Abstract
The self-assembly and fibrillation of amyloid β (Aβ) proteins is the neuropathological hallmark of Alzheimer’s disease. However, the molecular mechanism of how disordered monomers assemble into aggregates remains largely unknown. In this work, we characterize the assembly of Aβ (1-40) monomers into dimers using long-time molecular dynamics simulations. Upon interaction, the monomers undergo conformational transitions, accompanied by change of the structure, leading to the formation of a stable dimer. The dimers are primarily stabilized by interactions in the N-terminal region (residues 5-12), in the central hydrophobic region (residues 16-23), and in the C-terminal region (residues 30-40); with inter-peptide interactions focused around the N- and C- termini. The dimers do not contain long β-strands that are usually found in fibrils.
Introduction
The self-assembly of amyloidogenic proteins is related to several neurodegenerative diseases(1–3). According to the amyloid cascade hypothesis, self-assembly of amyloid β (Aβ) is the primary model for the development of Alzheimer’s disease (AD) (1, 4). The final products of the amyloid self-assembly process are fibrillar structures that contain long β-strands(5–7), whereas Aβ monomers are largely unstructured(8–10), which leads to the question of how the conformational transition occurs during self-assembly.
Recent compelling evidence show that amyloid oligomers rather than fibrils are the most neurotoxic species (11–15). The neurotoxicity of Aβ oligomeric species have been attributed to intracellular, membrane, and receptor-mediated mechanisms (16–27). Various morphologies have been ascribed to oligomers, from spherical aggregates to filamentous (28, 29). It is proposed that oligomers form the critical entities, called nuclei, needed to transition to proto-fibril states before finally fibrillating (30). Spectroscopic characterization of Aβ oligomers revealed that they are composed of random coil secondary structure, which is able to transition to β-structure as the aggregation progresses (30–33). Sarkar et al. showed that the oligomer chemical shifts are very different from fibrils, in particular the N-terminal and the central segment (residues 22-29) (32). These finding are in line with the data from Ahmed et al., which show that oligomers have disordered molecular conformations (30).
There are two principle alloforms of amyloid β proteins, Aβ (1-40) and Aβ (1-42), defined by the number of residues; with the former being the most abundant and the latter the most aggregation prone and neurotoxic (34–40). Despite the small structural difference (two amino acids) between the Aβ40 and Aβ42 alloforms, they display distinct behavior, although the structural basis for this is unknown (37–41). Hence, a detailed characterization of these oligomeric forms of Aβ is important for understanding neurotoxicity and pathology in AD. Recent studies have demonstrated that single-molecule approaches are powerful methods to study oligomers (42–45). Single-molecule techniques, such as AFM (46–51), tethered approach for probing inter-molecular interactions (TAPIN) (52, 53), and FRET (32), have shown that the early stage oligomers exhibit prolonged lifetimes and stabilities. Novel features of the interaction and self-assembly of Aβ40 and Aβ42 peptides were determined using single-molecule AFM-based force spectroscopy (47). However, due to their transient nature and heterogeneity many questions about the oligomer formation process structure and the structure and dynamics of Aβ oligomers are left unanswered (54, 55).
Computational simulations have been utilized to supplement the novel single-molecule techniques used to probe early stages of aggregation and, in some cases, elucidate the dynamics and mechanism of aggregation (50, 56–60). Computational studies of the dynamics of Aβ42 lead to the discovery that, in an aqueous environment, the protein mainly assumes α-helical structure (61). However, the helices are not stable and transition between structured and unstructured conformations multiple times. Further studies showed that Aβ42 is more structured compared to Aβ40 and has a less flexible C-terminal segment (57). These findings are in line with the comparison of Aβ40 and Aβ42 by Yang and Teplow, which showed that Aβ42 forms more stable conformations that tend towards β-structure and stable C-terminus (62). More recent simulations have revealed that the size and distribution of the early aggregates for Aβ40 and Aβ42 vary, the most common oligomer being dimers for the former and pentamers for the latter (63, 64). These results qualitatively reproduce the main features of oligomer size distributions measured experimentally (41, 65). Furthermore, Aβ42 displayed turn and β-hairpin structures that are absent in Aβ40.
Biased simulation strategies using a coarse-grained force field has also been employed to investigate the aggregation pathway (66). Zheng et al. demonstrate that the while pre-fibrillar oligomers typically consist of antiparallel β-structure they are distinct from fibrillar structures and very dynamic. These structural characteristics are also demonstrated for the Aβ40 dimer in the findings of Tarus et al., which show that dimers are compact conformations with inter-peptide antiparallel β-structures (67). Similar observations were also reported by Watts et al. using a different force field (68). However, how the structures of oligomers contribute to neurotoxicity remain unclear. Leaving the fundamental questions related to the mechanism of oligomer self-assembly and dynamics unanswered. Which, in turn, has impeded the progress in the development of treatment for these diseases.
