Energy gap of conformational transition related with temperature for the NACore of α-synuclein†

Pengxuan Xia ^a, Yuanming Cao ^a, Qingjie Zhao *^b and Huiyu Li *^a
aCollege of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, China
^bThe Research Center of Chiral Drugs, Shanghai Frontiers Science Center for TCM Chemical Biology, Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

First published on 13th August 2024

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Introduction

The aggregation of proteins and peptides into amyloid fibrils is associated with many degenerative diseases including Parkinson's disease (PD),^1,2 Alzheimer's disease (AD),^3,4 type II diabetes mellitus (T2D)^4,5 and Huntington's disease (HD).⁶ The presynaptic protein α-syn is a major component of Lewy bodies,⁷ which are neuron-related aggregates found in Parkinson's disease. α-syn has 140 amino acids and consists of three structural domains. One of them, the NACore structural domain (non-amyloid-β component), is located in the central structural domain of α-syn, between residues E61 and V95. The NACore is known to play an important role in both cytotoxicity and aggregation of α-syn.^8–11 This structural domain also includes highly hydrophobic motifs that are essential for α-syn aggregation. Many studies have shown that deletion or truncation of large segments in the NACore significantly reduces the degree of fibrosis and alters the cytotoxicity of α-syn.^10,12–14 Therefore, it can be said that the NACore segment plays a crucial role in α-syn aggregation.

Rodriguez JA, David S Eisenberg, et al.⁹ elucidated the atomic resolution structure of two short fragments of alpha-synuclein in the fibrillar state through microelectron diffraction. Using this technique, an 11-residue fragment (NACore) located in the core of the amyloid fibril of the full-length α-synuclein was structurally analyzed to a high resolution of 1.4 angstroms. Pallbo et al.¹⁵ used cryo-TEM, X-ray scattering and spectroscopy techniques to study the aggregation of NACore peptide fragments. The peptide forms long proto fibrillar aggregates consisting of β-sheets, and small globular aggregates were observed in the research.

Molecular dynamics (MD) simulations have been widely used for peptide aggregation,^16–26 and they provide detailed microscopic insights into the aggregation process and the conformation of early-forming oligomers at atomic resolution. Using molecular dynamics (MD) simulations to investigate the pathogenic mechanisms of the NACore segment of α-synuclein is a widely adopted approach in current research. Huang et al.²⁷ used coarse-grained molecular dynamics simulations to study the aggregation dynamics of the NACore peptide, the α-synaptic peptide associated with Parkinson's disease in the key region of nuclear proteins. Alıcı et al.²⁸ focused on the atomic structure of two small fibril fragments of α-syn, NACore (68–78) and SubNACore (69–77), which are essential for cytotoxicity and fibril formation in Parkinson's disease (PD). Pallbo²⁹ studied the interaction between aggregating peptides (NACore) and phospholipid membranes, focusing on the structure of the aggregates formed and their influence on the aggregation process. Sun et al.³⁰ found that fullerenol, a hydroxyl derivative of fullerene, has the potential to be a nanomedicine candidate for inhibiting amyloid aggregation. This research investigated the effect of the degree of hydroxylation on the inhibition of NACore aggregation.

However, there are only a few studies on the tendency of NACore fibrillation at different concentrations and temperatures. Exploring the conformational transition of NACore at different concentrations and temperatures could provide insight into the self-assembly mechanism of NACore.

Result and discussion

To investigate the self-aggregation kinetics of NACore (68–78) peptides, we explored three types of peptide systems, dimeric, tetrameric and octameric systems at 300 K and three different temperatures (300 K, 310 K, 320 K) for the octameric system, respectively, using all-atom molecular dynamics simulations. To eliminate the bias of the initial states, the last 200 ns of simulations are selected for analysis.

The convergence of the five systems was assessed by calculating the RMSD of each simulation as well as the R_g plots. In Fig. S1(b) and (c) (ESI†), all simulations converged in the last 200 ns, indicating reasonable convergence for the research systems.

