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. 2019 Jun 18;17:87–100. doi: 10.1016/j.isci.2019.06.022

α-Helical Motif as Inhibitors of Toxic Amyloid-β Oligomer Generation via Highly Specific Recognition of Amyloid Surface

Yixiang Jiang 1,2,3, Xuehan Jiang 1,3, Xiaodong Shi 1, Fadeng Yang 1, Yang Cao 1, Xuan Qin 1, Zhanfeng Hou 1, Mingsheng Xie 1, Na Liu 1, Qi Fang 1, Feng Yin 1,2,∗, Wei Han 1,∗∗, Zigang Li 1,2,4,∗∗∗
PMCID: PMC6606958  PMID: 31255986

Summary

Amyloid fibril surfaces can convert soluble proteins into toxic oligomers and are attractive targets for intervention of protein aggregation diseases. Thus far, molecules identified with inhibitory activity are either large proteins or flat cyclic compounds lacking in specificity. The main design difficulty is flatness of amyloid surfaces and the lack of knowledge on binding interfaces. Here, we demonstrate, for the first time, a rational design of alpha-helical peptide inhibitors targeting the amyloid-beta 40 (Aβ40) fibril surfaces, based on our in silico finding that a helical fragment of Aβ40 interacts in a unique way with side-chain arrays on the fibril surface. We strengthen the fragment's binding capability through mutations and helicity enhancement with our Terminal Aspartic acid strategy. The resulting inhibitor shows micromolar affinity for the fibril surface, effectively impedes the surface-mediated oligomerization of Aβ40, and mitigates its cytotoxicity. This work opens up an avenue to designing aggregation modulators for amyloid diseases.

Subject Areas: Bioorganic Chemistry, Computational Chemistry, Biochemistry

Graphical Abstract

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Highlights

  • •

    Demonstrating that alpha-helical motif specifically recognizes Aβ amyloid surface

  • •

    In silico structure-based design of peptides as PPI blockers

  • •

    Disrupting Aβ-fibril interaction to prevent toxic oligomer formation

Bioorganic Chemistry; Computational Chemistry; Biochemistry

Introduction

Soluble peptides or proteins can misfold and self-assemble into highly ordered aggregates, which have garnered tremendous interest, as they are implicated in protein conformational diseases that range from neurodegenerative disorders to systemic amyloidosis (Chiti and Dobson, 2006). A key hallmark of such diseases is amyloid fibril deposition (Tycko, 2011). Structurally, these fibrils are characterized by highly repetitive packing of identical peptide chains in extended β-sheets (Tycko, 2011). Owing to the close relationship between protein self-assembly and the etiology of multiple diseases, there has been a longstanding, therapeutic interest in preventing this process.

Alzheimer disease is the most common neurodegenerative disorder and is characterized by the self-assembly of a 40- to 42-amino acid amyloid-beta (Aβ) peptide into amyloid fibrils (Lorenzo and Yankner, 1994). Previous studies have shown that the Aβ assembly process comprises a series of microscopic events involving protein-protein interactions (PPIs) between various molecular species, including monomers, oligomers, and fibrillar aggregates. These events contribute in different extents to the overall kinetics of converting soluble Aβ into amyloid fibrils (Meisl et al., 2017). Therefore, associated PPIs are the likely targets for inhibiting Aβ assembly (Arosio et al., 2014a, Arosio et al., 2014b). Many research groups focus on screening inhibitors that can block or reverse this overall assembly process to alleviate the associated toxicity (Bartolini and Andrisano, 2010; Härd and Lendel, 2012).

As an alternative, structure-based rational design employs structural information of the binding interface to assist in inhibitor construction. This approach has proven particularly effective in the design of PPI inhibitors (Azzarito et al., 2013). However, this structure-based approach to the discovery of protein self-assembly inhibitors is limited by both the complexity of the assembly process and the experimental difficulty in characterizing the structural details of PPIs pertaining to individual microscopic events during the assembly process (Habchi et al., 2017, Munke et al., 2017). So far, there have been a few examples of inhibitor design of inhibitors for Aβ self-assembly (Doig and Derreumaux, 2015, Goyal et al., 2017). Depending on their specific binding modes, these compounds block fibril growth, prevent lateral association of fibrils, or shift the equilibrium toward non-toxic fibrillar aggregates (Jiang et al., 2013, Sievers et al., 2011, Soto et al., 1998).

Recent experimental and theoretical studies have revealed that a fibril's surface can greatly facilitate the conversion of soluble Aβ into oligomeric species (Arosio et al., 2014a, Arosio et al., 2014b, Cohen et al., 2013, Meisl et al., 2014, Michaels et al., 2015, Šarić et al., 2016). Moreover, a key step in the conversion process is the association of soluble Aβ monomers with the fibril surface (Šarić et al., 2016). Because of the growing evidence that oligomers rather than either monomers or fibrillar aggregates are the actual culprits of Aβ toxicity, the interaction between Aβ monomers and the fibril surface is an appealing target for inhibition (Lesne et al., 2006, Munke et al., 2017).

