Role of Hydrophobicity and Charge of Amyloid-Beta Oligomer Eliminating d‑Peptides in the Interaction with Amyloid-Beta Monomers

Inhibition of the self-assembly process of amyloid-beta and even more the removal of already existing toxic amyloid-beta assemblies represent promising therapeutic strategies against Alzheimer’s disease. To approach this aim, we selected a d-enantiomeric peptide by phage-display based on the interaction with amyloid-beta monomers. This lead compound was successfully optimized by peptide microarrays with respect to its affinity and specificity to the target resulting in d-peptides with both increased hydrophobicity and net charge. Here, we present a detailed biophysical characterization of the interactions between these optimized d-peptides and amyloid-beta monomers in comparison to the original lead compound in order to obtain a more thorough understanding of the physicochemical determinants of the interactions. Kinetics and apparent stoichiometry of complex formation were studied using surface plasmon resonance. Potential modes of binding to amyloid-beta were identified, and the influences of ionic strength on complex stability, as well as on the inhibitory effect on amyloid-beta aggregation were investigated. The results reveal a very different mode of interaction of the optimized d-peptides based on a combination of electrostatic and hydrophobic interactions as compared to the mostly electrostatically driven interaction of the lead compound. These conclusions were supported by the thermodynamic profiles of the interaction between optimized d-peptides and Aβ monomers, which indicate an increase in binding entropy with respect to the lead compound

Amino acid sequences of D3 and DB1 to DB5.

<p>Amino acid sequences of D3 and DB1 to DB5.</p

Inhibition of Aβ fibril formation and Aβ aggregation disassemble by DB3 and DB3DB3.

<p>A) Monomeric Aβ(1–42) (400 nM) was mixed with different concentrations of DB3 (0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100 μM) and the aggregation state of Aβ was analyzed using an Aβ aggregate specific ELISA. For DB3DB3 half of the molar concentrations compared to DB3 were used. Aβ without DB3 and DB3DB3 addition was taken as control. For DB3 an EC₅₀ of 6 μM was calculated using a logistic fit model. DB3DB3 inhibited the formation of Aβ fibrils more efficiently with an EC₅₀ of 7 nM. B) The disassembly properties of DB3 and DB3DB3 were measured using an Aβ aggregation specific ELISA. Monomeric Aβ(1–42) (400 nM) was preincubated in order to form fibrils and mixed with nine different concentrations of DB3 (0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, 100 μM). For DB3DB3, the molar concentrations were half those used for DB3. For DB3 an EC₅₀ of 2.5 μM was determined. DB3DB3 disassembled Aβ aggregates at the lowest concentration (10 nM). Thus, the EC₅₀ could not be determined, but is < 10 nM. All data were determined in triplicate. The Mann-Whitney-U-test was performed for statistical analysis. * <i>p</i>< 0.05; ** <i>p</i> < 0.01; *** <i>p</i> < 0.001</p

TEM of Aβ-DB3 and -DB3DB3 co-complexes.

<p>10 μM initial monomeric Aβ(1–42) without (A) and with 10 μM DB3 (B) or 5 μM DB3DB3 (C) were coincubated for 24 h. Subsequently, the samples were absorbed onto formval/carbon coated copper grids and negative stained with 1% uranyl acetate. The images were obtained using a transmission electron microscope (TEM). Scale bar: 0.25 μm.</p

Aggregation state of monomeric Aβ(1–42) after 1 h incubation.

<p>For optimization of D3 with peptide microarrays, the peptide microarrays were incubated with 5 μM initially monomeric Aβ(1–42) for 1 h at room temperature. The aggregation state of this Aβ preparation was analyzed by density gradient centrifugation followed by 16% Tricine-SDS-PAGE. FITC-Aβ(1–42) was detected via FITC fluorescence and was only detectable in the first four lanes, which represent mainly monomeric and oligomeric FITC-Aβ [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153035#pone.0153035.ref014" target="_blank">14</a>].</p

Selection of DB1 to DB5 based on two cycles of peptide microarray based screenings.

