.org/JACS

Pathways of Amyloid‑β Aggregation Depend on Oligomer Shape Bogdan Barz,*,†,‡ Qinghua Liao,†,¶ and Birgit Strodel*,†,‡ †

Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany ¶ Department of Cell and Molecular Biology, Uppsala University, S-75124 Uppsala, Sweden ‡

S Supporting Information *

ABSTRACT: One of the main research topics related to Alzheimer’s disease is the aggregation of the amyloid-β peptide, which was shown to follow different pathways for the two major alloforms of the peptide, Aβ40 and the more toxic Aβ42. Experimental studies emphasized that oligomers of specific sizes appear in the early aggregation process in different quantities and might be the key toxic agents for each of the two alloforms. We use transition networks derived from all-atom molecular dynamics simulations to show that the oligomers leading to the type of oligomer distributions observed in experiments originate from compact conformations. Extended oligomers, on the other hand, contribute more to the production of larger aggregates thus driving the aggregation process. We further demonstrate that differences in the aggregation pathways of the two Aβ alloforms occur as early as during the dimer stage. The higher solvent-exposure of hydrophobic residues in Aβ42 oligomers contributes to the different aggregation pathways of both alloforms and also to the increased cytotoxicity of Aβ42.

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INTRODUCTION Amyloid aggregation is the process via which proteins or protein regions assemble into so-called fibrils rich in β-sheet content and eventually into amyloid deposits.1,2 In most of the cases this process is associated with diseases, the most devastating ones, such as Alzheimer’s disease, being related to the deterioration of neuronal cells that leads to dysfunctional regions of the brain. Understanding the aggregation process and the various intermediate protein assemblies that appear from the very early stages, such as the small oligomers and protofibrils until the appearance of fibrils and amyloid plaques, and elucidating their role and contribution to the evolution of the disease is of key importance.3 In the case of Alzheimer’s disease, the aggregation of the amyloid-β peptide (Aβ) is thought to be directly linked to the disease onset. In the past two decades, extensive experimental evidence has been generated which implicates Aβ oligomers in the development of the disease rather than the aggregation end stage, extremely stable and quite inert amyloid fibrils.4−9 The results of the various studies suggest that many oligomers different in terms of sizes and structures can be formed, and these oligomers may induce cellular stress and neuronal death via a variety of mechanisms, including membrane disruption and receptormediated toxicity. An important determinant for the cytotoxicity of amyloid oligomers is the surface hydrophobicity, as for different amyloid proteins or peptides it has been shown that the toxicity of their oligomers increases with increasing hydrophobicity.10−12 Moreover, it seems that smaller amyloid oligomers are usually more toxic than larger oligomers.11,13−15 © 2017 American Chemical Society

Aβ is present in the body in many forms and various lengths, but the main constituents are the 40 and 42 amino acids long (Aβ40 and Aβ42) peptides. Despite the much higher concentration (∼10-fold) of Aβ40 compared to Aβ42, the more toxic peptide is Aβ42, and it is also the main constituent of amyloid plaques.16 With the focus shifted from amyloid fibrils to small oligomeric species, an important question emerged regarding the differences between the aggregation pathways of Aβ40 and Aβ42. The aggregation stage where these differences are the most distinct is a topic of debate. One view, based on chemical kinetics experiments, takes into account several mechanisms involved in the Aβ aggregation process, to emphasize the role of the fibril-catalyzed secondary pathway for the production of new aggregates for Aβ40 and Aβ42.17 Other groups propose that the main differences in the aggregation pathways of the two alloforms appear only at the stage of fibril formation, and that the initial oligomers formed during primary nucleation are less organized with indiscernible structural differences or specific oligomeric species between Aβ40 and Aβ42.18 Finally, a third hypothesis is that the different toxic activities observed for Aβ40 and Aβ42 are directly related to the size and structure of oligomers that are formed during the primary nucleation process.19,20 In the third case, the relative amounts of specific oligomer species is considered a signature of the early aggregation process for each peptide. Received: September 28, 2017 Published: December 13, 2017 319

DOI: 10.1021/jacs.7b10343 J. Am. Chem. Soc. 2018, 140, 319−327

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Journal of the American Chemical Society

Coarse-Grained Transition Networks Reveal the Importance of Dimers in the Assembly Process. Coarse-grained TNs (CGTNs) are derived by defining the aggregation state as simply the oligomer size in terms of peptides per oligomer. CGTNs were calculated for Aβ42 and Aβ40, and they describe the assembly of monomers into oligomers up to 18-mers for Aβ42 and 20-mers for Aβ40 (Figure 1). On the basis of the number of transitions between

In this study, we use atomistic molecular dynamics (MD) simulations combined with a novel transition network analysis method for elucidating the pathways of aggregation into oligomers, ranging from dimers to 20-mers, of Aβ40 and Aβ42. Complementary to experiments, MD simulations have been used to explore various aspects of Aβ oligomers.21−26 However, most of these studies focused on only the dimer and not on the pathways of aggregation, or a coarse-grained force field was used instead of an atomistic model. Our study moves beyond these limitations, allowing us to reveal distinct differences in the aggregation of the two Aβ alloforms. In particular, we emphasize the decisive role of the oligomer shape in the early aggregation process and demonstrate that extended/elongated oligomers, which are found to be of different importance for Aβ40 and Aβ42, are the main driving force behind the aggregation into larger oligomers. Compact conformations, on the other hand, are the metastable oligomers, which give rise to the same oligomer distributions as seen in experiments. In conclusion, our atomistic simulation results allow us to explain the experimentally observed differences in the pathways of early aggregation of the two Aβ alloforms and also provide a rationale for their different toxicities.