Screening-based approaches have been employed to identify several small molecules and antibodies that abolish the formation of oligomers by specifically impeding binding between the Aβ monomer and its fibril surface (Habchi et al., 2017, Munke et al., 2017). However, because the binding interface between Aβ monomers has not been identified, using a structure-based rational design of PPI inhibitors of this type remains difficult. This gap in knowledge may critically slow or even stop efforts to find more potent candidates.

Given this continued impasse, we sought to apply a structure-based approach to derive potent inhibitors that specifically affect the interaction between soluble Aβ40 monomers and the surface of fibrillar aggregates. In our previous study, using multiscale molecular dynamic simulations, we have identified a binding interface between Aβ monomers and the fibril surface (Jiang et al., 2018a, Jiang et al., 2018b). In particular, a helical motif in the N-terminus of Aβ monomers was found to be essential for the recognition of side-chain arrays on fibril surfaces. Interestingly, helical segments are the most frequently observed binding epitopes at PPI interfaces (Bullock et al., 2011) and are often used as sound starting points in the process of structure-based inhibitor design (Azzarito et al., 2013, Bullock et al., 2011). As such, it was compelling to investigate if a similar strategy could be extended to the discovery of inhibitors for Aβ-fibril interactions.

In practice, peptide fragments seldom exhibit favorable biological activity, which might be ascribed to their poor conformational stability. α-Helical stabilized peptides have been considered as a viable strategy with improved biophysical properties, which are capable of targeting aberrant PPIs (Mahon and Arora, 2012, Verdine and Walensky, 2007, Walensky and Bird, 2014). Given this, we recently developed a facile, helix-nucleating template (termed the Terminal Aspartic acid [TD] strategy) to restrict peptides to a helical conformation, thereby preserving their biological activity (Zhao et al., 2016). The TD strategy has a unique feature of conserving a modifiable NH2 group on the tether for further modification. We have successfully applied this strategy to estrogen receptor α (ER-α) and solved the co-crystal structure of our TD helical peptide in complex with ER-α (Jiang et al., 2018b, Xie et al., 2017).

In this study, we combine chemical synthesis, biophysical characterization, and computational modeling to rationally design a peptide-based inhibitor that can impede the surface-catalyzed oligomerization of Aβ40. The peptide inhibitor was initially constructed based on the previous knowledge of the Aβ-fibril binding interface and subsequently improved by employing our TD strategy. The optimized inhibitor, named cyclic helical amyloid surface inhibitor-1 (cHASI-1), displays micromolar binding affinity for the fibril surface. It selectively impedes the binding between monomers and fibrils of Aβ40, interferes with surface-catalyzed Aβ40 oligomerization, and lowers the cytotoxicity of Aβ40. Our work further suggests that the inhibitor acts by recognizing side-chain array arrangement on the fibril surface. As this structural feature also exists in many other amyloid fibrils, the helical peptide scaffold discovered here may be a useful motif for the design of other amyloid-surface inhibitors.

Results

Design and Optimization of the Peptide Inhibitor cHASI-1

We previously showed that the monomeric Aβ40 binding site on the Aβ fibrils comprise four adjacent side-chain arrays that belong to K16, V18, F20, and E22 (Figure 1A) (Jiang et al., 2018a, Jiang et al., 2018b). When segment Aβ3-14 (-E3FRHDSGYEVHH14-) (Figure S8) of an Aβ40 monomer folds into a helix, it can use its three helical faces to interact specifically with these four side-chain arrays. The helical face including residues D7 and E11 carries two acidic side chains, whereas another face including residues R5 harbors a basic side chain. These helical surfaces attract the positively charged K16 and negatively charged E22 arrays, respectively. A third surface including residues F4 and S8 has a hydrophobic side chain and contacts the V18 and F20 arrays.

Figure 1.

Figure 1

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Design and Optimization of the Peptide Inhibitor cHASI-1

(A) Representative binding mode of Aβ40 monomer on Aβ40 fibril surface (Jiang et al., 2018a, Jiang et al., 2018b). Aβ3-14 is shown in purple, whereas Aβ15-40 is shown in cyan. In the right panel, shown as blue, white and red spheres are side-chain atoms of K16, V18/F20, and E22 on the fibril surface, respectively.

(B) Scheme of HASI-1 (top) and cHASI-1 (bottom). isoD: L-isoaspartic acid; Dap: 2, 3-diaminopropionic acid.

(C) Scheme of expected binding mode of HASI-1, shown as a helical wheel, on Aβ40 fibril surface.

Based on these observations, we derived the following peptide sequence from Aβ3-14 called helical amyloid surface inhibitor 1 (HASI-1): A3FRADVRAERAE14 (Figures 1B, top, S8). Compared with its parent peptide Aβ3-14, HASI-1 carries three like charges on each of its charged helical faces and two hydrophobic side chains on its hydrophobic helical face (Figures 1A and 1C). In addition, we replaced the amino acids of Aβ3-14 that are not directly involved in the binding interface at positions E3, H6, Y10, and H13 with helix-prone alanine. This should allow HASI-1 to bind more strongly to the fibril surface than the parent peptide Aβ3-14.