<p>A) Promising replacements in the sequence of D3 were selected via PepSpots peptide array. Binding of monomeric Aβ(1–42) to spotted D3 derivatives was detected using the Aβ antibody 6E10 and a HRP-labeled secondary antibody. Several of the dots with the highest staining density, representing the most promising single replacements, are marked in red. The original D3 controls are indicated in blue. B) The HRP-intensity was evaluated by the staining density of the peptide dots and plotted against the amino acid substitutions. Eleven promising substitutions that showed > 1.5 times increase in binding to monomeric Aβ(1–42) when compared with that of D3, were chosen for a second generation peptide microarray. The red line represents the mean dot staining intensity of D3. C) Schematic overview of the first generation microarray output. D) Binding of FITC-Aβ(1–42) to the peptides DB1 to DB5. The binding of FITC-Aβ(1–42) to the spotted peptides was analyzed by measuring the FITC-fluorescence intensity. All intensities were background corrected. The signal intensities of the top five peptides were plotted. The red line represents the mean fluorescence intensity of D3.</p

K_D determination of DB3 and DB3DB3 to monomeric Aβ using biolayer interferometry (BLI).

<p>N-terminally biotinylated Aβ(1–42) monomers were immobilized on streptavidin biosensors and the binding of DB3 and DB3DB3 was detected. Representative double referenced sensorgrams of a dilution series of DB3 (A) and DB3DB3 (B) are shown, including the equilibrium dissociation constants (K_D) as means ± SD of data recorded in triplicate. For steady state analysis Langmuir´s 1:1 binding model was applied. Representative fits of DB3 (C) and DB3DB3 (D) are depicted with the corresponding corrected <i>R</i>².</p

Influence of DB3 and DB3DB3 on Aβ-introduced cytotoxicity.

<p>The cell viability assay was performed using PC12 cells in a MTT test. Therefore, Aβ(1–42) was preincubated for 4.5 h and further coincubated with DB3 or DB3DB3 for 40 min. The cells were incubated for 24 h with the Aβ(1–42)-peptide mixture or Aβ(1–42) alone as a control. The absorption of buffer treated cells was set to 100% cell viability. The cell viability of cells treated with Aβ and DB3 or DB3DB3 were compared with cells treated with Aβ only. The Mann-Whitney-U-test was used for statistical analysis. * <b><i>p</i></b> < 0.05; ** <b><i>p</i></b> < 0.01; *** <b><i>p</i></b> < 0.001.</p

Optimization of d‑Peptides for Aβ Monomer Binding Specificity Enhances Their Potential to Eliminate Toxic Aβ Oligomers

Amyloid-beta (Aβ) oligomers are thought to be causative for the development and progression of Alzheimer’s disease (AD). Starting from the Aβ oligomer eliminating d-enantiomeric peptide D3, we developed and applied a two-step procedure based on peptide microarrays to identify D3 derivatives with increased binding affinity and specificity for monomeric Aβ(1–42) to further enhance the Aβ oligomer elimination efficacy. Out of more than 1000 D3 derivatives, we selected seven novel d-peptides, named ANK1 to ANK7, and characterized them in more detail in vitro. All ANK peptides bound to monomeric Aβ(1–42), eliminated Aβ(1–42) oligomers, inhibited Aβ(1–42) fibril formation, and reduced Aβ(1–42)-induced cytotoxicity more efficiently than D3. Additionally, ANK6 completely inhibited the prion-like propagation of preformed Aβ(1–42) seeds and showed a nonsignificant tendency for improving memory performance of tg-APPSwDI mice after i.p. application for 4 weeks. This supports the hypothesis that stabilization of Aβ monomers and thereby induced elimination of Aβ oligomers is a suitable therapeutic strategy

Thioflavin T fibril formation assay.

<p>20 μM Aβ(1–42) was mixed with 20 μM DB1 to DB5 or 10 μM DB3DB3 and the ThT fluorescence was monitored. Aβ(1–42) without peptide addition was taken as the control. The ThT fluorescence of all samples were compared after 5 h, where the control, Aβ(1–42) only, reached its maximum in fluorescence emission. The Mann-Whitney-U-test was used for statistical analysis. * <i>p</i> < 0.05; ** <i>p</i> < 0.01; *** <i>p</i> < 0.001.</p