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RESULTS AND DISCUSSION The assembly process of Aβ40 and Aβ42 is studied here by means of atomistic MD simulations during which 20 peptides, initially separated, are allowed to interact. For each of the two peptide lengths, we combined the data from five independent simulations and derived the results presented below. The entire assembly process is described by transition networks (TNs). TNs proved to be successful in capturing essential features of short-peptide aggregation,27,28 Aβ42 aggregation,29 and the conformational dynamics of Aβ42 monomers.30 To investigate whether the implicit solvent affected the peptide diffusion and aggregation in our simulations, we performed a 1 μs MD simulation where three Aβ42 peptides were allowed to interact in explicit solvent (using the SPC/E water model) at the same concentration as the one from the simulations in implicit solvent (i.e., 770 μM). The peptides formed a trimer within the same time scale as in implicit solvent and also without major changes in the secondary structure of the peptides. In addition to the explicit solvent, one could also explicitly consider the effect of hydrodynamics on the aggregation process when using an implicit solvent. Chiricotto et al.31 recently showed that hydrodynamic interactions can speed up the aggregation of small peptides due to a high monomer diffusivity. The size fluctuation of the oligomers as well as the growth of the largest cluster were also influenced by the hydrodynamic interactions. The amount of β-sheet in the conformations from the current implicit solvent simulations showed no significant change between Aβ42 dimers (7.2%), tetramers (7.3%), and 18-mer (7.3%), or Aβ40 dimers (9.3%), tetramers (7.5%), and 20-mer(8.0%). The results from both explicit and implicit solvent simulations confirm the conclusion of our previous study29 that the early Aβ assembly proceeds without the formation of significant β-sheet content, at least for the peptide concentration and microsecond time scale used in this study. This observation is supported by recent experimental studies that showed the presence of Aβ40 and Aβ42 dimers and trimers that lack a defining secondary structure18 and also the existence of unstructured larger oligomers.32

Figure 1. Coarse-grained transition networks for (a) Aβ42 and (b) Aβ40. Nodes correspond to different oligomer sizes, and their area is proportional to the oligomer population. The thickness of the edges connecting the nodes corresponds to the number of transitions between the nodes, and the arrows indicate the direction of the transition, which can be association or dissociation.

different oligomer species, reflected in the thickness of the edges connecting the nodes, in the case of Aβ42, monomers contribute mostly to the formation of dimers and trimers, and dimers are involved in the formation of trimers and tetramers. While tetramers are also formed directly from trimers and monomers, the contribution from two dimers is much larger. This suggests that Aβ42 tetramers are mostly formed by the interaction of two dimers rather than the interaction between a trimer and a monomer. For Aβ40, monomers contribute the most to the formation of dimers but also to very large assemblies, such as 20-mers. Interestingly, Aβ40 tetramers are formed more often by the interaction between a monomer and a trimer than between two dimers. Considering that the population of Aβ42 tetramers is almost twice the population of Aβ40 tetramers, one can conclude that Aβ42 dimers are more efficient in forming Aβ42 tetramers than Aβ40 dimers are in forming Aβ40 tetramers. It should be noted that the initial monomer concentration used in our simulations is larger compared with some experimental conditions19,20 but has the same order of magnitude as in other studies.33 It is possible that at much lower concentrations than the one used in the current study, Aβ42 monomers would be longer lived, which could add importance to the tetramer formation pathway via a trimer plus monomer. Nonetheless, we observe a clear difference in the roles of Aβ40 and Aβ42 dimers in the further assembly process, which should also be present at lower concentrations. Another quantity that might be affected by the 320