To test the binding strength of HASI-1 with the fibril surface, we synthesized both Aβ3-14 and HASI-1. We used both fluorescence polarization (FP) and isothermal titration calorimetry (ITC) experiments to measure the affinity of these peptides for Aβ40 aggregates. With a fibril-containing solution of Aβ40, the apparent Kd values for HASI-1 are ∼25 μM using FP and ∼20 μM using ITC. This agreement between the FP and ITC results suggests the robustness of our affinity measurement. There was no obvious binding for Aβ3-14 (Figures 2A and S1O). Thus, and in accordance with our hypothesis, HASI-1 binds to the fibrils more strongly than its parent peptide Aβ3-14. To confirm this finding, we also conducted equilibrium simulations of the binding between both peptides and the surface of Aβ fibrils (see Transparent Methods). We performed simulations using the same multiscale model used previously to probe the binding between the Aβ monomer and its fibril surface (Han and Schulten, 2012, Han and Schulten, 2013, Jiang et al., 2018a, Jiang et al., 2018b). The affinities of HASI-1 and Aβ3-14 were 4.7 μM (Table 1) and 223.2 μM at room temperature (Figures S1A and S1B), respectively. These results corroborated our experiments, indicating that HASI-1 has a much stronger affinity for Aβ fibrils than that of Aβ3-14.

Figure 2.

Figure 2

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Binding Affinity between Peptide Inhibitors and Different Aβ40 Species, and CD Spectra of Peptide Inhibitors

(A) Fluorescence polarization assay showing binding affinity of the 20 nM fluorescein isothiocyanate-labeled peptides to 100 μM fibril-containing solution of Aβ40.

(B) Fluorescence polarization assay showing binding affinity of the 20 nM FITC-labeled cHASI-1 to Aβ40 (100 μM) in different aggregation states (freshly prepared Aβ monomers, 1 h incubated Aβ oligomers, and 24 h incubated Aβ mature fibrils) to obtain binding curves. Buffer: 20 mM sodium phosphate buffer (pH 7.4) supplemented with 200 μM EDTA and 0.02% NaN3. Error bars represent standard deviation from the mean of three independent experiments.

(C) CD spectra of HASI-1 and cHASI-1.

(D) CD spectra of cHASIs and sHASI-1.

All CD measurements were performed in ddH2O, pH 7.0, at 298 K. Their percent helicities were calculated by the [θ] 222 value.

See also Figures S1–S3.

Table 1.

The Experimental and Simulated Affinities of cHASI-1 and Its Variants for Aβ40 Fibrils at Room Temperature

Name Sequence Binding Affinity
Experimental (μM)
 Simulation (μM)
FP ITC
cHASI-1 cyclo(isoD-F-R-Dap)-D-V-R-A-E-R-A-E 3.8 2.9 0.7
cHASI-2 cyclo(isoD-F-E-Dap)-D-V-R-A-R-R-A-E 24.3 27.3 24.0
cHASI-3 cyclo(isoD-F-D-Dap)-R-V-R-A-E-R-A-E 24.6 25.1 22.0
cHASI-4 cyclo(isoD-F-R-Dap)-D-V-R-A-R-E-A-E 19.0 18.9 9.6
HASI-1 A-F-R-A-D-V-R-A-E-R-A-E 25.1 20.4 4.7
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See also Figure S8.

To test if the enhanced binding affinity was simply caused by the changed peptide amino acid composition, we synthesized a sequence-scrambled variant (sHASI-1, Table S1, Figure S8). This scrambled peptide had no measurable binding affinity for Aβ40 fibrils (Figures 2A and S1M). This finding confirms that the amino acid sequence in HASI-1 is critical for this peptide to bind its target.

Although the ability of HASI-1 to recognize the fibril surface was significantly greater than that of Aβ3-14, its affinity for the fibril surface is still weaker than that of full-length Aβ40 (25.1 or 20.4 μM versus 6 μM, respectively)(Meisl et al., 2014). As a helical structure may be required for the binding, it is reasonable to hypothesize that stabilizing the helical propensity of the peptide would further enhance the interaction between the peptide and the fibril surface. To this end, we adopted our TD stapling peptide strategy and mutated A3 and A6 of HASI-1 into an iso-D (L-isoaspartic acid) and a Dap (2,3-diaminopropionic acid), respectively. We then cross-linked the two unnatural amino acids (Figure 3), yielding a cyclic variant (cHASI-1) of HASI-1 that has a TD linker at the N terminus (Figures 1B bottom, Table 1, S8). Our previous work has shown that this N-terminal linker acts as a helical constraint by reducing the entropic cost of helix formation (Zhao et al., 2016).

Figure 3.