We recently characterized the conformational changes in monomers of Aβ42 peptide upon dimer formation using long time-scale all-atom molecular dynamics (MD) simulations (69). The simulations revealed that the dimer is very dynamic and resulted in a multitude of different conformations being identified. By utilizing the recently developed Monte Carlo pulling (MCP) approach (58), we were able to identify the most likely native conformations of the Aβ42 dimer, which generated statistically similar dissociation forces and interaction profiles as was observed in AFM experiments.
Here, we applied the developed MD simulation strategy to analyze the dimer formation of full-length Aβ40 protein using the specialized Anton supercomputer (70, 71). A variety of dimer conformations were identified, all with small segments of ordered structures and lacking the characteristic β-sheet structures found in amyloid fibrils. These dimers structures were then validated using MCP simulations and by comparing with stability and interaction data obtained from AFM-based force spectroscopy experiments. The validated dimer conformations were then used to compare Aβ40 and Aβ42 dimers and characterize the differences between the interaction of monomers and the resulting dimers.
Materials and methods
Monomer simulation procedure
To generate the initial structure of the monomers used for the dimer simulation, we conducted all-atom MD simulations using GROMACS ver. 4.5.5 (72) employing Amber ff99SB-ILDN force field(73) and the TIP3P water model(74). The initial monomer structures were adopted from NMR data(8) (PDB ID: 1AML) and an extra N-terminal Cys residue was added to mimic experimental sequence (69). The monomer was then solvated, neutralized with NaCl ions, and kept at 150 mM NaCl concentration. After which 500 ns NPT MD simulation, at 1 bar and 300 K, was carried out. Cluster analysis was then performed using the GROMOS method of clustering and root-mean square deviation (RMSD) as input for the protein backbone with a 3Å cut-off value, as previously described (50).
Dimer simulation on the specialized supercomputer Anton
The initial dimer conformations were prepared in the Maestro software package (Schrödinger, New York, NY) using the same force field and water model as for the monomer MD simulation. Dimer conformations were created by placing two copies of the representative monomer, cluster 1 in Fig. S1, at 4 nm center of mass (CoM) distance. Two configurations were created, parallel and orthogonal (90° rotation between the two monomers with respect to the long peptide axes). The dimers were then solvated, neutralized, and kept at 150 mM NaCl concentration after which they underwent 50 ns NPT simulation to relax the system. They were then submitted for 4 μs MD simulation on Anton.
We monitored secondary structure dynamics according to the method developed by Thirumalai’s group (75). Briefly, if the dihedral angles from two consecutive residues satisfy the definition of an α-helix (−80° ≤ ϕ ≤ −48° and −59°≤ ψ ≤ −27°) or β-strand (−150° ≤ ϕ ≤ −90° and 90 ≤ ψ ≤ 150°), the structures are considered to be α or β conformations, respectively. The changes of secondary structure over time are monitored by, and , where t =s and Δ=1 ns. When the residues adopt α- or β-conformations, the δi,α = 1 or δi,β = 1.
Accelerated MD simulations
To further extend conformational sampling, the resulting structures from the MD simulations on Anton were subjected to the accelerated MD (aMD) simulation method. The simulation procedure was adopted from the description by Pierce et al. (76) and the website (URL: http://ambermd.org/tutorials/advanced/tutorial22/) using Amber 14 software package (77). Briefly, dimer conformations from the last frame of the MD simulation on Anton and the from the two lowest energy minima were solvated, neutralized, and kept at 150 mM NaCl before being submitted for 500 ns aMD simulations. Simulations utilized the same force field and water model as previous simulations.
The principal component analysis of backbone dihedrals (dPC) (78) was used to calculate the energy landscape and identify the representative structures of the minima. The Fortran program (78) written by Dr. Yuguang Mu was used to perform this analysis. Intra-peptide contact probability maps were generated based on Cα atom contacts within the monomers using the GROMACS mdmat analysis tool.
Monte Carlo pulling simulations
The Monte Carlo pulling (MCP) method was performed to simulate AFM force spectroscopy experiments using our previously described procedure (58) and a modified PROFASI package (79). Briefly, the two Cα of the N-terminal Cys residues of each monomer were defined as the pulling groups. A virtual spring was attached onto each pulling group and used to stretch them during the pulling process. The energy dynamics of the spring were calculated using the A2A spring function (PROFASI package) with the total energy during the course of pulling described by, where E(x) describes the energy without an external force, k and t are the spring constant of the virtual spring, and L0 is the initial distance between the two Cα atoms. L(x) represents the real-time distance between the Cα atoms while x denotes the protein conformation being probed. When v = 0.083 fm per MC step, it mimics the pulling speed of 500 nm/s; which was used for all MCP simulations.
