Exploring Amyloid-beta Dimer Structure using Molecular Dynamics

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Exploring Amyloid-Beta Dimer Structure Using Molecular Dynamics Simulations Banafsheh Mehrazma, and Arvi Rauk J. Phys. Chem. A, Just Accepted Manuscript • DOI: 10.1021/acs.jpca.8b11251 • Publication Date (Web): 13 May 2019 Downloaded from http://pubs.acs.org on May 13, 2019

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The Journal of Physical Chemistry

Exploring Amyloid-beta Dimer Structure using Molecular Dynamics Simulations Banafsheh Mehrazma and Arvi Rauk* Department of Chemistry; University of Calgary; Calgary AB, Canada T2N 1N4 *[email protected] Abstract: A major hallmark of Alzheimer’s disease (AD) is the aggregation of Amyloid-beta peptides in the brains of people afflicted by the disease. The exact pathway to this catastrophic event is unknown. In this work, a total of 9.5 s molecular dynamics simulations have been performed to investigate the structure and dynamics of the smallest form of toxic A oligomers, i.e. the A dimers. This study suggests that specific hydrophobic regions are vital in the aggregation process. Different possible structures for A dimers are reported along with their relative binding affinity. These data may be used to design better A-aggregation inhibitors. The diversity of the dimer structures suggests several aggregation pathways. Introduction: In 2018, the link between Alzheimer’s disease (AD) and the secondary structural changes of the unbound disordered β-amyloid peptide (A) monomer to high content sheeted and misfolded aggregates became unquestionable.1 Based on the most convincing theory on AD, i.e. the β-amyloid peptide (Aβ) hypothesis,2–5 Alzheimer’s disease is initiated by the imbalanced generation and clearance of A.2–4,6 The A peptide in the brains of patients with Alzheimer’s disease is fifteen times more concentrated than in healthy brains.7 Aβ is found in different lengths, generally 39-43 amino acids.3 The most cytotoxic form is A428 with the sequence of DAEFR5HDSGY10EVH13H14Q15KLVFF20AEDVG25SNKGA30IIGLM35VGGVV40IA42 Not well-known structural changes in A peptide eventually lead to aggregates with high sheet content. i.e. oligomers or fibrils. It is believed that the oligomeric A is the most cytotoxic species of the peptide.2,9–17 An in vitro study has ranked the toxicity of the oligomers as tetramer> trimer> dimer.18 Nonetheless, it should be noted that there is a considerable unreliability about the ranking of toxicity in different orders of oligomers, because of the variety of conditions in each specific experiment.19

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The structural changes of A that lead into accumulation may arise from the high content of aromatic groups in this amyloidogenic peptide.20 To be more specific, there are a few highly aggregation-prone regions in A, i.e. the central hydrophobic core (CHC) within K16LVFF20, a second hydrophobic region (SHR) within G29A30IIGLM35and the C-terminal (C-Ter), VGGVV40IA42. Based on MD simulations21 and a Pro mutagenesis study,22 strong intermolecular interactions have been identified in these highly -sheet prone regions of A; i.e. CHC, SHR, C-termini. Similarly, in a distinct MD simulation study, Leu17-Ala21 (CHC) and Val39-Ala42 (C-terminal) were identified to be major regions in the intermolecular interactions between the two monomers.23 Additionally, by solid-state nuclear magnetic resonance (NMR) studies the main feature in the higher order oligomeric forms of A (150-kDa), was discovered to be anti-parallel -sheets at the C-terminal region, at Ile32-Val40 of one monomer and Met35-Gly37 of another strand.24 Likewise, in an electron cryo-microscopy (cryo-EM) study, fibrils were found to be comprised of two C-terminal interacting subunits.25 These hydrophobic regions have become the target of many anti-A-aggregation agents, and consequently have shown inhibitory effects.26,27 Rauk et al., have studied the binding of a series of designed pseudo-peptides to A13-23; designated as R (= the recognition site). However, based on the mentioned studies, there might be other regions in the peptide suitable as binding targets, as well. If a specific binding site in these aggregates is identified, a drug candidate with more specificity could be established. For this reason, knowing the structures of these aggregates can shed light on the potency and specificity of different drug candidates aiming for AD. Additionally, uncovering the structure of transient aggregates of A could pave the way to understand how these specified hydrophobic regions can propagate into catastrophic effects that lead to AD. It is expected as smaller A oligomers, including dimers, contain less -sheet content, they would be more flexible than the more structured fibrils. Thus, more flexibility of oligomers could be a reason that their toxicity is higher than fibrils, as they are able to interact with more different molecules by changing their conformation. Meanwhile, comparing Aβ42 with Aβ40, the higher toxicity of Aβ42 may be attributed to its higher -sheet content that may 2 ACS Paragon Plus Environment