Tetramers are more likely to form β-sheets than dimers and octamers at 300 K

To characterize the dynamics of NACore α-synuclein aggregation, we firstly analyzed the secondary structure properties in the different systems by analyzing the last 200 ns data for all MD runs at 300 K. In Fig. 1, we present the secondary structure data for different systems of NACore (68–78). To better illustrate the variability within the data, we have added error bars representing the standard deviations. These additions highlight the minor variations observed in the secondary structure content for different systems, providing a more comprehensive view of the data. As shown in Fig. 1(a), the average secondary structure properties of each peptide in the three systems were analyzed. For the dimer system, the content of random coil structure reaches ∼45% and the probability of a β-sheet structure is only ∼26%. When the number of peptides reaches four, the probability of random coil increases to ∼46% and the probability of β-sheet increases to ∼29%. For the eight peptides system, the probabilities of random coil and β-sheet are ∼47% and 28%, respectively. Interestingly, the probability of a helix structure is around 0.42%, 0.69%, and 0.04% in three systems, respectively. In Fig. 1(b)–(d), the average secondary structure properties of each residue in the three aggregated systems are analyzed. From Fig. 1(c), we can see that the two terminal residues have a tendency to coil, and the probability of β-sheet is highest in the tetramer system than other systems. In all three systems, the residues (68–78) of NACore predominantly form β-sheets (>20%), as shown in Fig. 1(b) and (c). And the residue T75 has the highest β-sheet probability of ∼50.8% in the tetramer system. Relative to alternative configurations, tetramers exhibit a heightened propensity for the generation of β-sheet segments within the length range of 5–9 residues (Fig. 1(d)). This observation underscores the inclination of tetramers towards the formation of more organized and structurally stable arrangements.^31,32 As seen in Fig. 1(e), the NACore dimer and octamer have RMSD values of 1.048 and 2.156, respectively. In contrast, the average RMSD value (0.923) of the NACore tetramer displays a sharp reduction. In Fig. 1(e), the average β-sheet probability of the NACore tetramer is also significantly larger than that of the dimer and octamer. The β-sheet probability reduction of the dimer and octamer is consistent with what is shown in Fig. 1(a). The most common conformations of NACore in the different studied systems can be clearly seen in Fig. 1(f) and the cluster probabilities are indicated. From the above analysis, we can see that tetramers are more likely to form an ordered β-sheet conformation at 300 K, whereas octamers may not form an ordered structure due to the lower reaction temperature. At lower temperatures, NACore tetramers exhibit relatively stable structures and are more prone to forming fibrillar structures, which is consistent with the findings of Xu et al.^8,31,32 However, according to previous studies, high concentrations of NACore will be more likely to fibrillate and form ordered and toxic structures.^15,27,29,33

The reason for this phenomenon may be due to the lower temperature, which prevents the high concentration of NACore from sufficiently undergoing aggregation in an octamer system. Therefore, we tried to explore the effect of temperatures on the aggregation of NACore in a high concentration environment (octamer). So, we analyzed the conformational character of NACore oligomers at different temperatures, as outlined in the following.

All-atom MD simulations show that the NACore oligomer has the highest β-sheet content at 310 K

To investigate the effect of temperature on the aggregations of NACore oligomer, we studied the NACore (68–78) octamer at 300 K, 310 K, and 320 K. We firstly analyzed the secondary structure of NACore octamers at different temperatures. As shown in Fig. 2(a), there was an increase in the average β-sheet probability (28% at 300 K, 34% at 310 K, and 31% at 320 K), accompanied by a reduction in the probability of coils and bridges (47% and 7.6% at 300 K; 45% and 6.2% at 310 K; and 47% and 7% at 320 K, respectively). The β-sheet probabilities of each residue in Fig. 2(b) showed that most of the residues in the NACore (68–78) octamer had higher β-sheet probability at 310 K than that at 300 K and 320 K. Interestingly, the β-sheet probabilities of G73, V74, T75, and A76 are significantly higher (30.2%, 41.5%, 55.6%, 58.0%) at 310 K than those at 300 K (18.0%, 31.6%, 44.4%, 45.5%) and at 320 K (26.5%, 37.0%, 52.8%, 52.9%). In addition, from Fig. 2(c), it can be seen that the probability of β-strand lengths increases in the interval of lengths 4 to 7 in the 310 K system. Fig. 2(d) shows that the major β chain formed in NACore oligomers is the 8-residue β chain at 310 K, with a probability of 36.5%, which is much higher than the probability of NACore oligomers at 300 K and 310 K (20.2% and 20.7%, respectively). In addition, the curves of β-sheet number with time evolution are also shown in Fig. 2(f). It can be seen that the system at 310 K consistently contains a larger number of β-sheets than the other two systems. And a snapshot of each five-hundred-nanosecond oligomer is shown in Fig. S2 (ESI†), which further provides evidence for the formation of more β-sheet-enriched oligomers. Additionally, to further explore the effect of high temperature on the conformation of NACore oligomers, we added simulation at 340 K. Interestingly, the results show that the amount of β-sheets is much lower at 340 K, as shown in Fig. S3(e) and (f) (ESI†).^34,35

The data reveals that, at a temperature of 310 K (closely mirroring the normal physiological condition of the human body), the mean likelihood of β-sheet formation within octameric assemblies peaked at 34%. This is in contrast to the observed probabilities at temperatures of 300 K and 320 K, which were recorded at 28% and 31%, respectively. Notably, certain residues, namely G73, V74, T75, and A76, demonstrated a marked elevation in β-sheet formation probability at 310 K, as described in Fig. 2(b). Furthermore, an enhanced probability for the formation of β-strands comprising 4 to 7 residues was identified at 310 K in Fig. 2(c), with the 8-residue β-chains exhibiting the most significant propensity for formation as evidenced in Fig. 2(c) and (d). These observations significantly contribute to our understanding of the temperature-mediated modulation of β-sheet formation in NACore octamers, offering pivotal insights into the molecular underpinnings of protein aggregation processes implicated in neurodegenerative pathologies.^{17,18,36–39}

In order to elucidate the aggregation kinetics of NACore (68–78) oligomers under varied thermal conditions, we plotted the probability density functions (PDFs) of intermolecular hydrogen bonding, radius of gyration (R_g), and solvent-accessible surface area (SASA) at 300 K, 310 K, and 320 K, as shown in Fig. S4 (ESI†). Notably, the analysis reveals that the mean counts of interchain hydrogen bonds exhibit a distinct elevation at 310 K (44 bonds), relative to 300 K (41 bonds) and 320 K (42 bonds), suggesting an augmented stabilization of β-sheet structures at a temperature approximating physiological conditions.³⁵ This is further observed in Fig. S4(b) (ESI†), where the R_g demonstrates a nuanced reduction in the 320 K system (peak ∼1.04 nm) compared to the slightly expanded configurations at 300 K and 310 K (peaks ∼1.05 nm and ∼1.07 nm, respectively), indicating a more compact aggregation state at the elevated temperature. A comprehensive assessment of the conformational character of NACore octamers, through metrics including R_g, contact number, and hydrogen bonding, elucidated in Fig. 2(f)–(h), delineates a non-monotonic dependency on temperature, with a pronounced increase in structural compactness and hydrogen bonding at 310 K, followed by the diminution as the temperature increases.