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oligomer states are generally smaller than those of the monomers, as they can have different values for N2 and N3, yielding many different states per oligomer size. This indicates a high probability of transitions between states of the same oligomer size resulting from internal reorganizations of the oligomers, while transitions between different oligomer sizes originate from association or dissociation events. The color coding in Figure 2 corresponds to the oligomer feature N4, which is directly related to the shape of the oligomer. Within the largest clusters of states with the same N1 value (i.e., dimers, trimers, tetramers, or hexamers), one can identify a gradual transition from extended (red) to more compact conformations (blue). Typical examples for extended and compact oligomers are shown in Figure S2. On the basis of the distribution of the oligomers with different shapes within each of these clusters and on the transitions between different clusters, it is apparent that the extended oligomers are more involved in association and dissociation events than the compact oligomers. This observation is also confirmed by the probability for oligomers to participate in the formation of a larger assembly depending on their shape. Table S1 lists this probability for monomers, dimers, trimers, and tetramers for both Aβ40 and Aβ42. For example, the probability of extended dimers and trimers to further aggregate is for both alloforms by more than a factor of 5 larger than for their compact counterparts. The coexistence of oligomers with the same number of peptides but with different geometrical characteristics has been previously hypothesized by Kloniecki et al.34 using ionmobility separation−mass spectrometry (IMS-MS) experiments to identify Aβ40 oligomers. They observed two types of possible conformations corresponding to the same oligomer size based on different drift times associated with the oligomer signal. The two types were compact and extended corresponding to smaller and larger collision cross-section (CCS) values, respectively. The stability of compact conformations was explained by the presence of the salt bridge between D23 and K28.35 Another computational/experimental study36 investigated the interplay of compact and extended conformations for the Aβ40 peptide and identified an increase in compactness with increasing temperature. More recently, small oligomers of Aβ42 were shown to be structurally very dynamic fluctuating between compact conformations and elongated or multiglobular conformations. This study combined the photoinduced cross-linking of oligomers with atomic force microscopy (AFM).37 Extended metastable conformations were also observed for the oligomers of Aβ fragments, such as wild-type Aβ(1−28) and the Aβ(1−28) A2V mutant, and were hypothesized to be ideal for seeding polymerization.38 The role of the ratio between compact and extended conformations in the dimer toxicity was emphasized for human and rat islet amyloid polypeptide (IAPP) dimers.39 Given the fact that, in the case of α-synuclein, stable compact, extended, and unfolded conformations were also identified,40 one can conjecture that the coexistence of these types of structures is a general characteristic of amyloid oligomers. Oligomer Mass Distribution of Compact Species Reproduces Experimental Observations. During the assembly processes of both Aβ40 and Aβ42, a large variety of oligomers is generated as shown in the DTNs. To quantify the oligomers formed during the early aggregation process, we calculated oligomer mass distributions from the five simulations, considering the entire simulation length. The mass

concentration is the conformation of the oligomers, as they have more time for internal reorganization before further growth at lower protein concentrations. It should be mentioned that the existence of oligomers with little β-sheet structure as observed from our simulations is supported by the study of Ahmed et al.33 who showed that at 200 μM Aβ42 concentration, small oligomer-like pentamers lack a high βsheet content.19,20 Detailed Transition Networks Reveal the Importance of Oligomer Shape in the Assembly Process. To gain further insight into the aggregation process, we include three additional features characterizing the structures of the oligomers into the definition of the aggregation states. An aggregation state is now a combination of four numbers: the oligomer size (N1), the number of interpeptide salt bridges (N2), the number of interpeptide hydrophobic contacts (N3), and the shape index of the oligomer (N4) (see Experimental Section for details). The resulting detailed transition networks (DTNs) for Aβ42 and Aβ40 (Figures 2 and S1) have the

Figure 2. Detailed transition networks for (a) Aβ42 and (b) Aβ40. Nodes correspond to different aggregation states, their area is proportional to the population of a particular state, and the color corresponds to the shape index as shown on the color scale (red for extended oligomers and blue for compact oligomers). The location of the nodes with respect to each other is based on the transition probability between the states represented as edges between the nodes; i.e., nodes that have a high transition probability between them are close to each other. The color of a specific edge is a combination of the colors of the nodes it connects. A large version of this figure is presented in Figure S1.

nodes with the largest population (reflected by the node area) corresponding to monomers. This is on the grounds that every simulation starts with 20 monomers, leading initially to a large monomer population, but even more due to the fact that the shape of the monomers is the only discriminating quantity between different monomeric states. The populations of the 321


