Tyrosine fluorescence spectra for the determination of Cu²⁺ binding affinity.

<p>(A) Aβ1-16, (B)Aβ1-24, (C)Aβ1-29, (D)Aβ1-35, (E)Aβ1-40. The concentration of Aβ peptides was 10 µM. The solid lines represent the best fitting curve using the independent two-Cu mode, whereas dot lines show the fitting curve simulated using one-Cu mode as depicted in the section of material and methods.</p

The content of secondary structure for the different Aβ peptides in the presence or absence of Cu²⁺ as calculated from CD spectra.

<p>*NRMSD (normalized root mean square standard deviation)  =  [(θ_obs(λ)-θ_cal(λ))²/(θ_obs(λ))²]^1/2.</p

The aggregation profiles.

<p>The aggregation profile determined by turbidity assay in the absence (A) and the presence (B) of Cu²⁺ for different Aβ peptides, (⋄) Aβ1-16, (▾) Aβ1-24, (▴) Aβ1-29, (○) Aβ1-35, and (▪) Aβ1-40.</p

The estimated EPR parameters of Aβ/copper (II) complex for the different Aβ peptides.

<p>The estimated EPR parameters of Aβ/copper (II) complex for the different Aβ peptides.</p

The estimated copper (II) binding constant using one-Cu and dependent two-Cu models for the different Aβ peptides.

<p>The estimated copper (II) binding constant using one-Cu and dependent two-Cu models for the different Aβ peptides.</p

The plot of secondary structure content vs. Cu²⁺ concentration.

<p>The plot for different Aβ peptides, (▪) Aβ1-16, (♦) Aβ1-24, (▴) Aβ1-29, (○) Aβ1-35, (▾) Aβ1-40 and (A) β–sheet percentage, (B) random coil percentage, and (C) α-helx. (D) The plot of β–sheet propensity vs. Aβ peptides.</p

Circular dichroism spectra of Aβ peptides.

<p>CD spectra for different Aβ peptides, (▿) Aβ1-16, (□) Aβ1-24, (•) Aβ1-29, (○) Aβ1-35, (▪) Aβ1-40, in the absence (A) and presence (B) of Cu²⁺. The concentration for both Aβ peptides and Cu²⁺ used in measurements was 30 µM. A normalized root mean square standard deviation (NRMSD) parameter was introduced to indicate for the quality between observed and calculated CD spectra.</p

The plot of DCF fluorescence intensity vs. Aβ concentration.

<p>The plot for different Aβ peptides, (▴) 30 µM Cu²⁺ alone, (♦) Aβ1-16, (▵) Aβ1-24, (•) Aβ1-29, (○) Aβ1-35, (◊) Aβ1-40, and (▿) Aβ25-35 in the presence of 30 µM Cu²⁺. Instead of generating H₂O₂, most Aβ peptides, except of Aβ25-35, inhibit the generation of H₂O₂. All measurements were measured after the fresh prepared samples were incubated at 37°C for 1 hr.</p

The TEM images of Aβ fibril morphologies.

<p>Images A, C, E and G represent the fibril morphologies for Aβ1-40, Aβ1-35, Aβ1-29 and Aβ1-16 with Cu²⁺ stripped off by EDTA, respectively. Images B, D, F and H represent the morphologies for Aβ1-40, Aβ1-35, Aβ1-29 and Aβ1-16 in the presence of Cu²⁺, respectively.</p

Anti-arrhythmic Medication Propafenone a Potential Drug for Alzheimer’s Disease Inhibiting Aggregation of Aβ: In Silico and in Vitro Studies

Alzheimer’s disease (AD) is the most common form of dementia caused by the formation of Aβ aggregates. So far, no effective medicine for the treatment of AD is available. Many efforts have been made to find effective medicine to cope with AD. Curcumin is a drug candidate for AD, being a potent anti-amyloidogenic compound, but the results of clinical trials for it were either negative or inclusive. In the present study, we took advantages from accumulated knowledge about curcumin and have screened out four compounds that have chemical and structural similarity with curcumin more than 80% from all FDA-approved oral drugs. Using all-atom molecular dynamics simulation and the free energy perturbation method we showed that among predicted compounds anti-arrhythmic medication propafenone shows the best anti-amyloidogenic activity. The in vitro experiment further revealed that it can inhibit Aβ aggregation and protect cells against Aβ induced cytotoxicity to almost the same extent as curcumin. Our results suggest that propafenone may be a potent drug for the treatment of Alzheimer’s disease