This intricate portrayal underscores a non-linear interdependence between temperature and the aggregation propensities of NACore octamers, accentuating the pivotal role of thermal conditions in modulating the aggregation conformational landscape of NACore octamers.

The highest β-sheet content observed in NACore octamers at 310 K compared to 300 K, 320 K, and 340 K can be attributed to several factors. Firstly, at 310 K, there is a significant increase in the number of interchain hydrogen bonds, which enhances the stabilization of β-sheet structures (Fig. 1(f)). Specifically, the average count of interchain hydrogen bonds at 310 K is higher than at 300 K and 320 K, suggesting that this temperature provides optimal conditions for β-sheet stability. Additionally, residue–residue interactions are notably stronger at 310 K. For instance, residue pairs such as V70–V77 and V77–V77 exhibit minimized average distances at this temperature (Fig. 3(d)), facilitating the formation of compact β-sheets. These tighter interactions between specific residues contribute significantly to the observed increase in β-sheet content. Moreover, the probability of forming longer β-strands, particularly those ranging from 4 to 7 residues, is higher at 310 K. This increase in β-strand length further promotes the stability and prevalence of β-sheet structures. At 310 K, the likelihood of forming 8-residue β-chains is also significantly higher compared to other temperatures, indicating a propensity for forming more extended and stable β-sheet regions (Fig. 2(c) and (d)). The energy gap between intermediate β-barrel states and stable β-sheet states is also lower at 310 K (Fig. 5). This reduced energy gap implies that NACore octamers can more readily transition from intermediate states to stable β-sheet structures at this temperature, thereby enhancing β-sheet formation. Lastly, the overall structural compactness of NACore octamers is greater at 310 K, as evidenced by the radius of gyration (R_g) and the probability density functions (PDFs) of intermolecular contacts (Fig. 2(f)–(h)). This compactness supports the formation of organized and stable β-sheet structures, contributing to the higher β-sheet content observed at this temperature. In summary, the highest β-sheet content at 310 K is due to enhanced interchain hydrogen bonding, stronger residue–residue interactions, increased β-strand lengths, reduced energy gaps for conformational transitions, and overall structural compactness. These factors collectively stabilize the β-sheet structures at 310 K, providing a comprehensive understanding of the temperature-dependent aggregation behavior of NACore octamers.

Analysis of residue–residue interactions and assembly pattern of β-sheet formation at different temperatures for NACore octamers

In order to identify the specific residue pairs that contribute the intermolecular interactions in NACore octamers, we calculated the number of molecule–residue contacts, hydrogen bonds (HBs), and interchain contacts in the three systems. In our simulations, the main chain–main chain (MC–MC) and side chain–side chain (SC–SC) contact numbers between residue pairs in the 310 K system are larger than those in the other two systems, especially A76–A76/V77/A78, V77–V77 and V78–V78 (Fig. 3(a) and (b)). In the 310 K system, the hydrogen bonding interactions of V74 and V77, A75 and A76 were significantly disrupted, while hydrogen bonding interactions of A76 and A78, and V77 and V77 were increased (Fig. 3(c)).⁴⁰ As shown through the representative snapshots between residues V70 and V77 (Fig. 3(d)), it can be clearly seen that the distance of V70–V77 at 310 K (1.126 nm) is smaller than that of the two systems at 300 K (1.27 nm) and 320 K (1.24 nm). At 310 K, the PDF of the V77–V77 pair distance has a peak centered at ∼0.94 nm, which is smaller than that at 300 K (1.25 nm) and at 320 K (1.35 nm), as shown in Fig. S5(a) (ESI†). The same occurs for the V70–V77 base pair (0.94 nm at 310 K, 1.24 nm at 300 K, and 1.35 nm at 320 K) (Fig. S5(c), ESI†). It is shown that both V70–V77 and V77–V77 are more compact at 310 K than at 300 K and 320 K. The details are shown in the representative snapshots (Fig. S5(b) and (d), ESI†). In summary, these results indicate that changes in temperature lead to enhanced interactions of various residue pairs. The interactions between the two base pairs, V77–V77 and V70–V77, are enhanced at 310 K, leading to the formation of β-sheets.