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hexamers in solution independent of SDS-PAGE or photoinduced cross-linking.41 Moreover, the oligomer distributions were partly confirmed by another study using IMS-MS.20 The oligomers observed with this method had sizes similar to those from the PICUP SDS-PAGE experiments, with Aβ42 forming dimers, tetramers, hexamers, and dodecamers, while for Aβ40, only dimers and tetramers were recorded. On the basis of differences in the CCS values of the identified Aβ40 and Aβ42 oligomers, tetramers were suggested as key oligomer species in explaining the different aggregation pathways. According to the authors,20 an Aβ42 tetramer conformation that is “open” relative to the Aβ40 tetramer conformation is more likely to accommodate a monomer/dimer leading to pentamer/ hexamer paranuclei. The oligomer mass distributions of compact conformations derived in our study is in agreement with the these experimental results19,20 apart from the absence of dodecamers for Aβ42 (Figure 3b), which is probably due to the small system size and thus incomplete sampling in our MD simulations. Among the computational studies involving the early aggregation of Aβ peptide, Urbanc et al.25,26 calculated oligomer size distributions, which are in agreement with the experimental data from Bitan et al.19 and, thus, with our results. They used discrete molecular dynamics (DMD) combined with a coarse-grained four-bead model for the peptide and implicit solvent, which was adapted so that the experimental oligomer size distribution was reproduced. More recently, Emperador et al.42 also performed DMD simulations of Aβ40 aggregation considering only four peptides. The oligomer size distribution from their study is similar to the one we observed here for compact Aβ40 oligomers. The agreement between the oligomer mass distributions for compact oligomers from our study and the above-mentioned experimental and computational studies supports our hypothesis that the shape of the oligomer plays an important role in the early aggregation of Aβ. Elongated/extended oligomers appear to be more involved in the aggregation process and the formation of larger oligomers than globular/compact oligomers. Dimers of Aβ40 and Aβ42 Are Structurally Different. To better understand the different behaviors of the two Aβ alloforms during the assembly process, we analyzed structural characteristics of their dimers and tetramers. From the coarsegrained transition networks we observed that, in the case of Aβ42, dimers contribute more to the formation of tetramers than in the case of Aβ40. Here we take a closer look at dimers by analyzing time-resolved, interpeptide contact maps from the early stages of dimer formation (Figure S4). During the first 5 ns of attachment, the two peptides comprising Aβ40 and Aβ42 dimers have contacts of only small intensity. The main difference between the two alloforms is that Aβ40 forms slightly stronger contacts between the C-terminal regions (amino acids A30−M35 with K28−L34). The C-terminal contacts of Aβ40 remain rather strong relative to other contacts for the rest of the dimer lifetime. In fact, most of the contacts between amino acids G25−V40 of one peptide and G25−V40 from the other peptide of Aβ40 intensify during the first 50 ns of the dimer formation, the strongest contacts occurring between A30−I32 and A30−I32. Aβ40 dimers also show significant contacts between the N-terminal amino acids, while in Aβ42 dimers the contacts between the N-terminal D1−Y10 regions of the two peptides are scarce. Moreover, the Aβ42 contacts between the C-terminal amino acids M35−A42

distributions including all oligomers independent of shape display different patterns for the two alloforms (Figure 3a). For

Figure 3. Oligomer mass distribution during the assembly process considering (a) all oligomers and (b) only compact oligomers (with N4 > 5). The red bars correspond to Aβ42 and blue bars to Aβ40. Error bars are standard errors of the mean.

Aβ42, the dimers and tetramers are outstanding from the rest, while for Aβ40, heptamers are rather abundant. The largest assembly for Aβ42 is an 18-mer, while for Aβ40, a 20-mer was formed (Figure S2). On the basis of our observations derived from the transition networks that the extended oligomers are the ones involved in the assembly process, we hypothesize that compact oligomers are metastable and thus the main species which appear in the oligomer distributions observed in various experiments. To verify this hypothesis, we calculated oligomer mass distributions for different values of N4, the characteristic that is directly related to the oligomer shape (Figure S3). The oligomers that best match previous experimental and computational studies appear for a value of N4 larger than 5, which corresponds to compact conformations. As shown in Figure 3b, the mass distributions of compact oligomers are very different from the ones considering oligomers of all shapes. Aβ42 displays major peaks at monomers, dimers, tetramers, and hexamers, while Aβ40 has major peaks at monomers, dimers, trimers, and tetramers. The first studies aiming at elucidating the basis for the different aggregation pathways of Aβ40 and Aβ42 used a combination of experiments consisting of photoinduced crosslink of unmodified peptides (PICUP) for oligomer stabilization and sodium dodecyl sulfate (SDS)−polyacrylamide gel electrophoresis (PAGE) to identify metastable oligomers specific to each alloform.19 The derived oligomer size distributions showed distinct species present for Aβ40 (monomers, dimers, trimers, and tetramers) and for Aβ42 (monomers, dimers, pentamers, hexamers, and dodecamers). On the basis of these results, Teplow and coauthors proposed a model where Aβ42 pentamers/hexamers are paranuclei that associate to form larger oligomers and protofibrils. They also emphasized the absence of pentamers/hexamers for Aβ40, assigning this oligomer species a key role for the differences in the aggregation pathways of Aβ40 and 42. The PICUP SDSPAGE experiments have been suggested to lead to artificial oligomers,18 but a recent study that combined sedimentation velocity centrifugation and small angle neutron scattering (SANS) has confirmed the presence of Aβ42 pentamers/ 322