Results
Aβ40 Monomer Structure
We performed all-atom MD simulations of Aβ40 monomers to identify the most representative monomer structure. We adopted the approach from our recent simulations of the Aβ42 dimer (69). Briefly, the Aβ40 monomer structure was simulated for 500 ns, the most representative structure was then identified using cluster analysis by calculating the RMSD of backbone atoms between all pairs of structures with a cut-off at 3Å (80). The results of the cluster analysis are shown in Figure S1. Twelve clusters were identified, with the 1st cluster comprising 47.5% of the entire population. The representative structure of this cluster contains a large α-helical segment in the central region of the peptide and is otherwise unstructured. Two copies of this structure were used to characterize the dimer conformation.
Characterization of Aβ40 Dimer Formation
Two dimer systems were generated by placing copies of the monomer structure in orthogonal (90°) or parallel orientations, with respect to the long peptide axis, at 4 nm CoM distance, Figure 1 right column. Both dimer conformations were then simulated for 4 μs on the special purpose Anton supercomputer.
To determine if the dimer simulations had converged, we monitored the time-dependent change in secondary structure of the peptides, Figure 1 left column. The graphs show that for the orthogonal configuration, α-helical content fluctuates with a decreasing tendency up to the 1 μs mark, after which the helical portion increases over the next 1 μs span, Figure 1a. Meanwhile, the β-content remains stable at approximately 5%, with minor fluctuations, until approximately 3.5 μs; after which a conversion from α-helical to β-structure is observed, with β-content reaching a maximum of ~12% at the end of the simulation. For the parallel configuration on the other hand, both α-helical to β-structure content fluctuate throughout the simulation, with averages of approximately 15% and 5%, respectively, Figure 1b. This suggests that, for both configurations, a local equilibrium state has not been reached.
We then used dPC analysis to analyze the energy landscape of the dimer. For both dimer configurations, several distinct energy minima were found, Figure S2Error! Reference source not found‥ Furthermore, both configurations show a rough and discontinuous energy landscape. This, in combination with the time-resolved change in secondary structure, suggests that the dimers are trapped in local energy minima, leading to insufficient sampling of the conformational space. To overcome this problem and to enhance the sampling of the conformational space, we extended the dimer simulation using aMD simulations (see specifics in Methods) allowing us to reach sampling enhancement by several orders of magnitude (76).
Accelerated Molecular Dynamics Simulations of Dimers
The result of the aMD simulations for the dimer is presented in Figure S3. Several well-defined and separated energy minima were identified for the orthogonal system, Figure S3a, while the parallel system only has few energy minima that are clustered in the same region of the energy landscape, Figure S3b. The aMD results were then pooled and the concatenated data set underwent dPC analysis again, Figure 2a. The snapshots in the figure depict representative structures from the two lowest energy minima. It is evident, that the dimer does not adopt long β-structures but has a mixture of short helices and β-structures.
The secondary structure of the dimers was characterized using DSSP (81). Each monomer was investigated separately with the results being displayed as residue specific probabilities, Figure 2b. Monomer 1 shows greater than 40% propensity for helix formation in residues 3-7, 11-13, and 25-29. β-structures are overall less likely compared to helices, however regions 10-30 and 35-38 have on average greater than 20% chance of β-structures. Monomer 2 on the other hand is more diverse, the helix probability is localized around residues 11-20, while collectively β-structures are more probable in the N- and C-terminal segments in residues 3-10 and 21-38, respectively.
To analyze the conformational diversity of the dimers we performed cluster analysis using the pooled aMD data. Similar to the analysis performed for monomers, clustering was performed using RMSD of backbone atoms between all pairs of structures with a cut-off at 3Å. Representative structures for the first 20 clusters are depicted in cartoon representation and relative populations on Figure 3. Structurally the clusters, with few exceptions, exhibit similar trends of low α-helical and β-structural content and high degree of unstructured regions. The main difference within the clusters arise from the different configurations of monomers.