Figure 3

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Representative Example for Synthesis of Stabilized Peptides cHASI-1

Synthetic details could be seen in the Transparent Methods.

Circular dichroism (CD) spectroscopy measurements showed that, in solution, cHASI-1 (33% helical content) was more helical than HASI-1 (Figure 2C). FP and ITC experiments (Figures 2A, S2H, and S2I) revealed an affinity of cHASI-1 for the Aβ fibril of 3.8 and 2.9 μM, respectively. This is approximately 6–8 times stronger than that of HASI-1 and almost two times stronger than that of full-length Aβ40 (∼6 μM) (Meisl et al., 2014). Collectively, these findings indicate that cHASI-1 is a promising candidate for the inhibition of Aβ40 aggregation.

Of note, the fitting of the ITC data could also allow us to obtain the binding stoichiometry of macromolecule-ligand interactions. Here, we obtained a binding stoichiometry of around one, which means that each cHASI-1 on average interacted with a single Aβ40 chain unit in the fibrils. According to the computational binding mode, cHASI-1 bound with fibrils could simultaneously interact with about three Aβ chains (Figure 4C). We could not reconcile this discrepancy since the exact concentration of fibrillar species could not be determined because of the stochastic nature of fibril formation. As such, all the fitting was conducted based on total Aβ40 monomer concentration, and therefore, the stoichiometry could not be established accurately. Nonetheless, as will be shown later, we were able to provide other evidence in support of the proposed binding mode (Figure 1C).

Figure 4.

Figure 4

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Probability of cHASIs Bound on Aβ40 Fibril Surface and Their Salt Bridge Interactions

(A) Probability of cHASIs (1–4) being bound on region 16–24 of Aβ40 fibril surface.

(B) Average chance of acidic and basic side chains forming salt bridge interactions with the fibril surface in bound cHASIs (1–4). A salt bridge forms if the distance between the carboxyl oxygen and ammonium/guanidinium nitrogen is shorter than 0.65 nm. Data are represented as mean ± SEM.

(C–H) Most populated binding poses of (C) cHASI-1, (D) cHASI-3, (E) cHASI-4, and (F–H) cHASI-2 on Aβ40 fibril surface. Each pose was taken as the center structure of the most populated conformation cluster. All the variants share a very similar binding pose except for cHASI-2, which exhibits also two additional binding poses with a significant probability that are no longer aligned in the direction of fibril axis (F and H).

See also Figure S7.

Binding Modes between cHASI-1 and Aβ40 Fibrils

Owing to the heterogeneity of the fibril-containing solution, we could not rule out that the peptide could also recognize monomeric and/or oligomeric species. To better determine the binding partners, we used freshly prepared Aβ40 solution to examine the interaction between the peptide and Aβ40 monomers. Our size exclusion chromatograms showed that only Aβ40 monomers are present in the fresh solution (Scheme S1C). FP failed to detect any obvious binding between cHASI-1 and the Aβ40 monomers (Figure 2B). Hence, cHASI-1 is unlikely to associate with monomeric species. Following Luo et al. (Wallin et al., 2018), we also prepared a sample that should contain mainly Aβ40 oligomeric species. Our transmission electron microscopy (TEM) experiment showed that the sample contained larger spherical aggregates with diameter of 15–100 nm, consistent with what was observed previously (Chimon et al., 2007), whereas our western blot experiment showed that the sample contained also small oligomers ranging from dimers to 16-mers (Figures S2A and S2B). The apparent affinity obtained with this sample is 16.5 μM (Figure 2B). Thus, cHASI-1 exhibits a moderate affinity for Aβ40 oligomers but binds much more strongly to the fibrils. We conclude that cHASI-1 specifically recognizes Aβ40 fibrils.

To locate the cHASI-1 binding sites, we also used a method based on aggregation-induced emission (AIE) (Hong et al., 2012). We used a tetra-phenylethene (TPE) group that is useful for in situ monitoring of amyloid fibrillation (Hong et al., 2012). We attached the TPE to the reserved N-terminal on-tether NH2 of cHASI-1 to avoid any large structural perturbation (Figure 3). The modified peptides (cHASI-1-TPE) alone did not emit luminescence (Figures 5A–5C) owing to the multiple ionic side chains of cHASI-1, which provide excellent solubility. In contrast, we detected luminescence increase in a dose-dependent manner when cHASI-1-TPE was incubated with the fibril-containing solution, corroborating the strong ability of cHASI-1 to bind to Aβ fibrils. As expected, sHASI-1-TPE that binds weakly to the fibrils showed negligible luminescence (Figure 5D). We collected the samples from the cHASI-1-TPE/Aβ40 fibril incubation system and could clearly observe that the Aβ40 fibrils were saturated with cHASI-1-TPE (Figures 5E and 5F), suggesting that cHASI-1 is primarily absorbed on the fibril surface.

Figure 5.