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aggregation pathways of Aβ40 and Aβ42.20 On the basis of our CGTNs (Figure 1), the Aβ42 tetramers contribute considerably to the formation of pentamers, hexamers, and heptamers but also to a smaller degree to the formation of 10-mers and 14-mers. The Aβ40 tetramers, on the other hand, lead to the formation of pentamers and to a smaller degree to 14-mers. This indicates that Aβ42 tetramers are more involved in the formation of higher order oligomers than Aβ40 tetramers. One of the reasons for this observation could be the larger number of Aβ42 tetramers but could also be related to the exposure of hydrophobic amino acids as discussed below. At the same time, the intermolecular contacts within tetramers also reveal why the Aβ42 tetramers are more stable and thus more numerous than the Aβ40 tetramers (Figure S5). Despite the overall low contact probabilities (up to 0.03) as visible in the contact maps of both Aβ42 and Aβ40 tetramers, there are noticeable differences between the two peptides. First, the average contact frequency of all Aβ42 contacts (0.0094) is higher than that of Aβ40 (0.0080). Second, in the case of Aβ42 (Figure S5, left) one can notice a contact map dominated by probability values within ∼0.01−0.02 (corresponding to green colors), indicating a more uniform distribution of the contact frequencies between certain regions of the peptide. Among the more dense areas in the map, where stronger contacts shown in red are also found, are the regions L17−F20 × L17−F20, L34− V40 × L34−V40, and D1−Y10 × M35−A42. For Aβ40 (Figure S5, right), on the other hand, one can observe many strong contacts indicated in red that are rather scattered but fewer areas with average probability contacts within ∼0.01− 0.02 than for Aβ42. These two differences indicate that Aβ42 tetramers are held together by on average stronger contacts involving whole regions of the peptide rather than disperse contacts between one or two amino acids only as in the case of Aβ40 tetramers. The hSASA for tetramers has a similar trend as for dimers, with the hSASA distribution of Aβ42 shifted to larger values compared to that of Aβ40 (Figure 5). The difference between the mean of the two distributions is ∼500 Å2, which is more than twice larger than the corresponding difference for dimers. Interestingly, the hSASA distributions for compact and extended Aβ40 tetramers are very distinct, which was not the case for Aβ40 dimers. The extended conformations have on average larger hSASA values for both alloforms, and

and M35−A42 are also very weak or missing. This suggests that Aβ42 dimers have the C-terminus, and to a lesser degree the N-terminus, free to interact with other peptides, which is not the case for Aβ40 dimers and might contribute to the large number of Aβ42 tetramers formed mainly via dimer association as observed from the CGTNs. The increased exposure of the hydrophobic amino acids of Aβ42 dimers to the solvent is also emphasized by the solventaccessible surface area (SASA) of the hydrophobic amino acids, which will be called hSASA henceforth. Aβ42 dimers have on average a larger hSASA than that of Aβ40 dimers with a difference of ∼230 Å2 (Figure 4a). As a confirmation of the

Figure 4. SASA of hydrophobic amino acids of dimers. In part a the hSASA distributions of all Aβ42 (red) and all Aβ40 (blue) dimers are shown. In parts b and c the hSASA distributions of extended (green) and compact (black) dimers for Aβ42 and Aβ40, respectively, are shown.

exposure of the C-termini in Aβ42 dimers, we calculated the SASA of the last two amino acids I41 and A42 yielding ∼380 Å2, which is a considerable contribution to the higher hSASA of the Aβ42 dimers compared to Aβ40. It should be noted that the increased solvent-exposure of hydrophobic residues within Aβ42 oligomers was shown to increase their cytotoxicity.12 The differences between Aβ40 and Aβ42 oligomers are further emphasized by comparing the hSASA of extended and compact dimers (Figure 4b,c). Aβ40 dimers, whether extended or compact, have very similar hSASA distributions. Extended Aβ42 dimers, on the other hand, have their hSASA distribution shifted toward larger values compared to compact dimers. Previous computational studies, where coarse-grained dimers obtained from DMD simulations26 were converted to atomistic resolution and equilibrated in explicit water,43 also showed larger hSASA values for Aβ42 than for Aβ40, but the difference between the two alloforms (∼100 Å2) was almost twice smaller than in our simulations. The absolute hSASA values were also almost twice smaller than the ones obtained in our current simulations, for both Aβ40 and Aβ42. This suggests an increased burial of hydrophobic amino acids in the dimers obtained from DMD simulations with the coarse-grained model. On the basis of our results, we propose that differences in the dimer conformations of Aβ40 and Aβ42, specifically the lack of interactions between the termini and the hydrophobic exposure in Aβ42, are responsible for the formation of a larger number of tetramers for Aβ42 compared to Aβ40. Differences between Aβ40 and Aβ42 Intensify for the Tetramers. Tetramers have been emphasized as key oligomeric species that might be directly linked to the different

Figure 5. SASA of hydrophobic amino acids from tetramers. In part a the hSASA distributions of all Aβ42 (red) and all Aβ40 (blue) tetramers are shown. In parts b and c the hSASA distributions of extended (green) and compact (black) tetramers for Aβ42 and Aβ40, respectively, are shown. 323