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produce a specific conformation suitable for interactions autocatalytic for -sheet formation and aggregation.23,24 Aβ42 with only two more residues is more cytotoxic than Aβ40, has been characterized to have different patterns of oligomerization.8 However, the exact difference in the structures of their oligomers is yet unknown. It should be pointed out that unraveling the structures in the oligomerization process of A, is hindered by difficulties both in experimental studies and computational studies. In initial stages of oligomerization, the aggregation rate is on a time scale of about 1 s. As a result, obtaining high-resolution structures of these small oligomers is a formidable task.28 The results from experimental studies are highly sensitive to the conditions of the performed experiments. Teplow and Hayden, in their review, have indicated the complications with tissue homogenization, cell lysis, and extraction techniques, change of concentration, and behavior of A oligomers.10 Hence, the smallest changes in experimental conditions can alter the distribution of A oligomers and hence may alter the results in SDS-PAGE, Western blotting, size exclusion chromatography, etc. In addition to the problem of the reproducibility of in vitro experiments, Doig et al. have discussed different aspects of the redundant issues in A studies, including high concentrations of A used in vitro, cell, and animal models compared to human brains.29 They also refer to the poor characterization of oligomer conformations, due to the fast equilibrium between different oligomer states.29 Doig et al, also point out the shortcoming of many studies in truncating the A segment, by referring to the renowned different behaviors of A40 and A42 with only two amino acids difference.29 In a similar vein, molecular dynamics (MD) simulation studies of flexible proteins are computationally highly expensive if one wishes to observe any proper conformational changes. As a result, many computational studies are far from equilibrium. Few approaches have been taken to tackle this problem. One technique is cross-linking of some regions of two monomers to each other, but it has the disadvantage of higher compact structures.30 A particular and realistic approach is to carry out several “reasonable” short MD simulations with different starting structures. This will increase the possibility to obtain a conformation close to the global energy minimum. In 2012, Zhu et al, performed MD simulations on 8 different starting 3 ACS Paragon Plus Environment


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structures of A42 dimers with the GROMOS force field 53a5.21 However, the MD simulations were only 100 ns long. For a system as flexible as the A42 dimers, this short amount of time is not enough for equilibration of the system, and not suitable for data analysis. Nonetheless, this MD simulation was able to predict -sheet interactions in the most stable structures, consistent with the experimental results that propose the importance of the A25-35 region in aggregation.24,31,32 To be more specific, these consisted of antiparallel C-terminal-SHR (CTerSHR-antiparallel) -sheet, parallel N-terminal-N-terminal (NTer-NTer-parallel) -helical or parallel SHR-SHR -sheet (SHR-SHR-parallel) structures.21 The parallel SHR-SHR interaction which is through Lys28-Gly33 with Ala30-Gly37 of the other strand was identified as the most stable structure.21 Zhu et al, also used the OPLS force field and found different structures for A42 dimers.21 However, the use of the GROMOS force field seems to be more appropriate as it was able to reproduce some experimental results.21 One should note that A dynamics and structure is known to be highly dependent on the force field used. For instance, Man et al, have compared the dimer structure of four different force fields: OPLS-AA, CHARMM22*, AMBER99sb-ildn, and AMBERsb14, and they discovered substantial differences in tertiary and quaternary structure in all different force fields.33 In another example, Somavarapu et al. have benchmarked a series of force fields against an NMR structure (pdb ID=2LFM), although in different conditions.34 The NMR structure of 2LFM is retrieved in PH=7.3, 15˚C, 50 mM NaCl, and it contains a -helix in residues H13-D23.35 We note that by contrast, Zhang, et al, deduced a structure by NMR at 10° that had no -helix or -sheet content.36 A series of GROMOS9653a6 calculations in PH= 7, 27˚C, 150 mM NaCl on the 2LFM NMR structure produced a highly -sheeted conformation, unlike Amber03 or Charmm22 force fields. This result is in opposing to what has been observed by Gerben et al. that Gromos96-53a6 represents the most accurate Cα chemical shifts compared to Amber03 or Charmm22 force fields.37 Accordingly, Gerben et al. have recommended the use of GROMOS force field for A dynamics studies. In a separate and more recent investigation, Robustelli et al. have defined all the pre-existing force fields overestimate the compactness of the disordered proteins, and similar to Somavarapu et al. they partly attribute this issue with the water models’ inadequate description of the dispersion forces.38 4 ACS Paragon Plus Environment

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The rational choice of oligomers to start exploring the structures of A aggregates is “dimers”, since these are the simplest neurotoxic A oligomers.39–41 A dimers are not only present in the brains of Alzheimer’s disease patients,42 but they are also identified to have the highest population among different categories of aggregates in human AD postmortem brains.9 They undoubtedly impair the brains in rats.19,43,44 In this investigation, we ask whether the proposed simulation times, about 1000 ns, are sufficient for A42 dimers to evolve into conformations that can serve as binding sites, either as seeds for further oligomerization, or for attachment of ligands that may prevent further oligomerization. The main focus is the R segment, which contains the CHC region, as well as two histidine amino acids known to be important in a metal neurotoxicity pathway of A. All starting structures involved R in the intermolecular interactions. However, the structures are dynamic during MD simulations, and other interactions may appear, as well. Energy analysis is performed to obtain the relative stability of the different resulting structures. Methods: AutoDock 4.245 was chosen to dock the two A monomers to each other. The initial structure of A42 was taken from a 700 ns simulation (Figure 1-a).46 The region between residues His13 to Asp23, the recognition site R, of this structure was manually opened up in a strand by using GaussView 4.1.2 (Figure 1-b). The Autodock docking box was assigned in such a way to enclose this region. In the docking process, one A is taken as the “protein” and the other as the “ligand”. The sidechains of R of the A peptide assigned as the ligand were allowed to be flexible. Seven of the dock poses were chosen for MD simulations. Henceforth, we call A42 simply as A



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Figure 1. (a) Reference geometry of A42, where cyan color represents the R segment (b) Extended geometry of A42 at R region for docking. (c) The AA’–a starting structure, as an example of the docking output. The flexible chain in the docking process is colored in red, with its R region in pink.