Figure 5

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Photograph of cHASI-1-TPE, HASI-1-TPE, and sHASI-1-TPE under Illumination

(A–C) Photographs of 10 μM Aβ40 fibril systems incubated with 0 μM (A), 5 μM (B), and 10 μM (C) cHASI-1-TPE or sHASI-TPE, taken under illumination with a UV light of 365nm. In each panel, cuvettes 1 and 2 contained the blank buffer and 10 μM cHASI-1-TPE alone, respectively. Cuvettes 3 and 4 contained Aβ40 fibril solution incubated with HASI-TPE and cHASI-1-TPE, respectively.

(D–F) (D) Photograph of 10 μM sHASI-1-TPE taken under illumination with a UV light of 365 nm. Bright field (E) and fluorescence image (F) of 10 μM Aβ40 fibrils stained by 10 μM cHASI-1-TPE.

We further probed the structural details of the binding interface between cHASI-1 and the fibril surface to test if the inhibitor worked as designed. We first simulated the binding between cHASI-1 and the fibrils (see Transparent Methods). The simulated binding affinity results agreed well with the experimental value (0.7 μM versus 3.8 or 2.9 μM, respectively) (Table 1 and Figure S1C). The observed interface between cHASI-1 and the fibril surface is similar to what was seen in our previous computational study of Aβ-fibril binding (Jiang et al., 2018a, Jiang et al., 2018b). The positively charged helical face of the inhibitor was in contact with the E22 array of the fibril surface, whereas the negatively charged helical face of the inhibitor was in contact with the K16 array.

To confirm the simulation results, we synthesized a series of cHASI-1 variants (Table 1, Figure S8). Each variant swapped a distinct pair of charged residues from the two oppositely charged helical faces of cHASI-1 (cHASI-2 to cHASI-4). As our CD experiments showed that these variants have a similar helicity to cHASI-1 (Figure 2D), any change in the binding affinity should arise largely from the impact of the modifications on direct binding. If the binding pattern hypothesis is correct, any of the modifications would cause an electrostatic mismatch and impede binding. In support of our hypothesis, both FP and ITC measurements showed that all tested variants had significantly reduced (by about 5- to 6-fold) affinities for the fibril surface as compared with cHASI-1 (Figure 2A and Table 1). Moreover, we also performed binding simulations for the variants revealing a large reduction in affinity for the fibril surface (Table 1 and Figures S1D–S1F). Structural analysis indicated that, although the binding poses of the variants remain basically unchanged (Figures 4A and 4D–H, Table S2, S7), both of their acidic and basic faces form fewer salt bridge contacts with the fibril surface than cHASI-1 (Figure 4B).

In addition, we also determined the significance of hydrophobic interaction between cHASI-1 and the fibril. In our proposed binding mode, the side chains of Phe4 and Val8 of cHASI-1 could interact with the side chains of Phe and Val on the Aβ40 fibril surface through hydrophobic interactions. Consequently, we mutated either Phe4 or Val8 of cHASI-1, or both, into alanine (Figure S8). The FP assay was used to measure their affinity for fibrils. Compared with that of cHASI-1, the fibril affinity of the two variants with single mutation (∼5.4 μM for F4A and ∼6.9 μM for V8A) was reduced by half. The affinity (∼12.9 μM) of the variant containing double mutations (F4A/V8A) became around one-fourth of that of cHASI-1 (Figure S3). These results suggested the importance of Phe4 and Val8 for the hydrophobic interactions involved in the binding between cHASI-1 and Aβ40 fibrils, which further supported our proposed binding mode between cHASI-1 and Aβ40 fibrils.

cHASI-1 Specifically Affects Fibril-Surface-Mediated Nucleation of Aβ40

We next examined how cHASI-1 affects Aβ40 aggregation using a thioflavin T (ThT) assay to examine the aggregation kinetics in the presence or absence of varying cHASI-1 concentrations (Wolfe et al., 2010). Following Meisl et al. (2016), we initially filtered the Aβ40 monomer solution by size-exclusion chromatography (see Scheme S1C). This was done to ensure that the solution used to probe the aggregation kinetics was free of seeds (Hellstrand et al., 2010). In all cases, the aggregation kinetic curve exhibited a typical sigmoid shape, comprising an initial delay in aggregation and then a rapid formation of aggregates (Michaels et al., 2015). cHASI-1 was effective in slowing the lag phase of 10 μM Aβ40 aggregation in a dose-dependent manner from 10 to 50 μM (1 to 5 molar equivalents [M eq]) and became saturated when it reached 5 M eq (Figures 6A and S4A).

Figure 6.

Figure 6

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cHASI-1 Specifically Affects Fibril-Surface-Mediated Oligomerization of Aβ40

(A) Aggregation kinetics of 10 μM Aβ40 in the absence or presence of various amounts (10–50 μM) of cHASI-1.

(B) Kinetics of 10 μM Aβ40 aggregation in the absence or presence of 1–5 molar equivalents (10–50 μM) of HASI-1 relative to Aβ40.

(C) Aggregation kinetics of 10 μM Aβ40 upon addition of preformed Aβ40 fibrils (0.5 μM), grown in the absence or presence of 2.5 equivalent (25 μM) and 5 equivalent (50 μM) of cHASI-1.