Previous studies have found that antiparallel β-sheets and parallel β-sheets form in early oligomers, and that the number of parallel β-strand arrangements increases with increasing β-sheet size.^41–45 Therefore, we analyzed the β-sheet layer patterns in NACore octamers at different temperatures. As shown in Fig. 3(a), the number of MC–MC contacts in the contact diagram indicates that arrangements of NACore octamers are present. However, it is noteworthy that the MC–MC contact probability of NACore octamers at 310 K (Fig. 3(a)) shows the antiparallel β-sheet patterns (Fig. 3(f)). In order to compare the differences of β-sheet patterns at different temperatures, we analyzed the angle between two β-strands that form β-sheets. As shown in Fig. 3(f), the peaks (120–160°) in the 310 K system are significantly larger, sharper and more pronounced than those in the other two systems, indicating that the probability of antiparallel β-sheet layers increases at 310 K. In addition, we also counted the probability of parallelism and anti-parallelism at different temperatures. The probability of a parallel/antiparallel structure is 71.56%/28.44% at 300 K, 63.48%/36.52% at 310 K, and 63.92%/36.08% at 320 K, respectively. These values indicated that the octamer was more likely to form parallel structures and the parallel β-sheet organization is an intrinsic property of NACore oligomers. At 310 K, the increase in the number of hydrogen bonds and the change in R_g indicate that the octamer structure becomes more compact and ordered, which may promote the formation of antiparallel β-folds. β-fold formation usually depends on the specific amino acid sequence and the surrounding environment.^45–47 We comprehensively analyzed the β-sheet patterns of NACore octamers at different temperatures, with particular attention given to the structural changes at 310 K. The results demonstrated that at 310 K, there was an increase in both main chain–main chain (MC–MC) and side chain–side chain (SC–SC) contact numbers between specific residue pairs, indicating more intimate intermolecular interactions. Concurrently, significant alterations in hydrogen bonding interactions among certain residues facilitated the formation of antiparallel β-sheet layers. Angle analysis revealed a marked increase in the probability of antiparallel β-sheet formation at 310 K, suggesting that temperature plays a pivotal role in determining β-sheet stability and assembly pattens. Additionally, a comparative assessment of the proportions of parallel and antiparallel β-sheets at different temperatures highlighted an increased propensity for antiparallel β-sheet formation at 310 K, indicative of enhanced structural compactness and order.

The NACore octamers transiently adopt ordered β-barrel conformations at 310 K

To explore the aggregation process of the NACore octamer, cluster analysis was conducted across three systems at temperatures of 300 K, 310 K, and 320 K. The four predominant conformations of NACore octamers at these temperatures are illustrated in Fig. S6 (ESI†). It was observed that the most prevalent conformational cluster across all studied systems was the layered β-sheet. Previous research has indicated that β-barrel formations exhibit considerable toxicity and represent an intermediate state.^47–49 By analyzing the clusters, it was determined that the probability of β-barrel formation at 310 K (8.99%) significantly exceeded that of the other systems (2.98% and 6.98%, respectively), and was less than the proportion of layered β-sheets within each system (Fig. 4(d)–(f)).

To comprehensively understand the impact of temperature on the conformational space of the NACore octamer, two-dimensional potential energy landscapes were constructed for each system. These landscapes were delineated using the number of inter-mainchain backbone (MC) hydrogen bonds (H-bonds) and the Cα-root mean squared deviation (Cα-RMSD) of the NACore as reaction coordinates (Fig. 4(b)–(d)). For calculating Cα-RMSD, β-barrel conformations of these systems were employed as reference structures. The analysis revealed two minimal energy clusters within each system (Fig. 4(b)–(d)). One is centered around the coordinates (2.13 nm, 40 H-bonds), we labeled A near this center, and it corresponds to the β-barrel conformation. The other, centered around (2.2 nm, 42 H-bonds), labeled as B, corresponds to non-β-barrel conformations comprising single-layered and bilayered β-sheets. These configurations include monolayer β-sheets, bilayer β-sheets, and disordered structures. The depth of minimal energy basin near A for these systems aligned with the trend in β-barrel probability (300 K < 320 K < 310 K). In Fig. 5(e)–(g), A and B depict representative ordered octamer conformations for the three systems, including β-barrel and monolayer β-sheet structures for the 300 K and 310 K systems, and a bilayer β-sheet structure for the 320 K system. The following figure (top view) is obtained by rotating the above figure (front view) by 90°. Similar to the β-barrel structure, the β-sheet conformation has also been reported to be associated with neurotoxicity.^50,51 These β-barrel oligomers have been postulated to form the assembly “amyloid pores” and to play an important role in the pathology of amyloid assembly.^47,49 These potentially toxic β-barrels with well-defined structures may serve as novel therapeutic targets against neurodegenerative diseases.

The conformational transition of NACore oligomers from intermediate states to stable structures is dependent on temperature

To illustrate the mechanism of the conformational transition of NACore octamers, we analyzed the energy gap of the conformational transition of NACore octamers as a function of temperature in Fig. 5. As illustrated in Fig. 5(a), temperature exerts a significant impact on the free potential energy of the typical conformations. Additionally, in Fig. S7 (ESI†), we present snapshots of the representative structures for the different systems. The typical conformational changes of the NACore octamer in the final state at different temperatures are visually depicted in Fig. 5(b). We further calculated the energy gap between the β-barrel and β-sheet layer, and our analysis indicates that the energy gap between the β-barrel and a β-sheet of the NACore octamer is lower at 310 K than at other temperatures (Fig. 5(c)). This observation suggests that the NACore octamer can transfer from an intermediate state to a stable state easily at 310 K. Through Fig. 5(e) and (f), we observe that the number of both hydrogen bond and hydrophobic interactions has maximum values at 310 K, indicating that the interaction between residues of NACore is stronger at 310 K than at other temperatures. The intermolecular distances between the β-strands and between the β-sheets are 8.32 Å and 4.91 Å, respectively (Fig. S9, ESI†). The bilayer conformation in our research agrees well with the fibrillar aggregates observed with the Cryo-TEM and X-ray method.^15,30,52,53