The chosen AA’ complexes were placed in a cubic box with a dimension of 9 x 9 x 9 nm3. Each system was solvated by the simple point charge water model (SPC).47 Each A monomer has a total charge of -3. In order to neutralize each dimeric system, 6 Na+ ions were added. Next, a 10000 step steepest descent energy minimization, followed by a 100 ps positionrestrained MD simulation was performed to stabilize the system. Molecular dynamics simulations were carried out by GROMACS 4.6.5 software48 and the GROMOS96 53a5 force field.49 The GROMOS force field is a united aliphatic C-H atom force field. The GROMOS force field has been used by many other research groups to study the structure of disordered peptides.50,51,52,53 Particle mesh Ewald was chosen for long range electrostatic interactions. Twin-range approach was chosen for neighbor searching. Both van der Waals and electrostatic cut-offs were chosen to be 1 nm. All bonds were constrained by LINCS algorithm.54 The NoseHoover temperature coupling was chosen to maintain the temperature at 310 K.55,56 The Parrinello-Rahman pressure coupling57,58 was used to keep the pressure at 1 bar, with a coupling constant of 1 ps. In order to perform energy analysis, it was required to assign each A monomer, and the solvent, as distinct energy groups.


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Each simulation was performed for about 1 s, with a time step of 2 fs. VMD software was used for visualizing the structures.59 The time scale, about 1 s, is not long enough for the system to fully equilibrate, i.e. reach a point where the ensemble includes the global minimum structure. However, we expect that local equilibrium may be achieved. The extent of equilibration and the approach to equilibrium is assessed by cluster analysis, a procedure in which root mean square deviation (RMSD) is used as a criterion of similarity. In order to carry out cluster analysis, a RMSD cut-off of 0.3 nm was considered for grouping similar structures based on backbone atoms. The structure with the greatest number of similar structures, i.e., the conformation with the highest population, is labelled Cluster 1. Cluster 2 would have the next highest population, and so forth. We expect that as equilibrium is approached, the final structures in each simulation should have the highest populations, i.e., the lowest cluster numbers.

In an

equilibrated system, the relative populations may be used to obtain free energy differences, but this is likely not applicable in the present case, even between structures from the same simulation, and certainly not when comparing structures from different simulations. Therefore, each interesting ensemble from cluster analysis was subjected to a more sophisticated energy analysis (see next section). The secondary structural analysis of each reported cluster was performed by using STRIDE software.60 The percentage of coil, turn, and strand features in each system is reported in Table S1. Relative Energy Determination: To evaluate the free energy change of binding of the two peptide monomers, Gbinding, for the reaction (1), several approximations have been applied. 2 Aaq, monomer  A*A'*aq, dimer

Gbinding

(1)

with Gbinding = Ggas(A*A'*) + Gsol(A*A'*) – 2 (Ggas(A) + Gsol(A)).

(2)

where the asterisked terms, A* and A'* indicate that the structures of the two A strands are as in the dimeric form, A*A'*aq. In the following, by “gas-phase” we mean all potential terms, 7 ACS Paragon Plus Environment


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V, that do not involve the solvent and are calculated with a dielectric constant of 1. Thus, Ggas(A*A'*) contains internal gas-phase electrostatic (polar) and van der Waals (nonpolar/Lennard-Jones) terms for each monomer A* and A'*, Vgas(A*) = Vgas,es(A*) + Vgas,vdW(A*) and Vgas(A'*) = Vgas,es(A'*) + Vgas,vdW(A'*), and the gas-phase intermolecular interaction between them, Vint(A*-A'*) = Vint,es(A*-A'*) + Vint,vdW(A*-A'*). The GROMOS96 53a5 force field could be used to calculate all the energy components in equation (2). All these energy components are “ensemble averaged”, i.e., averaged over all members of the cluster. In general, Linear Interaction Energy (LIE) theory assumes that in the process of ligand binding, no conformational changes are observed in the protein, while it considers the ligand to undergo structural changes. Hence, LIE calculates the binding free energy based on the changes in surroundings of the bound and unbound ligand. The standard LIE (LIE-S) method, calculates the energy of binding in equation (2) as: GbindingLIE-S ≈ Vint(A*-A'*) + Gsol(A*A'*) – Gsol(A)

(3)

The first two terms on the left hand side of the equation (3), are the two different components of interaction of A with its surroundings. Vint(A*-A'*)) is simply the interaction energy of A with A'. The term Gsol(A*A'*) in equation (3) accounts for the interaction of the ligand with water (The underline for (A*) implies that only one A is considered in the calculation, as the other chain (A') is considered rigid, and hence cancels. We have overcome this shortcoming of LIE-D method, as will be discussed later). Gsol(A) stands for the interaction of A monomer with water. The electrostatic and van der Waals components of all the interaction energies Vint(A*-A'*)), Gsol(A*A'*) and Gsol(A) are separated with different weighting parameters  and , respectively: Vx(z) =  Vx,es(z) +  Vx,vdw(z)

(4)

where, x refers to either the gas phase intermolecular interaction energy (Vint) or the interaction energy with solvent (Vsol), and z refers to the type of interactions. It should be


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emphasised that the Gsol terms are derived from the GROMOS-calculated solvent interaction energies, Vsol, after application of a modification of linear response theory (equation (4)).61 Based on LIE-S method the parameters  and  are 0.161 and 0.5, respectively. Thus, the electrostatic and van der Waals components of the interaction of A (considered as ligand) with solvent and A' (considered as protein) are scaled differently and contributions from A' are assumed to cancel. GbindingLIE-S