(D) Kinetic aggregation of 10 μM Aβ40 solution in the presence of 20% of preformed seeds in the absence or presence of 2.5 or 5 equivalent of cHASI-1 (25μM or 50 μM). Buffer: 20 mM sodium phosphate buffer (pH 7.4) supplemented with 200 μM EDTA and 0.02% NaN3, with 20 μM ThT. The data were from three independent measurements.

See also Figure S4 and Scheme S1.

Unlike cHASI-1, HASI-1 did not cause any noticeable delay in the aggregation process unless its concentration was increased to four times of that of Aβ40 (Figure 6B). The weaker ability of HASI-1 to affect the aggregation kinetics agrees with its lower affinity for the fibril surface (Figure 2A).

The concentration of cHASI-1 needed to effectively inhibit Aβ40 aggregation is much higher than the affinity of this peptide (Kd = 3.8 μM). We attribute this difference to competitive binding of Aβ40 monomers. The affinity of cHASI-1 was measured when the aggregation process was almost complete. Most of the monomers would be assembled in the fibril with a low concentration (<1 μM) of free monomers left in solution (O'Nuallain et al., 2005). These free monomers would have a limited impact on the measured affinity of cHASI-1. In contrast, a much higher concentration (∼10 μM) of free monomers would be present during the lag phase of the ThT kinetics experiment. Thus, many more cHASI-1 molecules are needed to compete with Aβ monomers for the fibrils. Because of this competitive binding, the inhibition effect of cHASI-1 depends on the molar ratio of the peptide inhibitor to Aβ monomers rather than the absolute amount of the peptide added. A similar conclusion was also reached previously for the inhibitory effect of other molecules on Aβ42 aggregation kinetics (Habchi et al., 2017). Nonetheless, the concentration of Aβ40 in vivo is much lower than that used in vitro (McLean et al., 1999). Thus, a lower concentration of cHASI-1 might be able to alter fibril-surface-mediated oligomerization of Aβ40 in vivo.

The aggregation kinetics of Aβ are sensitive to several key events including nucleation in water, nucleation mediated by amyloid surface, and growth of fibrils by addition of free monomers to the ends of existing fibrils (Michaels et al., 2015). To determine which of these events are affected by cHASI-1, we conducted kinetic experiments of Aβ40 aggregation in the presence of various concentrations of seeds. The seed-containing solution was prepared through a procedure reported previously (Cukalevski et al., 2015) (see Transparent Methods). Our TEM experiment confirmed that this solution contained mainly preformed fibrillar aggregates (Figure S2C).The observed morphology was also similar to that of seeds prepared in the previous study (cf. Figure S8 in Ref. Meisl et al., 2014).

We conducted kinetic experiments of Aβ40 aggregation in the presence of various concentrations of seeds, which are mainly preformed fibrils. At low concentrations, the seeds provide additional catalytic surfaces that can allow surfaced-mediated nucleation to dominate the aggregation kinetics; at high concentrations, the kinetics of growth at fibril tips surpass those of the other events (Arosio et al., 2014a, Arosio et al., 2014b, Cukalevski et al., 2015) Therefore, these experiments provide a means of examining the inhibitory effect of cHASI-1 on individual events of aggregation.

The low-seed-concentration and high-seed-concentration regimes for Aβ40 were shown previously to be 0.5%–10% and 10%–50%, respectively (Figures S4D and S4E) (Cukalevski et al., 2015). With the same experimental setup, we found that cHASI-1 can slow aggregation kinetics in a dose-dependent manner in the presence of 5% preformed seeds but did not alter the aggregation kinetics in the presence of 20% preformed seeds (Figures 6C and 6D). This suggests that the peptide affected surface-mediated nucleation but not fibril growth.

To test if cHASI-1 could affect nucleation in water, we fit the kinetic data to the master equation (Eq. [6] in Transparent Methods) derived by Knowles that describes the rate law for the normalized mass of aggregates as a function of time (Meisl et al., 2014). The key parameters of this master equation include the rate constants of oligomerization in water (kn) or on the fibril surface (k2) and fibril growth (k+). To see if a particular microscopic event is affected by the inhibitor, we allowed the rate constant of one event to vary while fitting the data obtained at different inhibitor concentrations using single global rate constants for the other events (Habchi et al., 2017, Munke et al., 2017). Our kinetic data agreed best with a model in which cHASI-1 delays aggregation by suppressing Aβ40 nucleation on the fibril surface instead of nucleation in water (Figures 6A, S4B, and S4C).

cHASI-1 Reduces Formation of Oligomers and Alleviates Aβ40 Toxicity

We monitored the amounts of monomeric and oligomeric species present in samples collected at different time points during aggregation with 5% preformed seeds. Under this condition, the surface-mediated nucleation is supposed to be dominant. Different Aβ species were separated according to molecular weight by size-exclusion chromatography (Cohen et al., 2013). All eluted fractions were divided into three groups: monomers, small oligomers (trimers to 14-mers), and larger oligomers (15-mers to 20-mers) as previously described (Cohen et al., 2015). The amounts of Aβ in each group were measured using an Aβ40-sensitive EP1876Y (Abcam) antibody quantified with Gel-Pro Analyzer (Version 4.0) software (Figure 7C).