To explore the interactional mechanism of NACore octamers, based on the above analysis, we calculated the distance between critical residues, hydrogen bonds and hydrophobic interactions of the NACore octamers. Additionally, our analysis of the distance between residues V70 and V77 revealed that their average distance is also minimized at 310 K, as shown in Fig. 5(d). These residues (V70, V77) play a crucial role in fostering the formation of β-sheets in proteins.⁵⁴ Our investigation revealed a significant decrease in the distances between specific amino acids, indicative of temperature's pivotal role in shaping the stability of β-sheet layers and assembly patterns. Notably, at 310 K, alterations in hydrogen bonding and hydrophobic interactions suggest a more compact and organized octamer structure, potentially fostering the formation of β-folded layers, as shown in Fig. 5(e) and (f). These findings underscore the critical influence of temperature on the energy gap of configurational transition between the intermediate state (β-barrel) and the final state (β-sheet layers) in NACore octamers, offering valuable insights into the contribution of β-sheet formation to the pathogenesis of neurodegenerative diseases. Moreover, they highlight the regulatory role of temperature in modulating protein structural stability and aggregation kinetics.

Conclusion

In this study, we explored the effects of concentration as well as temperature on the assembly kinetics and conformational combinations of NACore oligomers by performing extensive all-atom explicit solvent MD simulations. Our molecular dynamics simulations with long timescales (total 47.7 μs) can be concluded as follows.

Firstly, tetramers are more prone to form β-sheets at 300 K than dimers and octamers (Fig. 1). We suppose that the lower temperature can prevent NACore oligomers polymerizing at high concentrations. Secondly, the MD simulation results showed that the NACore octamer at 310 K has a higher β-sheet content and larger β-sheet size (Fig. 2). The inter-chain hydrophobic interactions, the backbone hydrogen bonding, and the residue–residue interactions between V70–V77 as well as V77–V77, play important roles in the aggregation tendency of NACore octamers at 310 K (Fig. 2 and 3). Thirdly, the conformational transition of NACore oligomers from intermediate states (β-barrel conformation) to stable structures (β-sheet layers) is dependent on the temperatures (Fig. 4 and 5). The energy gap from intermediate states to the stable states is lower at 310 K. Our previous study also reported that β-barrel conformations are the intermediates of these β-rich oligomers.⁴⁹ The observation of β-barrel oligomers was consistent with previous research.⁵⁵

In conclusion, our findings illuminate the impact of concentration and temperature on the β-sheet propensity and ordered conformation of NACore oligomers at the atomic level, offering insights into neurodegenerative disease mechanisms, and underscoring temperature's regulatory role in protein structural dynamics.

Materials and methods

Molecular systems used in the simulations

The peptide used in our simulation was taken from the Protein Data Bank (PDB) (code: 6PES).⁵⁶ Because the fibril was identified at a high resolution of 2.3 Å, we chose this type of peptide and captured the ⁶⁸GAVVTGVTAVA⁷⁸ fragment from this structure for testing. To obtain the initial structure of the NACore (68–78) peptide for the simulation, we first built a multimer system using Materials Studio and performed MD simulations at 300 K for 1000 ns. Then we chose the conformation at 27.3 ns as the initial structure, as shown in Fig. S1(a) (ESI†). In order to understand the contribution of different concentrations and temperatures to the aggregation of NACore (68–78), we have systematically performed simulations of the dimer, tetramer and octamer at 300 K, and the octamer at 300 K, 310 K, 320 K and 340 K using all-atom MD simulations. A peptide contains 81 atoms and the total number of atoms is 10488, 17544, and 20382, for the two, four, and eight peptide systems, respectively. For each research system, seven independent trajectories are obtained starting with different velocities. The simulation time for each MD trajectory was 1.5 μs in all simulation systems except for the dimer system. The accumulated simulation time is 47.7 μs. The details of all the simulations are summarized in Table 1.

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MD simulations

MD simulations were performed using Gromacs 2022.3 on our high-performance GPU cluster.^57,58 For NACore aggregation studies, GROMOS is the most convenient force field.^59–61 Therefore, our study is based on the GROMOS96 43a1 force field.²⁸ The GROMOS96 43a1 force field was used for describing the process of protein aggregation.⁶² Each protein system was placed in the center of the box filled with SPCE water,⁶³ with a minimum distance of 1.0 nm between protein atoms and the box edges. In the molecular dynamics simulations of each system, the total charge is maintained at neutrality without the necessity of introducing additional sodium (Na⁺) or chloride (Cl⁻) ions. Constraints were applied to all bond lengths using the LINCS method⁶⁴ for proteins, allowing an integration time step of 2 fs. The pressure was kept at 1 bar using the Parrinello Rahman method⁶⁵ with a coupling time constant of 2.0 ps. Electrostatic interactions were treated using the particle mesh Ewald (PME) method.⁶⁶ The simulations were performed in the isothermal–isobaric (NPT) ensemble using periodic boundary conditions.

In this study, the SPCE water model was employed for MD simulations due to its proven accuracy in reproducing the thermodynamic properties of liquid water and its extensive use in protein simulations. To ensure that our findings were not biased by the choice of water model, we also performed additional simulations using the CHARMM36m force field with the TIP3P water model. The comparative analysis between the SPCE and TIP3P models showed consistent results, confirming the reliability of our initial simulations (Fig. S8, ESI†).