≈

(Vint,es(A*-A'*) + Vsol,es(A*A'*) – Vsol,es(A)) + (Vint,vdw(A*-A'*) +

Vsol,vdw(A*A'*) – Vsol,vdw (A)) + 

(5)

where,  is a a fitting constant. In LIE-S, it is taken as zero. The LIE-S method has been improved by using a training set of free energy perturbation-derived energies to fit the parameters  and . As a result, a constant value of  = 0.18 was adopted,62 while the reference value,  = 0.43, was adjusted based on different functional groups present in the A peptide.63 The LIE-D procedure is a further tweaking of the LIE-S method,64 by including electrostatic intraligand terms, Vgas,es(A*A'*) and Vgas,es(A) into Equation (6): GbindingLIE-D

≈

(Vgas,es(A*A'*) + Vint,es(A*-A'*) + Vsol,es(A*A'*) – Vgas,es(A) -

Vsol,es(A)) + (Vsol,vdw(A*A'*) + Vint,vdw(A*-A'*) – Vsol,vdw (A)) + 

(6)

In addition, Miranda, et al. introduced a new parameter, D, to obtain a better  parameter.64 D is taken as the difference between the polar and nonpolar contributions to the binding free energy of the ligand. D = (Vgas,es(A*A'*) + Vint,es(A*-A'*) + Vsol,es(A*A'*) – Vgas,es(A) - Vsol,es (A)) (Vsol,vdw(A*A'*) + Vint,vdw(A*-A'*) – Vsol,vdw (A))

(7)

and hence  is calculated as: =fD+g

(8)



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where, the values, f = -0.95 and g = -2.06 (kcal/mol), were fitted from a training set of 24 protein ligand complexes.64 As pointed out earlier, LIE-D considers that the protein is rigid. However, in the present study, A42, is a very flexible molecule. To accommodate some of this flexibility, we reverse the roles of ligand and protein in equations (6) and (7). The new sets of terms could be found in equation (9), henceforth is called LIE-DR: GbindingLIE-DR ≈ (Vbond,es(A*A'*) + Vint,es(A*-A'*) + Vsol,es(A*A'*) – Vbond,es(A) - Vsol,es(A)) + (Vsol,vdw(PP*A'*) + Vint,vdw(A*-A'*) – Vsol,vdw (A)) + 

(9)

The average of GbindingLIE-R and GbindingLIE-DR is calculated, and designated as LIE-Ave. We note that the intraligand van der Waals terms are not included in equation (6). For a system as large as A dimer, these terms may have a significant impact on the final free energy. LIE-Ave partially corrects this deficiency in the intraligand van der Waals terms (Vgas,vdW(A)), and as well as solvation energy of A' (the protein). Results for the LIE-D, LIE-DR, and LIE-Ave procedures are listed in Tables 1 and S1. Error estimates for the energy terms in equation (2), presented in Tables 1, and S1, were calculated through the block-averaging procedure of Hess.65 The terms for monomeric A correspond to the ensemble of the most prominent equilibrated structure (shown in Figure 1a). Previously, Ggas(A) was reported to be 4376 kJ/mol.66 More specifically, the electrostatic and van der Waals components were Vgas,es(A) = 5239 kJ/mol and Vgas,vdW(A) = -863 kJ/mol, respectively. 66 MD simulation with an Amber Force Field:

To test the effect of the chosen force field on the stability of the retrieved structures, the most stable structure was chosen to carry out a benchmark with the AMBER99SB-ILDN force field.67 The Amber99sb-ILDN force field has yielded improved agreement with experimental NMR and circular dichroism data compared to other force fields.33,34 As a result, the most stable


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structure, i.e. cluster A-A'-d-1 (see below), was subjected to a 700 ns MD simulation under similar conditions to the previous MD simulations, but with the AMBER99SB-ILDN force field.

Results: Seven poses from the docking process were chosen as starting structures for MD. If at the end of the simulation, an interesting event is occurring, a major cluster in that time frame is chosen for another long-time MD simulation. a) A- A'-a and A-A'-b A-A'-a and A-A'-b trajectories belong to two successive MD simulations, with the latter starting from the last most prominent cluster of the former. The starting structure, named as A-A'-a, was subjected to a 744 ns MD simulation. The RMSD calculation and cluster analysis (Figure 2) suggested a locally equilibrated system. The most populated structure, cluster a-1, along with the second most populated structure, cluster a-2, are shown in Figure 2. The most prominent ensemble, i.e. cluster a-1, existed through 248-497 ns with population of only 13%. An intermolecular antiparallel -sheet was created between the residues Gln15Ala21 and Gly29’-Met35’, which is an R-SHR’ interaction, i.e. between the R region of one A and the SHR (second hydrophobic region) of the other A’. Additionally, there were small intramolecular -sheets at Ala2-Arg5 with Lys16-Phe19, and Leu34’-Val36’ with Val39’-Ile41’. Cluster a-2, with a population of 9% existed through the simulation time frame of 643-744 ns. Cluster a-2 possesses very similar structure to cluster a-1, with only one more residue pair added to the intermolecular -sheet. In the last 50 ns of simulation time (Figure 2 and S1), cluster a-14 appeared that had a distinctly different structure compared to the previous two ensembles. This structure contained an additional intermolecular -sheet at Val36-Val40 and His14’-Val18’, i.e. an SHR-R’ interaction. Since the population size was too small (less than 2%) to investigate its stability through energy



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analysis, another 1 s simulation was carried out to see if an A-A' dimer with two intermolecular R-SHR interactions would persist for a longer time.