Figure 7.

Figure 7

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cHASI-1 Reduces Formation of Oligomers and Alleviates Aβ40 Toxicity

(A) Amounts of Aβ40 oligomers in the absence or presence of cHASI-1. Monomers and oligomers were separated with size-exclusion chromatography and quantified with the EP1876Y (Abcam) antibody at t = 1, 2, 3, 4, and 5 h. Initial concentration of Aβ40 monomers and cHASI-1 were 10 μM and 50 μM, respectively. Preformed fibrils (0.5 μM) were added to promote the oligomerization.

(B) Viability of PC12 and SH-SY5Y treated with preformed Aβ40 fibrils (0.5 μM) and fresh monomer (10 μM) in the absence or presence of 10, 20, and 50 μM cHASI-1. The average and standard deviations shown are over four replicates of each condition. *, p < 0.05, one-way analysis of variance.

(C) Dot-blot assay was quantified by the use of Gel-Pro Analyzer (version 4.0) software. The average and standard deviations shown are over three times of measurements.

(D) Viability of two types of normal cells treated with 2.5–160 μM cHASI-1. Error bars represent SEMs of at least three independent measurements.

See also Figures S5 and S6.

Even at the beginning of the aggregation process, oligomer levels were decreased in the presence of cHASI-1 (Figure 7A). The amount of small and large oligomers obtained with cHASI-1 at t = 1 h was ∼70% and ∼30% less than that of the corresponding oligomer types obtained in the absence of cHASI-1 at the same time point, respectively. Moreover, the levels of small and large oligomers continued to decrease even after the lag phase was surpassed (Figure 6C). Thus, the maximum Aβ40 oligomer level was greatly reduced by cHASI-1. To further confirm that the inhibitor reduced the Aβ40 oligomer level, we also employed SDS PAGE gel electrophoresis assay to analyze the abundance of different oligomer components at the same time points described earlier. A similar reduction in oligomer level was also observed while the monomer level remained largely affected (Figure S5). Our results agreed with a study of the inhibitory effect of a chaperone protein on nucleation from the fibril surface (cf. Figure 4 in Ref. Cohen et al., 2015).

Next, we examined if cHASI-1 could also alleviate the toxicity induced by Aβ40 aggregation. PC12 and SH-SY5Y cells, which have been widely used in the studies of amyloid toxicity (Andreetto et al., 2015, Chen et al., 2017, Chen et al., 2018, Choi et al., 2017, Cohen et al., 2015, Nerelius et al., 2009), were employed in the present study. These two cell types were incubated with the same oligomer sample prepared as earlier (10 μM Aβ40 with 0.5 μM seeds) in the presence or the absence of cHASI-1 to assess the impact of cHASI-1 on the cytotoxicity of Aβ40 aggregates. Cellular viability was approximately 30%–40% survival without inhibitor and was rescued to approximately 75%–85% survival with 5 M eq of cHASI-1 treatment (Figures 7B and S6A). As a control, we also examined the inhibitory effect of three documented amyloid β inhibitors, including Curcumin (Baum and Ng, 2004, Kim et al., 2001, Yang et al., 2005), Galanthamine (Matharu et al., 2009), and Cucurbit [7] uril (Lee et al., 2014). The cHASI-1 was just as effective as these inhibitors (Figure S6B).

As the oligomer sample used was a mixture of monomers, oligomers, and fibrillar seeds, we further incubated the cells with either the fresh monomer solution or the fibril sample to assess which of the species was responsible for the observed cellular toxicity. As shown in Figure S6C, Aβ40 monomers had little effect on cellular viability and Aβ40 fibrils exhibited only moderate toxicity that was still much less than that observed when the cells were incubated with the oligomer sample. Therefore, the observed cellular toxicity was mainly caused by the oligomer species in the sample, and cHASI-1 protected the cells from the aggregate toxicity most likely by lowering the oligomer level.

Finally, we tested the intrinsic cytotoxicity of cHASI-1 using two normal cell lines (HEK-293 and Chang liver) as well as the PC12 and SH-SY5Y cells used in the toxicity test. For all the cell types examined, 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenylt-etrazolium bromide (MTT) results demonstrated only a slight effect on normal cellular growth and proliferation even with the addition of over 100 μM cHASI-1 (Figures 7D and S6D). This finding highlights the low cytotoxicity of cHASI-1, making it a promising candidate for inhibition of Aβ aggregation.