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number of hydrophobic contacts for each of these three oligomer types. In general, no major differences between the number of interpeptide hydrophobic contacts is observed between the oligomers of both alloforms. On the basis of our results, we conclude that both electrostatic and hydrophobic interactions are important for the stabilization of the compact, small oligomers and during the formation of larger assemblies. It should be mentioned that previous simulation studies already conjectured that weak but stabilizing interactions, such as intermittent salt bridge formation, are sufficient for the growth of Aβ assemblies.45 Collision Cross-Section of Dimers and Tetramers. To assess the similarity of the oligomers determined in our simulations with oligomers observed in experimental studies,46 we calculated the CCS values of dimers and tetramers using the Mobcal software.47,48 The CCS values were calculated using the trajectory approximation (TA) and are shown in Table S2, together with values from previous experimental or computational studies. The CCS analysis, presented in detail in Supporting Information, depicts a complex picture of the CCS values governed by different experimental or simulation conditions and by different CCS calculation methods. The CCS of Aβ40 and Aβ42 dimers and tetramers, either compact or extended, are overall in agreement with previously reported experimental18,20,34,49 or computational values.22−24 However, there are large variations even among the experimental values. We also tested different solvent environments and conclude that Aβ oligomers simulated in explicit solvent have larger CCS values than those in implicit solvent (by ∼100 Å2 larger for Aβ40 tetramers), while simulations in vacuum generally decrease the CCS of oligomers obtained in implicit solvent by up to ∼150 Å2 for Aβ42 tetramers. Moreover, other methods for calculating the CCS from simulations, such as the projection approximation (PA) or the exact hard sphere scattering (EHSS), can lead to deviations from the TA values of up to ∼200 Å2 for Aβ40 and Aβ42 dimers. Thus, at the computational level, the solvent model has a direct effect on the sampled conformations and thus on the CCS values, while at the experimental level, different conditions and types of ions (negative20,49 or positive18,34) can lead to differences in the CCS values of up to 300 Å2 for dimers and 800 Å2 for tetramers. Moreover, none of the computational studies thus far aimed at reproducing the conditions of the IMS-MS experiments (gas phase, different protonation states) of Aβ oligomers. Thus, the oligomer conformations and CCS values reported from simulations might be quite different from experimental ones. At the current state, it is therefore not possible to truly link theoretical and experimental CCS values for Aβ oligomers and draw conclusions about the oligomer shape based on experimental CCS results. This shortcoming will be addressed in our future studies, requiring a systematic simulation approach aimed at producing CCS values under experimental conditions. Shape of Larger Oligomers. The largest assemblies observed in our simulations were a 20-mer for Aβ40 and an 18mer for Aβ42. The Aβ40 20-mer is elongated and curved, which could grow into a circle upon further oligomerization (see Figure S2). It has an estimated length of ∼240 ± 2.4 Å with a diameter of the cross-section varying between 20 and 50 Å. Unstructured Aβ40 oligomers with sizes ranging from 50 to 150 Å have been shown to coexist with cross-β-sheet fibrils in high resolution NMR experiments.32 These experimental observations are also in agreement with the low amount of

compact Aβ42 tetramers sample hSASA values close to those sampled by extended Aβ40 tetramers. Compact Aβ40 tetramers have the smallest hydrophobic SASA. Computer simulations of Aβ42 tetramers in explicit solvent44 produced hSASA values of ∼5400 Å2, which is ∼567 Å2 more than the average hSASA for compact (∼4833 Å2) Aβ42 tetramers and ∼373 Å2 more than that of extended (∼5027 Å2) Aβ42 tetramers from our simulations. Interestingly, the N4 value related to the shape of the Aβ42 tetramers from Brown et al.,44 based on the reported momenta of inertia, is ∼6, which corresponds to a compact tetramer according to our definition. Nonetheless, the hSASA values from Brown et al. and from our simulations are much closer than the differences observed between the hSASA of dimers from previous studies43 and the ones from this work. This comparison strengthens our previous observation that the much smaller hSASA of Aβ dimers calculated in ref 43 is most likely due to the coarse-grained method used for obtaining the dimers. Role of Electrostatic Interactions and Hydrophobic Contacts in the Early Aggregation. To obtain insight into the role of electrostatic interactions in the aggregation process, we colored the DTNs based on the average number of salt bridges per peptide in the oligomers (Figure S6). For both alloforms, most of the oligomers have nodes with 0, 1, or 2 salt bridges per peptide, and the nodes with the same number of salt bridges tend to cluster together per oligomer size, indicating structural similarities. For Aβ40 there are more often larger oligomers with two salt bridges than for Aβ42. Moreover, Aβ40 dimers, trimers, and tetramers have nodes with three salt bridges (indicated in Figure S6), which is not the case for Aβ42 where hexamers are the only oligomer species also with three salt bridges per peptide. Taking a closer look at one of the Aβ42 hexamers with three salt bridges, we noticed that the formation of the salt bridges is directly related to the formation of a more compact conformation. This observation is illustrated in Figure S7 where a hexamer is shown before and after the formation of the very compact conformation, which is accompanied by the formation of two salt bridges. Moreover, the comparison of the DTNs colored according to the oligomer shape (Figure S1) with the DTNs colored according to the salt bridges (Figure S6) also confirms that, in general, the compact oligomers are stabilized by more interpeptide electrostatic interactions compared to their elongated counterparts. We further investigated the time evolution of the total number of interpeptide salt bridges during the formation of the 18-mer of Aβ42 (Figure S8). The number of salt bridges increases considerably until the 18-mer is completely formed, when it reaches a stable value of ∼31 corresponding to ∼1.7 salt bridges per peptide. The role of electrostatic interactions in the formation of large oligomers was emphasized in a previous computational study.26 Regarding the role of interpeptide hydrophobic contacts in the aggregation process, the DTN color based on the average number of hydrophobic contacts per peptide (Figure S9) indicates that hydrophobic contacts are more present in larger oligomers. However, compact Aβ40 trimers and compact Aβ42 tetramers and hexamers also have a high average number of hydrophobic contacts (of ∼5.7, ∼ 5.9, and ∼7.5, respectively), which correlates to the observation that the compact oligomers have fewer solvent-exposed hydrophobic residues as quantified by the hSASA. For the calculation of the above-mentioned values, we selected all compact Aβ40 trimers and Aβ42 tetramers and hexamers and then computed the average 324