To identify segments important for the interaction of Aβ40 monomers, we performed analysis of the pair-wise residue interactions. Intra-peptide contact probability maps were generated based on Cα atom contacts within the monomers, Figure S4. For Monomer 1, interactions in three segments stand out, residues 5-12, residues 16-23, and residues 30-40, Figure S4a. The interactions within these three segments reveal that the monomer during the simulations, with high probability, is found in a compact turn-like conformation with C-terminal interacting with the central segment of the peptide. Monomer 2 on the other hand is more dynamic with few residues interacting within the N-terminal region and the 16-23 segment, Figure S4b. The interaction patterns of the two monomers reveal that, apart from neighbor residue interactions, the main difference is found in the way the two monomers interact with the 16-23 region; for Monomer 1 the interaction happens with residues 33-38, while for Monomer 2 it is residue 28-32, Figure 4a.
The inter-peptide interactions of the dimer were obtained using the pair-wise interactions of Cα atom between the monomers, Figure 4b. The contact map reveals that the interactions between the two monomers occur in the central region of the peptide as well as between the N-and C-terminals and the two C-termini. Comparison of the contact data and the dimer structures, revealed by cluster analysis, Figure 3, shows that the 20 most populated clusters are a mixture of different conformations that all contain N-C terminal interactions, with a few configurations also containing C-C terminal interactions. Monomer 1 primarily interacts through its central and C-terminal segments, while Monomer 2 interacts through the N- and C-terminal regions.
1.1.1 Validation of Dimer Conformations
To validate the simulation results as well as identify the experimentally relevant conformations we used the Monte Carlo pulling approach to simulate AFM pulling experiments and to compare the simulated results with the experimental data. The rupture force and interaction patterns for the top candidates are presented in Figure 5. The interaction patterns of the simulated dissociation processes were normalized with respect to the experimentally obtained contour lengths. Experimentally observed values for the dissociation force was 56.6 ± 20.5 pN (STD), approximated using a Gaussian distribution, with a two-peak distribution of the interaction pattern favoring interaction in the N-terminal and central regions (47).
The dimer obtained following analysis of the MD simulations on Anton, named “MD” on Figure 4, shows a distinct three-peak interaction pattern, with majority of interactions located in the N-terminal and central regions of the proteins, while the dissociation force is 36.5 ± 18.4 pN. Dimer conformations from the two most populated clusters (Clu 01 and Clu 02) from Figure 2 (following the aMD simulations) produce rupture forces of 61.7 ± 27.5 pN and 35.6 ± 17.7 pN, respectively. Similar to the MD dimer, the two aMD conformations produce the distinct three-peak interaction pattern. However, Clu 01 shows a very large C-terminal peak. The dissociation of dimer Clu 01 is statistically similar to the experimentally observed results, using a non-parametric two-sample Kolmogorov-Smirnov with 0.05 significance.
To characterize the interaction pattern and the dissociation force of a dimer (within fibrils) with high β-structure content, we created two dimer conformations from NMR structures of Aβ40 fibrils with different morphologies (PDB IDs: 2LMN (wild-type) and 2MVX (Osaka mutant)). The dissociation patterns for the two fibril dimers are significantly different compared to experimental results and the results obtained for the MD and aMD dimers, Figure S6. Although, the fibril dimers contain the three-peak interaction pattern, the patterns are significantly different; for the 2LMN dimer the majority of interactions happen within the central part of the dimers, while for 2MVX dimer the interactions are dominated by the N- and C-terminals.
1.2 Discussion
Although the behavior of Aβ peptides have been subject to numerous studies, our present study presents a number of new features about the Aβ40 dimers. The equilibrated monomer structure, used as the initial conformation to characterize the dimerization process, is in line with recent data obtained using NMR and simulations of the Aβ proteins, which showed that the monomer has unstructured segments and can assume helical secondary structure (10, 82). Another interesting feature of the monomer structure is the presence of a turn on each side of the central helix, the turn conformation is believed to be the first folding event in the structural transition of Aβ proteins and important for the aggregation process (5, 83, 84).
Our computational analysis of the aggregation of Aβ40 into dimers reveal a broad range of peptide structures and very dynamic feature of the dimers. In particular, we did not identify significant β-conformation in the monomers within the dimer, Figure 3. The interaction of two monomers lead to conformational transitions within the monomers, accompanied by change in local structure of the peptides, leading to the formation of a stable dimer. Investigation of the dimer structures showed that the Aβ40 dimers exhibit a heterogeneous ensemble of conformations that contain a diverse number of structures. Dimers are primarily stabilized by interactions in the N-terminal region (residues 5-12), in the central hydrophobic region (residues 16-23), and in the C-terminal region (residues 30-40); with inter-peptide interactions focused around the N- and C- terminals. The 20 most populated clusters are a mixture of different conformations that all contain N-C terminal interactions, with a few configurations also containing C-C terminal interactions. Similar observations regarding the interaction pattern of Aβ40 dimers have been presented by Tarus et al. (85). The authors showed that regions, identified in our simulations, were also interacting and important for the stability of the dimer. However, unlike the dimer conformations identified here, their dimers contained significant β-structure content. More recent findings from the same group (86) show that the dimers structures are more diverse and do not contain a large extent of β-structure, and that the dimer is stabilized by nonspecific interactions. The low β-structure content is in agreement with our findings, and also can explain the role of structural plasticity in the interactions of Aβ oligomers with binding partners and ultimately their toxicity. The structural flexibility of the dimer may also play a role in the aggregation progression, where the free energy cost of transitioning from less ordered states is much less compared to dimeric states with high level of ordered β-structures.