It should be noted that, despite the numerous studies of surface-mediated nucleation of Aβ40, it is not yet clear whether the oligomers produced on the fibril surface are all nuclei. The results presented earlier revealed that the cHASI-1 could reduce the level of nuclei formed on the fibril surface, but we could not rule out the possibility that the formation of certain types of oligomers on the fibril surface may not be affected by the inhibitor. Nevertheless, if such oligomers existed, their cellular toxicity should be insignificant, which would otherwise contradict to what was observed in our cellular toxicity assays.

Discussion

We have rationally designed a peptide-based inhibitor of Aβ aggregation based on structural insights revealed by our previous computational study of the binding between Aβ and fibrils (Jiang et al., 2018a, Jiang et al., 2018b). We demonstrated that the inhibitor, when constrained to a helical conformation, selectively impedes the binding of Aβ monomers to the lateral surface of Aβ fibrils, suppressing Aβ fibril-surface-mediated oligomerization. Furthermore, we showed that, owing to its ability to reduce the production of oligomers, our helical peptide inhibitor mitigates the cellular toxicity of Aβ40.

Owing to the lack of apparent pockets, fibril surfaces have been targeted mainly by flat molecules (Jiang et al., 2013, Young et al., 2017). Our study demonstrates that a helical peptide scaffold can also recognize the fibril surface. In a helical conformation, peptides with proper sequences can arrange their amino acid side chains with similar properties on the same helical face. When aligned in the direction of the fibril, the inhibitor uses its helical face to form extensive interactions with side-chain arrays on the fibril surface. Several faces of the helix of the designed inhibitor, cHASI-1, simultaneously interact with adjacent side-chain arrays on the Aβ40 fibril surface, maximizing both hydrophobic and electrostatic attraction. Since side-chain arrays are a common feature of many other amyloid fibrils of alpha-synuclein, human islet amyloid polypeptide, and prions (Ke et al., 2017), this helical peptide scaffold can be applied to the development of modulators of other protein aggregation.

Artificially mimicking conformationally specific peptides by chemically “stapling” amino acids at the primary sequence level has become increasingly attractive (Walensky and Bird, 2014). Owing to their facile modification, it is feasible to construct specific peptides in a particular conformation by a series of strategies, such as introducing β-amino acids (Schmitt et al., 2006). In this study, helicity is subtly influenced by the N-terminal nucleating TD tether as shown by CD (Figures 2C and 2D). Different from most nucleating methods, our TD strategy preserves an NH2 group at the N terminus, providing an access point for additional chemical modifications. Various functional groups may be stitched, including the AIE dye used here and others such as black phosphorus (Chen et al., 2018) for nano-medicine. We anticipate that our combined experimental and computational approach will allow for well-designed and powerful peptides for better understanding and therapeutic management of amyloid diseases.

Limitation of Study

This work reports an important discovery of recognition patterns of Aβ fibril surface with conformation constraint peptides, which may offer a general concept for designing modulators for amyloid-β aggregation by peptides. Although we have demonstrated the pattern experimentally in vitro, the in vivo performance remains to be answered. To improve the in vivo performance, we could further manipulate the chemical structure of cHASI-1. For example, we could sew a shuttle peptide at the N terminus of cHASI-1 to address the delivery issue across the blood-brain barrier (Oller Salvia et al., 2016).

Methods

All methods can be found in the accompanying Transparent Methods supplemental file.

Acknowledgments

We acknowledge financial support from the Natural Science Foundation of China (Grants 21778009 and 81572198 to Z.L., Grant 21673013 to W.H., and Grants 81701818 to F.Y.) and from the Shenzhen Science and Technology Innovation Committee (Grants JCYJ-20170412150609690, KQJSCX20170728101942700 to Z.L., Grants JCYJ20160330095839867, JCYJ20170818085409785, and KQTD2015032709315529 to W.H., and Grants JCYJ20170807144449135 to F.Y.). This work is supported by High-Performance Computing Platform of Peking University. We acknowledge financial support from Beijing National Laboratory of Molecular Science open grant BNLMS20160112.

Author Contributions

Conceptualization and design – Y.J., X.J., W.H., Z.L. Data generation – Y.J., X.J., X.S., F. Yang, Y.C., X.Q., Z.H., M.X., N.L., Q.F.). Data analysis and interpretation – Y.J., X.J., W.H., Z.L. Manuscript preparation – Y.J., X.J., F. Yin, W.H., Z.L. Review and editing – F. Yin, W.H., Z.L. Funding acquisition – F. Yin, W.H., Z.L.

Declaration of Interests

The authors declare no competing interests.

Published: July 26, 2019

Footnotes

Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2019.06.022.

Contributor Information

Feng Yin, Email: yinfeng@pkusz.edu.cn.

Wei Han, Email: hanw@pkusz.edu.cn.

Zigang Li, Email: lizg@pkusz.edu.cn.

Supplemental Information

Document S1. Transparent Methods, Figures S1–S9, Scheme S1, and Tables S1 and S2
mmc1.pdf (2MB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Transparent Methods, Figures S1–S9, Scheme S1, and Tables S1 and S2
mmc1.pdf (2MB, pdf)

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