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Journal of the American Chemical Society β-sheet (∼8%) and helix observed (∼9%) in the Aβ40 20-mer. Interestingly, significant structural changes that differentiate between protofibrils and oligomers were shown to appear first in oligomers with lengths of 150−550 Å.50 The Aβ42 18-mer observed in our simulations is rather linear as can be seen in Figure S2 and has a length of ∼140 ± 3 Å and a diameter that varies from 21 to 71 Å. High molecular weight Aβ42 oligomers were identified in high speed AFM experiments as narrow structures (∼50 Å) with a beaded string morphology, similar to protofibrils.51 These species were considered off-pathway toward fibril formation and suggested to dissolve in low molecular oligomers that would be more prone to form fibrils. The 18-mer observed in the present study resembles a unit that leads to a beaded string morphology. Together with the Aβ40 20-mer, these are potential conformations for high molecular weight oligomers observed in experimental studies.

be related to the aggregation propensity and also toxicity of Aβ oligomers. A strong correlation between the solvent-exposure of hydrophobic amino acids and increased toxicity has been reported for oligomers of amyloidogenic proteins10 including Aβ4212 and was shown to be especially relevant for small oligomers.11

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EXPERIMENTAL SECTION

Computational Details. To perform the MD simulations, we used the Gromacs 4.5.5 parallel software package52 with a leapfrog stochastic dynamics integrator and the OPLS/AA force field53,54 with the GBSA implicit solvent.55 Five independent all-atom MD simulations where 20 Aβ monomers were free to move in a cubic box with side lengths of 350 Å were performed at a solute concentration of ∼770 μM for each of Aβ40 and Aβ42. The initial 20 monomers were a mixture of conformations with helical content based on the PDB ID 1IYT of Aβ42 and conformations with coiled structure based on previous MD simulations of Aβ42 monomer in explicit water.56 For Aβ40, we used the same starting structures but removed the last two residues. The simulations were started with different initial velocities and each had a production run of 500 ns, resulting in a total of 2.5 μs simulation time per Aβ alloform. The temperature was kept at 300 K via velocity rescaling with a stochastic term algorithm57 with a time constant for coupling of 2 ps. Both electrostatic and van der Waals interactions were calculated up to a cutoff distance of 12 Å and using a shift function to ensure that the interatom energies and forces are zero at the cutoff. Periodic box conditions were considered for the boundaries. The hydrogen atoms were treated as virtual interaction sites, permitting an integration time step of 4 fs while maintaining energy conservation.58 Transition Network Analysis. Transition networks were calculated by defining an empirical aggregation state that is optimal for describing the assembly process. The aggregation state is a combination of four numbers, N1|N2|N3|N4, that correspond to four structural features of a specific oligomer. N1 represents the oligomer size expressed in number of peptides and identified using a cutoff distance of 5 Å between any two atoms belonging to different peptides. N2 is the rounded average number of salt bridges between individual chains from the identified oligomer, while N3 is the rounded average number of hydrophobic contacts between any two peptides involved in the oligomer. Finally, N4 is a measure of how compact or elongated a particular oligomer is. It is defined as the ratio between the largest moment of inertia and the lowest one, multiplied by 10 and rounded to the nearest integer. Thus, N4 can take values between 1 and 10, where 1 corresponds to extended or elongated shapes and 10 to compact shapes. Throughout the text we call oligomers with N4 > 5 “compact” and oligomers with N4 ≤ 5 “extended”. Typical examples of extended and compact dimers and tetramers are shown in Figure S2. After testing several metrics for the transition networks, we found that the choice of these particular characteristics best describes the aggregation of Aβ, while keeping their number to a minimum. The oligomer size is an intrinsic characteristic of the Aβ assemblies. Salt bridges and electrostatic interactions have been previously shown that they play important roles in the oligomer stability43 as well as the formation of large assemblies.26 The hydrophobic interactions are hallmarks of amyloidogenic peptides and oligomers as well as of amyloid fibrils. Finally, the shape of the oligomers was an important quantity to consider, due to its role in the aggregation process, which is also more and more apparent from recent studies based on ion mobility mass spectrometry.18,20,34 We have previously considered the amount of β-sheet within an oligomer as part of the transition network metric,28,29 but due to the small changes in the secondary structure of the peptides during the initial aggregation process we decided not to include it here. The number of intermolecular hydrogen bonds used in the aggregation state definition in our previous work28 could have also been included here but at the expense of a much more complex transition network, which was not our goal. Another quantity that one could have considered is the solvent-