Figure 2. A-A'-a: cluster analysis for second 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

The result for further simulation, designated as A-A'-b, can be found in Figure 3. The overall backbone structure observed in cluster a-14, was retained through the entire 1 s simulation time. Two flat regions in the RMSD curve (in Figure S2) suggest two distinct time intervals, each locally equilibrated. This is in sync with cluster analysis, where cluster b-1 existed through 714 ns-1.1 s and right after, it was replaced by cluster b-2, which remained the major structure to the end of 1.5 s simulation time. The population of these two most populated ensembles was 22% and 18%, respectively. Cluster b-1, contained two intermolecular antiparallel -sheets; one -sheet between residues His14-Ala21 and Gly29’-Val36’, and another between Ile32-Gly37 and Gln15’-Phe20’. Intramolecular -sheets were observed within Gln15’-Phe20’ and Ser26’-Ile32’ and also between Asp1-Arg5 and Lys16-Phe20. In cluster b-2, these R-SHR’ and SHR-R’ interactions were also present, along with the mentioned intramolecular -sheets. Additionally, Phe4’-His6’ made an antiparallel -sheet with Gly33-Met35 (a SHR-NTer’ interaction).


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Figure 3. A-A'-b: cluster analysis for 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

b) A-A'-c The MD simulation of the A-A'-c starting structure produced two main ensembles with very similar conformations, cluster c-1 and c-2 (Figure 4). Cluster c-2, with the population of 15%, was present between 205 to 464 ns. No significant intermolecular -sheet appeared apart from a minor -sheet between residues Asp7-Ser8 and Leu17’-Val18’. The rest of the -sheet interactions are all intramolecular: mainly R-CTer on one strand and small C-terminal and Nterminal -sheets on the other (see Figure 4). Cluster c-1, with a population of 25%, appears at 699 ns and stays as the major ensemble for the rest of simulation. The intermolecular -sheets are within Phe4-His6 and Phe19’-Ala21’ and also within His14-Gln15 and Val36’-Gly37’. In Figure 4, the most significant -sheet belongs to the strand in blue color, where the C-terminal is in contact with R region. This is very similar to the structure seen for A monomer (Figure 1-a).



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Figure 4. A-A'-c: cluster analysis for 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

c) A-A'-d The results of a fourth simulation are provided in Figure 5 and S4. The RMSD curve (Figure S4) suggests that the system reached local equilibrium at 400 ns. The cluster analysis revealed very long -sheet structures for this specific simulation. Figure 5 illustrates the major cluster, existing between 688-904 ns. The ensemble d-1 contains a significant intermolecular -sheet interaction between His14-Asp23 and Leu17’-Ser26’ (R-R’). Additionally, there are -sheets between Ile31-Leu34 and Tyr10’-His14’ (SHR-NTer’), and also Glu11-Phe19 and Met35’-Ala42’ (R-CTer’).


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Figure 5. A-A'-d: cluster analysis for 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

d) A-A'-e and A-A'-f Another distinct starting structure was initially simulated for 1 s but yielded no structures with any intermolecular interactions of long duration. However, the 10th cluster appeared to have specific interactions between Asp1-His6 and Asp23’-Gly29’ and also between Ser8-Val12 and Gly38’-Val42’. Consequently, this cluster was chosen for a further 1 s MD simulation and served as the starting point for A-A'-e.

The final simulation results for A-A'-e are

summarized in Figure 6. The RMSD curve fluctuated abruptly until 130 ns (Figure S5). As a result, the first 130 ns part of simulation was not included in the cluster analysis. The fluctuations in RMSDs become smaller when the first cluster (e-1) appears. All three major clusters contain antiparallel R-R’ -sheet interaction between Leu17-Glu22 and Lys16’-Ala21’. All have intramolecular -sheet interactions between Leu17-Glu22 and Asn27-Ile32 and Met35-Val40 (R-SHR-CTer).



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Figure 6. A-A'-e: cluster analysis for 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

First major cluster in the trajectory is cluster e-2, at time interval of 276-483 ns, with a population of only 9%. The features of this ensemble are a small intermolecular -sheet between Glu3-His6 and Lys28’-Ile31’ (NTer-SHR’), along with intramolecular -sheets through Ile31’- Val40’. Later, at 667-812 ns, cluster e-1 appeared. As can be seen in Figure 6, the Cterminal region of the red colored A opened up to a random coil. Instead, an additional sheet between Gly9-Glu11 and Phe4-His6 (in blue) is created. The structural changes bring the two methionine residues in vicinity of each other with an average distance of 0.9 nm (see Figure 16 ACS Paragon Plus Environment

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6). Although this cluster has the highest population (16%), it is unstable and vanishes at 812 ns. Later, at 897 ns, cluster e-3 manifests slight changes and persists to the end of the 1 s simulation time. This conformation has a small -sheet at Gly9-Glu11 with Phe4-His6 on one strand (in blue color), and Phe4’-Arg5’ with Phe19’-Phe20’ at the other (in red color). The sidechains of Met35 and Met35’ remain in proximity of each other. The stability of this latest structure, cluster e-3 was investigated by performing another 1 s MD simulation, A-A'-f. The results are provided in Figure 7. The RMSD calculations (Figure S6), suggests that a local equilibrium is reached after 280 ns. However, in order to compare the structure of cluster e-3 with the structures in the initial stage of the second trajectory, the cluster analysis in f series included the first 280 ns, as well. Looking at structures in Figure 6 and Figure 7, it is evident that the core hydrophobic -sheet is maintained through all the clusters in the course of 2 s simulation time. In the first 200 ns, cluster f-2 with a population of 25%, exists. The corresponding conformation is slightly different than that of cluster e-3. The red colored A has lost its N-terminal’ -sheet with R’ but instead the N-terminal of the other A strand makes -sheet with Gly38-Val39. Next, at time interval of 337 to 674 ns, ensemble f-1, with the highest population of 37% appears. The structure is very similar to the previous cluster with a minor loss of -sheet content in the N-terminal of the A in blue color. In the last 100 ns time of trajectory, cluster f-6 evolves that is very similar to the ensembles observed in the previous 1 s trajectory, i.e. the e series. Cluster f-6, contains an intermolecular -sheet between Phe4-Arg5 and Leu34’-Met35’. Additionally, Glu3’-Arg5’ is in -sheet with Phe19’-Ala21’.