Analysis methods

We calculated the secondary structure using the dictionary of secondary structure of protein (DSSP) program.⁶⁷ If the distance between the atoms (N and O) is within 3.5 Å and the angle (N–H·O) is ≥120°,⁶⁸ we consider that a hydrogen bond is formed. If the distance between the heavy atoms of two discontinuous residues is ≤5.4 Å, a pairwise residue is considered the contact status. If two or more coherent residues in each chain adopt the β-strand conformation, and at least two backbone hydrogen bonds are obtained among these residues,⁶⁹ we considered that two chains can form a β-sheet. The cluster conformations are employed using the GROMOS analysis method with a Cα root mean square deviation (Cα-RMSD) cutoff of 0.2 nm for all the residues. A pairwise residue forming a β-sheet is defined as a β-sheet contact.⁷⁰ A two-dimensional (2D) free energy landscape is constructed using −RTlnH(x,y), where H(x,y) is the probability of two selected reaction coordinates, which is the radius gyration (R_g) and β-sheet contact. The visual molecular dynamics (VMD) program⁷¹ was used for graphical structure analysis and trajectory visualization.

Data availability

Any additional information required to re-analyse the data reported in this paper is available upon request.

Conflicts of interest

The authors declare no competing interests.

References

1. M. H. Polymeropoulos, C. Lavedan, E. Leroy, S. E. Ide, A. Dehejia, A. Dutra, B. Pike, H. Root, J. Rubenstein, R. Boyer, E. S. Stenroos, S. Chandrasekharappa, A. Athanassiadou, T. Papapetropoulos, W. G. Johnson, A. M. Lazzarini, R. C. Duvoisin, G. Di Iorio, L. I. Golbe and R. L. Nussbaum, Science, 1997, 276, 2045–2047 CrossRef CAS PubMed .
2. A. B. Singleton, M. Farrer, J. Johnson, A. Singleton, S. Hague, J. Kachergus, M. Hulihan, T. Peuralinna, A. Dutra, R. Nussbaum, S. Lincoln, A. Crawley, M. Hanson, D. Maraganore, C. Adler, M. R. Cookson, M. Muenter, M. Baptista, D. Miller, J. Blancato, J. Hardy and K. Gwinn-Hardy, Science, 2003, 302, 841 CrossRef CAS PubMed .
3. J. Nasica-Labouze, P. H. Nguyen, F. Sterpone, O. Berthoumieu, N.-V. Buchete, S. Coté, A. De Simone, A. J. Doig, P. Faller, A. Garcia, A. Laio, M. S. Li, S. Melchionna, N. Mousseau, Y. Mu, A. Paravastu, S. Pasquali, D. J. Rosenman, B. Strodel, B. Tarus, J. H. Viles, T. Zhang, C. Wang and P. Derreumaux, Chem. Rev., 2015, 115, 3518–3563 CrossRef CAS PubMed .
4. S. Bedrood, Y. Li, J. M. Isas, B. G. Hegde, U. Baxa, I. S. Haworth and R. Langen, J. Biol. Chem., 2012, 287, 5235–5241 CrossRef CAS PubMed .
5. M. Anguiano, R. J. Nowak and P. T. Lansbury, Biochemistry, 2002, 41, 11338–11343 CAS .
6. M. Nakano, K. Ebina and S. Tanaka, J. Mol. Model., 2013, 19, 1627–1639 CrossRef CAS PubMed .
7. M. G. Spillantini, M. L. Schmidt, V. M.-Y. Lee, J. Q. Trojanowski, R. Jakes and M. Goedert, Nature, 1997, 388, 839–840 CrossRef CAS PubMed .
8. L. Xu, R. Nussinov and B. Ma, Eur. J. Med. Chem., 2016, 121, 841–850 CrossRef CAS PubMed .
9. J. A. Rodriguez, M. I. Ivanova, M. R. Sawaya, D. Cascio, F. E. Reyes, D. Shi, S. Sangwan, E. L. Guenther, L. M. Johnson, M. Zhang, L. Jiang, M. A. Arbing, B. L. Nannenga, J. Hattne, J. Whitelegge, A. S. Brewster, M. Messerschmidt, S. Boutet, N. K. Sauter, T. Gonen and D. S. Eisenberg, Nature, 2015, 525, 486–490 CrossRef CAS PubMed .
10. A. M. Bodles, D. J. S. Guthrie, B. Greer and G. B. Irvine, J. Neurochem., 2001, 78, 384–395 CrossRef CAS PubMed .
11. M. Bisaglia, A. Trolio, M. Bellanda, E. Bergantino, L. Bubacco and S. Mammi, Protein Sci., 2006, 15, 1408–1416 CrossRef CAS PubMed .