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CONCLUSIONS In this work, we revealed the important role of the oligomer shape during the early Aβ peptide aggregation. Differences in the oligomer size distributions and aggregation pathways of Aβ40 and Aβ42 have been previously reported with both experimental19,20 and computational26 methods. Here we demonstrated that these differences are due to compact oligomers that are less involved in the aggregation process than extended conformations. The coexistence of compact and extended conformations of the same oligomer size was previously observed in electrospray ionization IMS-MS studies34 and also AFM experiments.37 Our novel analysis of the aggregation process using transition networks reveals that extended oligomers are the ones driving the aggregation process, while the compact oligomers are metastable and participate less in the formation of new assemblies. The oligomer mass distribution of compact oligomers, except for dodecamers, is in remarkable agreement with experimental oligomer size distributions for both Aβ40 and Aβ42. The coarse-grained and detailed transition networks provide a unique way of describing the aggregation process and identifying relationships between oligomers of different size and shape. CGTNs disclosed the critical role of dimers in the formation of higher-order oligomers for the two alloforms leading to different oligomer mass distributions. On the basis of the contact map analysis and the solvent-exposure of hydrophobic amino acids, we further propose that differences in the aggregation pathways between the two alloforms already manifest themselves in different dimer conformations. The two peptides in the Aβ42 dimers form only a few contacts with each other via some of their mostly hydrophobic C-terminal residues. However, most of these and also the N-terminal residues are solvent-exposed and can thus easily interact with other monomers or oligomers. This is not the case for Aβ40 dimers and is a likely reason for the larger number of tetramers for Aβ42 compared to Aβ40 and the subsequent increased probability for compact Aβ42 tetramers. The tetramers of Aβ42 are characterized by a higher average contact frequency and by a contact pattern that involves regions of the peptide rather than scattered contacts involving only one or two amino acids as in the case of Aβ40. Both features contribute to the stability of Aβ42 tetramers and their larger frequency in the mass distribution. We further observed a considerable increase in the hydrophobic SASA of Aβ42 dimers and tetramers compared to Aβ40, which, based on previous experimental findings, can 325



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accessible surface area, particularly that of hydrophobic amino acids, though due to its indirect correlation with the number of hydrophobic contacts we decided to leave it out of the metrics and discuss it in detail for specific oligomers, i.e., dimers and tetramers. To calculate the transition matrix that includes all pairwise transitions between aggregation states, we first identified all the aggregation states and the number of transitions between states considering all five trajectories and using a lag time of 20 ps. Thus, we built a N × N matrix, where N is the number of states encountered, with the populations of transitions between any two identified states. The transition matrix was row-normalized to obtain transition probabilities between states and multiplied by a scaling factor of 1000 in order to easily import it into Gephi. In the transition network plots, the nodes represent aggregation states. The area of each node is proportional to the population of the state, while the thickness of network edges corresponds to transition probabilities between two states. The transition networks were visualized with the program Gephi,59 and the distribution of nodes was optimized using the clustering/layout Atlas2, which applies linear repulsion between nodes based on their size and quadratic repulsion between edges based on their weight (probability). A simplified transition network is shown as an example in Figure S10. Coarse-grained transition networks were derived similarly except that the aggregation state was defined as the number of peptides in the oligomer and the edges between the nodes are actual number of transitions between states. The CGTN plots were created with the software Visone.60 Oligomer Mass Distribution. For each of the five trajectories, we calculated the oligomer mass distribution at each frame, which consists of the normalized frequency to encounter a specific oligomer multiplied by the oligomer size. This ensures that the probabilities of having, for example, all peptides in monomeric conformations or all peptides in tetrameric conformations are equivalent. The average oligomer mass distribution was calculated from each trajectory, and the five final distributions were averaged into one distribution along with the standard error of the mean for each oligomer. Other Analysis. The solvent accessible surface area (SASA) was calculated using the algorithm implemented in the Visual Molecular Dynamics (VMD) software61 with a probe radius of 1.4 Å. The hydrophobic SASA was calculated by only considering the solvent exposed hydrophobic amino acids. A hydrophobic contact was defined to be present when two hydrophobic amino acids had at least two atoms within 5 Å distance. Contact maps were calculated for each frame by considering a contact between two amino acids whenever at least two atoms belonging to the two amino acids were within 5 Å distance. Average contact maps were calculated by averaging the values of each pairwise contact over the number of frames considered.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the computing time granted by the John von Neumann Institute for Computing (NIC) and provided on the supercomputer JUROPA at Forschungszentrum Jülich.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/jacs.7b10343. The effect of the simulation-time scale on the oligomer distribution, collision cross section analysis, Tables S1− S5, and Figures S1−S10 (PDF)

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Article

AUTHOR INFORMATION

Corresponding Authors

*[email protected] *[email protected] ORCID

Bogdan Barz: 0000-0001-7982-3955 Qinghua Liao: 0000-0002-2260-8493 Birgit Strodel: 0000-0002-8734-7765 Notes

The authors declare no competing financial interest. 326


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Pathways of amyloid-β aggregation depend on oligomer shape

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