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Figure 7. A-A'-f: cluster analysis for 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

c) A-A'-g For the starting structure of A-A’-g, the N-terminal of one A and the C-terminal of the other were in close proximity of each other.


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Figure 8. A-A'-g: cluster analysis for second 1 s simulation. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.

The RMSD curve in Figure S7 reveals an equilibration after 400 ns. Consequently, the cluster analysis contains only the last 600 ns simulation time (Figure 8). The two most populated ensembles have R-R’ -sheet interaction through residues His13-Val18 and Gln15’-Phe20’, and also SHR-SHR' interaction through Gly33-Gly37 and Ile31’-Met35’. In addition, similar intramolecular -sheet interactions are observed between Ala21-Lys28 and Ile31-Gly37. The only distinguishable difference between these clusters is the existence of small -sheets at the terminal regions, including a -sheet between Ser26-Lys28 to Phe4’-His6’. Energy analysis and Discussion: The simulations discussed above yielded 15 separate structures for the A42 dimer. Only four series of trajectories, namely the A-A’–d, -g, -e, and -f families, maintained an R-R’ -sheet interaction. Here, in an attempt to determine the relative stabilities of the selected clusters, the



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energy profiles for the association reaction by “LIE-Ave” for all 15 A-A’ complexes are calculated and provided in Table 1, and in more detail in Table S2. The five structures with the highest stability, from three trajectories, in order of decreasing stability, are d-1, b-2, b-1, g-1 and g-2, ignoring the large error estimates. The stability of the structures will be discussed in detail in the following. Table 1 A-A’ dimer; the major clusters for each system is provided, along with their population size (pi) and

their energy profile in kJ/mol. The shaded rows represent the two successive simulations. The clusters are listed by the hierarchy of their appearance in the trajectory. Gint(A*Cluster Ggas(APi GLIE-D GLIE-DR GLIE-Ave Ggas(A*) Ggas(A’*) number A’) A’*) a-1 0.13 4721±7 4522±16 -970±26 8273±23 -72±42 -63±44 -68±61 a-2 0.09 4775±58 4793±22 -1256±66 8312±64 -96±49 -87±57 -92±75 b-1 0.22 4628±5 4924±45 -1429±16 8123±34 -138±56 -90±42 -114±70 b-2 0.18 4847±7 4897±6 -1182±8 8562±9 -133±41 -105±41 -119±58 c-2 0.15 4553±9 4783±11 -939±17 8398±16 -68±42 -46±42 -57±59 c-1 0.25 4494±12 4886±16 -1112±16 8267±18 -109±42 -77±41 -93±59 d-1 0.04 4976±29 5017±55 -1252±77 8741±70 -170±52 -151±51 -160±72 e-2 0.16 4732±10 4755±5 -1010±7 8477±9 -97±41 -76±41 -87±57 e-1 0.09 4890±11 4408±6 -1069±19 8229±16 -47±41 -92±41 -70±58 e-3 0.07 4908±13 4452±11 -1048±12 8312±15 -60±41 -101±41 -81±58 f-2 0.25 4942± 4391±3 -1072±9 8261±9 -63±41 -116±41 -90±58 f-1 0.37 4700± 4675±10 -1040±10 8355±11 -73±41 -104±42 -88±59 f-6 0.09 4624±18 4502±19 -976±20 8150±23 -57±42 -101±43 -79±60 g-1 0.15 4746±31 4751±9 -1040±11 8457±24 -99±42 -125±41 -112±59 g-2 0.10 4820±10 4710±18 -1160±52 8371±39 -101±49 -111±52 -106±71 Monomer

Vgas(A)

A

4376 ±11

According to the LIE-Ave method, the dominant cluster in A-A’-d series (Figure 5) is the most stable structure in Table 1 (GLIE-Ave = -160

±72

kJ/mol). The error estimate is large due to the

uncertainty in GROMOS96 53a5 force field gas phase energy (Ggas(A-A’) = 8741

±70

kJ/mol),

possibly due to averaging over the low population, 4%. The intramolecular energies for the individual monomers, Ggas(A*), and Ggas(A'*), are the highest among all the structures, which means the long -sheet has placed a high amount of strain in the backbone of the two A monomers (Ggas(A*) = 4976 free A Ggas(A) = 4376

±11

±29

kJ/mol, and Ggas(A'*)= 5017

±55

kJ/mol, compared to the

kJ/mol). However, this effect is compensated by relatively strong 20 ACS Paragon Plus Environment

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intermolecular interaction, Gint(AA’) = -1252