We validated the dimer conformations using MCP approach to simulate the force-induced dissociation of the dimers and compared the obtained force and interaction patterns with experimental results. The simulations were performed at conditions identical to the experimental ones and allowed us to identify the dimer conformation of Clu 01 as the most probable dimer probed during experiments. Probing of dimer conformations with high degree of β-structure content, adopted from fibril structures, showed that such dimers produce dissociation forces significantly different compared to experiment as well as our simulated dimers. Furthermore, the interaction pattern of high β-content dimers was strongly shifted compared to experiments.
Comparing the Aβ40 dimer with the Aβ42 dimer, analyzed in our recent publication (69), shows that the Aβ42 dimer is stabilized by interactions in the central region (residues 16-23) between the two monomers as well as C-C terminal interactions through residues 30-36 and 36-42. Interactions also occur between the N-termini of the two monomers. Suggesting that the two extra C-terminal amino acids of Aβ42 affects the spatial orientation within the dimer as well as the inter-peptide interaction pattern of the monomers. These finding are in line with recent finding about the monomeric Aβ peptides (82), which show that while the two alloforms show similar structural elements, their conformations are different and that in turn has a large effect on the inter-molecular interactions of the peptides.
1.3 Conclusions
All-atom MD simulations allowed us to structurally characterize Aβ40 dimers. Structures were organized in clusters, with ~54% represented in the 20 most populated clusters. Dimers are stabilized by interactions in the central hydrophobic region (residues 17-21) as well as N-C terminal interactions (residues 1-10 and 30-40), through hydrophobic interactions and H-bonds. Aβ40 dimer did not show parallel in-register β-sheet structures, as one may expect based on the known structures of Aβ fibrils. Comparison of Aβ40 to Aβ42 dimers revealed differences in their conformations. Aβ40 dimers are stabilized primarily by interactions within the central hydrophobic regions and the N-terminal regions, whereas Aβ42 dimers are stabilized by interactions in the central and C-terminal regions. Aβ40 dimers are more dynamic compared to Aβ42 dimers. Comparison, based on MCP simulations, between Aβ40 and Aβ42 showed that overall, the dimers of both alloforms exhibit similar interaction strengths. However, the interaction maps, and more importantly the patterns, clearly show differences.
List of abbreviations
- AD
- Alzheimer’s disease
- AFM
- Atomic force microscopy
- aMD
- Accelerated MD
- CoM
- Center of mass
- dPC
- Dihedral principal component
- DSSP
- Define secondary structure of proteins
- FRET
- Förster resonance energy transfer
- MCP
- Monte Carlo pulling
- MD
- Molecular dynamics
- NPT
- Isothermal-isobaric
- PDB
- Protein data bank
- RMSD
- root-mean square deviation
- TAPIN
- Tethered approach for probing inter-molecular interactions
Author contributions
MH, YZ, and YLL conceived and designed the overall study. MH and YZ performed the Aβ40 simulations and analyzed all the simulation data. Z.L. performed the analysis of the experimental dataset. All authors discussed the results and wrote the paper.
Competing interests
Authors declare no competing interests.
Additional information
Supplementary Information accompanies this paper at: http://
Acknowledgments
This work was supported by NIH grants GM096039 and GM100156, NSF grant 1004094, and the PSCA14025P award for computer time on Anton at Pittsburgh Supercomputing Center (PSC) - all to YLL. MH was supported by the Bukey Memorial Fellowship. Computational modeling was performed using facilities of the Holland Computing Center at the University of Nebraska (supported by the Nebraska Research Initiative) and the San Diego Supercomputing Center at the University of California San Diego through the Extreme Science and Engineering Discovery Environment (XSEDE; supported by National Science Foundation [ACI-1053575 for XSEDE]). Anton computer time was provided by the PSC through Grant R01GM116961 from the NIH. The Anton machine at PSC was generously made available by D.E. Shaw Research. We thank Dr. Yuguang Mu for providing the dPCA Fortran program.
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