12. M. Periquet, T. Fulga, L. Myllykangas, M. G. Schlossmacher and M. B. Feany, J. Neurosci., 2007, 27, 3338–3346 CrossRef CAS PubMed .
13. M. J. Volles and P. T. Lansbury, J. Mol. Biol., 2007, 366, 1510–1522 CrossRef CAS PubMed .
14. K. Vamvaca, M. J. Volles and P. T. Lansbury, J. Mol. Biol., 2009, 389, 413–424 CrossRef CAS PubMed .
15. J. Pallbo, E. Sparr and U. Olsson, Q. Rev. Biophys., 2019, 52, e4 CrossRef CAS .
16. H. Li, Y. Luo, P. Derreumaux and G. Wei, J. Phys. Chem. B, 2010, 114, 1004–1009 CrossRef CAS PubMed .
17. H. Li, Y. Luo, P. Derreumaux and G. Wei, Biophys. J., 2011, 101, 2267–2276 CrossRef CAS PubMed .
18. L. Xie, D. Lin, Y. Luo, H. Li, X. Yang and G. Wei, Biophys. J., 2014, 107, 1930–1938 CrossRef CAS PubMed .
19. P. Krupa, A. K. Sieradzan, M. A. Mozolewska, H. Li, A. Liwo and H. A. Scheraga, J. Chem. Theory Comput., 2017, 13, 5721–5730 CrossRef CAS PubMed .
20. M. Song, Y. Zhu, G. Wei and H. Li, Mol. Simul., 2017, 43, 1189–1195 CrossRef CAS .
21. M. Song, Y. Sun, Y. Luo, Y. Zhu, Y. Liu and H. Li, Int. J. Mol. Sci., 2018, 19, 1815 CrossRef PubMed .
22. X. Wang, M. Song, S. Zhao, H. Li, Q. Zhao and J. Shen, Mol. Simul., 2020, 46, 638–649 CrossRef CAS .
23. S. Zhao, Y.-Y. Zhu, X.-Y. Wang, Y.-S. Liu, Y.-X. Sun, Q.-J. Zhao and H.-Y. Li, Int. J. Mol. Sci., 2020, 21, 2601 CrossRef CAS PubMed .
24. L. Chen, S. Zhao, Y. Zhu, Y. Liu, H. Li and Q. Zhao, Interdiscip. Sci.: Comput. Life Sci., 2021, 13, 751–765 CrossRef CAS PubMed .
25. Y. Zhang, L. Chen, X. Wang, Y. Zhu, Y. Liu, H. Li and Q. Zhao, Processes, 2021, 9, 1749 CrossRef CAS .
26. Y. Zhang, Y. Zhu, H. Yue, Q. Zhao and H. Li, Front. Mol. Biosci., 2022, 9, 982276 CrossRef CAS PubMed .
27. R. Huang, R. Tang, X. Song, J. Wang, K. Chen and W. Tian, Proteins, 2023, 91, 16–21 CrossRef CAS PubMed .
28. H. Alıcı, J. Mol. Model., 2020, 26, 132 CrossRef PubMed .
29. J. Pallbo, M. Imai, L. Gentile, S. Takata, U. Olsson and E. Sparr, Front. Physiol., 2020, 11, 592117 CrossRef PubMed .
30. Y. Sun, A. Kakinen, C. Zhang, Y. Yang, A. Faridi, T. P. Davis, W. Cao, P. C. Ke and F. Ding, Nanoscale, 2019, 11, 11933–11945 RSC .
31. L. Xu, S. Bhattacharya and D. Thompson, Phys. Chem. Chem. Phys., 2018, 20, 4502–4512 RSC .
32. L. Xu, S. Bhattacharya and D. Thompson, Chem. Commun., 2018, 54, 8080–8083 RSC .
33. M. R. Brown, S. E. Radford and E. W. Hewitt, Front. Mol. Neurosci., 2020, 13, 609073 CrossRef CAS PubMed .
34. M. Guo, Y. Xu and M. Gruebele, Proc. Natl. Acad. Sci. U. S. A., 2012, 109, 17863–17867 CrossRef CAS PubMed .
35. G. Feller, Semin. Cell Dev. Biol., 2018, 84, 129–137 CrossRef CAS PubMed .
36. B. Ahmad, G. Muteeb, P. Alam, A. Varshney, N. Zaidi, M. Ishtikhar, G. Badr, M. H. Mahmoud and R. H. Khan, Int. J. Biol. Macromol., 2015, 75, 447–452 CrossRef CAS PubMed .
37. P. I. Semenyuk, Int. J. Mol. Sci., 2022, 23(19), 11545 CrossRef CAS PubMed .
38. Y. Tan, Y. Chen, X. Liu, Y. Tang, Z. Lao and G. Wei, Int. J. Biol. Macromol., 2023, 241, 124659 CrossRef CAS PubMed .
39. Y. Zou, Y. Sun, Y. Zhu, B. Ma, R. Nussinov and Q. Zhang, ACS Chem. Neurosci., 2016, 7, 286–296 CrossRef CAS PubMed .
40. S. Lan-Mark and Y. Miller, ACS Chem. Neurosci., 2022, 13, 370–378 CrossRef CAS PubMed .
41. X. Li, J. Lei, R. Qi, L. Xie and G. Wei, Phys. Chem. Chem. Phys., 2019, 21, 15686–15694 RSC .
42. Z. Zhang, H. Chen, H. Bai and L. Lai, Biophys. J., 2007, 93, 1484–1492 CrossRef CAS PubMed .
43. D.-W. Li, S. Mohanty, A. Irbäck and S. Huo, PLoS Comput. Biol., 2008, 4, e1000238 CrossRef PubMed .
44. J. Nasica-Labouze, M. Meli, P. Derreumaux, G. Colombo and N. Mousseau, PLoS Comput. Biol., 2011, 7, e1002051 CrossRef CAS PubMed .