±77

kJ/mol, through extended intermolecular

antiparallel -sheet that involves R-R’, R-CTer’, and SHR-NTer’ intermolecular interactions. In addition, this structure displayed the highest non-polar interaction among all clusters (Gsol,vdW(A*) + Gsol,vdW(A’*) = -665 ±31 kJ/mol, see Table S2). The next most stable conformers are very similar to each other and arose from the A-A’-b series, b-2 and b-1 (Figure 3), GLIE-Ave = -119±58 kJ/mol and GLIE-Ave = -114±70 kJ/mol, respectively. Cluster b-2 contains two R-SHR’ -sheet interactions and, one SHR-NTer’ -sheet interaction, but not an R-R’ -sheet. The trajectory A-A’-b is a 1 s continuation from the 1 s A-A’-a trajectory (Figure 2). Thus, A-A’-b-2 emerged as the last structure among two 1 s successive trajectories. Focusing on A-A’-a and –b families, GLIE-D and, GLIE-DR manifested the same trend (see Table 1), i.e. as the simulations progressed, more stable structures evolved. In both, -a and –b families, the last appearing structures are in fact the most stable structures. The next most stable structures occurred in A-A’-g series (Figure 8), GLIE-Ave = -112 ±59 kJ/mol and -106 ±71 kJ/mol, for g-1 and g-2, respectively. As in the A-A’-d trajectory, in both g-1 and g-2 clusters, there is a significant R-R’ antiparallel -sheet interaction, but unlike d-2, both are significantly more globular with an SHR-SHR’ -sheet, and an intramolecular antiparallel sheet, encompassing residues Gly25-Asn27 and Gly29-Val36 in one of the monomer units. The remaining trajectories yielded structures of lower energy. The two major A-A’-c structures (Figure 4) have very minor intermolecular -sheet interactions but contain two intramonomer R-CTer -sheet interactions. The last appearing cluster, cluster c-1, had the lower energy, as expected, GLIE-Ave = -93 ±59 kJ/mol. As with the -a and -b trajectories, trajectory -f (Figure 7) was a 1 s continuation of the 1 s -e trajectory (Figure 6). Similar conformations arose throughout both trajectories. Clusters e-2 and f-1 are the ensembles with the most favorable binding affinity in these series, GLIE-Ave = -87±57 kJ/mol and GLIE-Ave = -90±58 kJ/mol, respectively. Cluster e-2 has R-R’ and NTer-SHR’ interactions, and cluster f-1 is similarly structured but with extended NTer-SHR’ interaction. It is worth mentioning that two opposite methionine amino acids were detected to be in the vicinity 21 ACS Paragon Plus Environment


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of each other, in two A-A’-e and –f series. It was suggested earlier that two adjacent Met35 residues may facilitate the reduction of bound Cu2+ to Cu+, which may give rise to the copper neurotoxicity of A.4 In summary, the most stable structures are the ones with the highest number of intermolecular -sheet interactions, d-1 and b-2. In d-1 ensemble, R-R’ and R-CTer’ -sheets are present, and in b-2, two R-CTer’ -sheets exist. The next most prominent structures belong to –g series. These contain R-R’ parallel sheets, as well as SHR-SHR’ interactions. The fact that the structures which incorporate the C-terminal segment have a high stability is consistent with the experimental results that show the importance of the C-terminal, especially A25-35 region in aggregation.24,25,31,32,68 The importance of the SHR region is in line with the NMR analysis of Huang et al., on 150 kDa A42 oligomers.24 Huang et al. assigned anti-parallel -sheets within Ile32-Val40 and Met35-Gly37 between different monomers.24 Similar results were observed by Bertini, et al., for A40 oligomers.32 Our simulations in –g series yielded an antiparallel -sheet SHR-SHR’ interaction involving residues Gly29-Val36 and Gly33’-Met35’. Based on fluorescence studies, aggregation and disaggregation free energy of A40 dimers are reported to be -23.0

±2.9

and -18.2

±0.5

kJ/mol, respectively.69 However, due to the high error

estimates of S◦ = -80.8 ±83.5 kJ/mol, and H◦ = -43.8 ±24.5 kJ/mol), the results should not be used for the absolute energy prurposes.69 To the best of our knowledge, to this date, the absolute binding affinity of A dimers is still unknown, nonetheless our values could be used to compare the relative energies of the presented ensemble of structures. A criticism often leveled against the GROMOS96 53a5 force field is its bias toward generating structures with high -sheet content. However, since fibril structures, and higher oligomeric structures are observed to have high -sheet content, we regard this bias as a strength since it can be viewed as accelerating equilibration toward the experimentally observed structures. Accordingly, the elongated and extended oligomers have been reported to be a propelling factor in the aggregation into larger oligomers.70


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In the same vein, the secondary and tertiary structures of b-, d-, e-, f-, strikes a great resemblance to the building blocks of different sizes of aggregates compared by Nagel-Steger.30 For instance, the highly extended tertiary structure of d-1 is highly similar to the building block of a hexamer detected from ssNMR, which is an extended U-shaped fibril.71 Additionally, the Sshaped C-terminal segment structures of b-, f- and, e-series highly match the S-shaped fibril structure (PDB: 2MXV) and ssNMR disc-shaped penatmers.30 Barz et al. associate the high solvent accessibility in Met35-Ala42 region in A42 dimers to higher propensity of this region to further aggregation.70 Accordingly, by looking at the structures of a- to g- series (Figures 2 to 8), it is evident that this region is accessible to other A peptides. The close proximity of two Met35 in f- and e- series are also in correspondence with the popular copper cytotoxicity theory of A.4 Also, based on NMR studies of A dimers, the two Leu34 are observed to be in vicinity of each other.30 This feature is observed in g- series of our MD simulations (Figure S8). Additionally, it is suggested that no substantial change in -sheet content is observed from dimerization to the higher order of oligomers, which implies that -sheet formation precedes the oligomerization.30 Nonetheless, we should point out that the retrieved dimer structures are discordant to what was observed by Man et al., where they compared the structure of A42 dimer by four different force fields; OPLS-AA, CHARMM22*, AMBER99sb-ildn, and AMBERsb14.33 Although, the structures of the dimers in Man et al. study were very different among different force fields, but in all the intermolecular interactions were weak and transient. However, for the case of GROMOS 9653a5 force field, we observed very different outcome. In this respect, the secondary structures for each given cluster are represented in Table S2, to compare the reported structures to other studies. Apart from d-1 cluster which contains the highest -strand propensity (69.0%), the rest of structures contain -strand between 31.0% to 47.6%. Additionally, apart from d-1, the turn content is in a span of 21.4 to 42.9%, and the coil content is in a span of 14.3 to 31.0%. 23 ACS Paragon Plus Environment


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In a separate MD simulation with OPLS-AA force field, Barz and Urbanc reported the -strand, turn, and coil propensity of A42 to be 6.6%, 40.1%, and 48%, respectively.23 In a MD simulation study of A dimers with GROMOS 53a5 force field, the most stable structure, named as Xp gave the -strand, turn, and coil propensity of A42 to be 26.2%, 13.1%, and 38.1%, respectively.21 Man et al, have reported AmberffSB99-ildn to give a 24.3%, 41.1%, and 29.1% for the -strand, turn, and coil propensity, respectively.33 Although, Man et al, claimed that this force field regenerates secondary structure contents similar to circular dichroism (CD) spectra.33 But it should be pointed out that the CD data is acquired for only A40 dimers, and not A42 dimers, and the oligomeric structures of A40 and A42 are reported to have distinct structures.8 The corresponding CD data for A40 dimers displays 3% -helix content and 13% -strand content.72 Using SDS-PAGE and manipulating photo-induced cross-linking of unmodified proteins (PICUP) on A40 the -helix and -strand were reported to be 10% and 39%, respectively.18 One should also note that the experimental data from PICUP may not be a real representation of A dimer structures as the oligomers are chemically stabilized in PICUP. In conclusion, by comparing the secondary structure content from the current simulation with the mentioned computational and experimental studies of A40 dimers, higher -sheet content was observed in the presented structures of A42 dimers. Amber simulation of d-1: To test the dependence of the final structure on the Gromos96 53a5 force field, a 700 ns MD simulation on the d-1 structure was carried out with the AmberffSB99ildn force field, which is recognized to reproduce the experimental results. After fluctuations in structures due to introducing the AmberffSB99-ildn parameters, the system evolved into two major clusters; cluster1 (462–641 ns) and cluster2 (566- 700 ns). The cluster analysis and RMSD calculation is provided in Figure 9, and S9, respectively. The main intermolecular core interaction (R-R’) of d-1 structure prevailed through the whole trajectory, including cluster 1 and cluster2. In cluster 1, the intermolecular antiparallel -sheet interaction exists between Leu17-Glu22 and Val18’-Asp23’. In cluster 2, this interaction increased into one more amino acid, i.e. between Leu17-Asp23 and Leu17’-Asp23’. The long -sheet interaction is destabilized after introducing AmberffSB99-ildn parameters, but main interactions are retained. 24 ACS Paragon Plus Environment

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Furthermore, in cluster 1, the -sheet interaction between Glu3’-Asp7’ with Tyr10’-His14’ and also Tyr10’-His14’ with Ala30-Leu34 is observed. An antiparallel -sheet Arg5-Glu11 and Ile’Leu34’ is also present. In cluster 2, an antiparallel interaction between Asp1’-Asp7’,Tyr10’Gln15’, Ala30-Val36, and Gly38-Leu41 a appeared. A minor sheet between Glu11-Val12 and Leu34’-Met35’ is observed as well.

Figure 9. A-A’-Amber: cluster analysis for 700 ns simulation with AmberffSB99-ildn force field. The cyan and pink segments belong to the R region of the two monomers. The pink belongs to the A in red, and the cyan belongs to the other A in blue.



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In the end, one may argue that because of the high error estimates in the calculated energies, all the presented structures are not distinguishable in stability. We acknowledge the fact that A dimer structures are highly labile and dynamic and the presence of different structures with similar stability is also debated by other research groups.70,73,74 Conclusion: The study of MD simulations of A42 dimers illustrates the importance of the hydrophobic regions, i.e. CHC (R) and SHR in aggregation and stabilization of the dimer structure. The diversity of the dimer structures suggests several aggregation pathways. All the structures had one or more of R-SHR’, R-CTer’ or R-R’ interactions. In addition, other intermolecular or intramolecular interactions were established during the MD simulations. One of the most stable structures based on LIE-Ave is cluster b-2, which contains two R-SHR’ interactions and one SHR-NTer’ -sheet interaction. This structure has one of the highest numbers of intermolecular -sheets among the developed structures. Structure c-1 contains two R-CTer’ interactions. Additionally, the LIE-Ave method recognized the cluster g-1 as a stable structure. This structure contains R-R’ and SHR-SHR’ interactions. The most stable structure, d1, has extended R-R’ -sheet, with small SHR-N-Ter’, and R-CTer’ interactions. The fact that the C-terminal (including SHR) interaction is associated in all these high stable structures, is in accordance with the experimental reports24,68,25,31 and other computational studies21 that suggest C-Terminal interaction is an essential part of the aggregation process. This important characteristic is probably the reason for the higher neurotoxicity of A42 than its more abundant alloform, A40. Supporting Information Figures S1 – S7: RMSD curves for the A-A’-a - A-A’-g simulations. Table S1: The Secondary Structure assignment for each given cluster. The percentage of turn, coil, and -strand propensity in each cluster is reported.


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Table S2: The GROMOS force field interaction energies for A-A’ complexes. Acknowledgements The authors thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for financial support of this work, and Compute Canada for generous allocations of computer resources.

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