45. Z. Yang, Y. Yao, Y. Zhou, X. Li, Y. Tang and G. Wei, Int. J. Biol. Macromol., 2023, 230, 123194 CrossRef CAS PubMed .
46. J. Zeng, Y. Tang, X. Dong, F. Li and G. Wei, Proteins, 2023, prot.26482 Search PubMed .
47. X. Liu, X. Li, Q. Qiao, F. Li and G. Wei, ACS Chem. Neurosci., 2023, 14, 1310–1320 CrossRef CAS PubMed .
48. L. Xie, Y. Luo and G. Wei, J. Phys. Chem. B, 2013, 117, 10149–10160 CrossRef CAS PubMed .
49. L. Liang, Y. Zhang, Y. Zhu, J. Bai, Y. Ni, J. Wan, H. Yue, Q. Zhao and H. Li, ACS Chem. Neurosci., 2024, 15, 1356–1365 CrossRef CAS PubMed .
50. E. L. Guenther, Q. Cao, H. Trinh, J. Lu, M. R. Sawaya, D. Cascio, D. R. Boyer, J. A. Rodriguez, M. P. Hughes and D. S. Eisenberg, Nat. Struct. Mol. Biol., 2018, 25, 463–471 CrossRef CAS PubMed .
51. W. Chen, Y. Zhou, G. Chen, Y. Wu, B. Tu, F. Liu, L. Huang, A. M. C. Ng, A. B. Djurišić and Z. He, Adv. Energy Mater., 2019, 9, 1803872 CrossRef .
52. X. Liang, N. Andrikopoulos, H. Tang, Y. Wang, F. Ding and P. C. Ke, Small, 2024, 20, 2308753 CrossRef CAS PubMed .
53. J. A. Rodriguez, M. I. Ivanova, M. R. Sawaya, D. Cascio, F. E. Reyes, D. Shi, S. Sangwan, E. L. Guenther, L. M. Johnson, M. Zhang, L. Jiang, M. A. Arbing, B. L. Nannenga, J. Hattne, J. Whitelegge, A. S. Brewster, M. Messerschmidt, S. Boutet, N. K. Sauter, T. Gonen and D. S. Eisenberg, Nature, 2015, 525, 486–490 CrossRef CAS PubMed .
54. H. J. Dyson, P. E. Wright and H. A. Scheraga, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 13057–13061 CrossRef CAS PubMed .
55. X. Ge, Y. Sun and F. Ding, Biochim. Biophys. Acta, –Biomembr., 2018, 1860, 1687–1697 CrossRef CAS PubMed .
56. D. R. Boyer, B. Li, C. Sun, W. Fan, M. R. Sawaya, L. Jiang and D. S. Eisenberg, Nat. Struct. Mol. Biol., 2019, 26, 1044–1052 CrossRef CAS PubMed .
57. D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof, A. E. Mark and H. J. C. Berendsen, J. Comput. Chem., 2005, 26, 1701–1718 CrossRef CAS PubMed .
58. M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess and E. Lindahl, SoftwareX, 2015, 1–2, 19–25 CrossRef .
59. A. S. Kamenik, P. H. Handle, F. Hofer, U. Kahler, J. Kraml and K. R. Liedl, J. Chem. Phys., 2020, 153, 185102 CrossRef CAS PubMed .
60. D. Petrov, C. Margreitter, M. Grandits, C. Oostenbrink and B. Zagrovic, PLoS Comput. Biol., 2013, 9, e1003154 CrossRef CAS PubMed .
61. H. Mani, C.-C. Chang, H.-J. Hsu, C.-H. Yang, J.-H. Yen and J.-W. Liou, Bioengineering, 2023, 10, 1004 CrossRef CAS PubMed .
62. C. L. Fernandes, L. G. Sachett, L. Pol-Fachin and H. Verli, Carbohydr. Res., 2010, 345, 663–671 CrossRef CAS PubMed .
63. H. J. C. Berendsen, J. R. Grigera and T. P. Straatsma, J. Phys. Chem., 1987, 91, 6269–6271 CrossRef CAS .
64. B. Hess, H. Bekker, H. J. C. Berendsen and J. G. E. M. Fraaije, J. Comput. Chem., 1997, 18, 1463–1472 CrossRef CAS .
65. M. Parrinello and A. Rahman, J. Appl. Phys., 1981, 52, 7182–7190 CrossRef CAS .
66. T. Darden, D. York and L. Pedersen, J. Chem. Phys., 1993, 98, 10089–10092 CrossRef CAS .
67. W. Kabsch and C. Sander, Biopolymers, 1983, 22, 2577–2637 CrossRef CAS PubMed .
68. D. Van Der Spoel, P. J. Van Maaren, P. Larsson and N. Tîmneanu, J. Phys. Chem. B, 2006, 110, 4393–4398 CrossRef CAS PubMed .
69. H. He, Y. Liu, Y. Sun and F. Ding, J. Chem. Inf. Model., 2021, 61, 2916–2925 CrossRef CAS PubMed .
70. N. S. Burkoff, C. Várnai and D. L. Wild, Bioinformatics, 2013, 29, 580–587 CrossRef CAS PubMed .
71. W. Humphrey, A. Dalke and K. Schulten, J. Mol. Graphics, 1996, 14, 33–38 CrossRef CAS PubMed .

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4cp02131b

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