The Effects of Different Glycosaminoglycans on the Structure and Aggregation of the Amyloid-β (16–22) Peptide

Abstract

Aggregates of the amyloid-β (Aβ) peptide are implicated as a causative substance in Alzheimer’s disease. Molecular dynamics simulations provide valuable contributions for elucidating the conformational transitions of monomeric and aggregated forms of Aβ be it in solution or in the presence of other molecules. Here, we study the effects of four different glycosaminoglycans (GAGs), three sulfated ones and a nonsulfated one, on the aggregation of Aβ16–22. From experiments, it has been suggested that GAGs, which belong to the main components of the brain’s extracellular space, favor amyloid fibril formation. Our simulation results reveal that the binding of Aβ16–22 to the GAGs is driven by electrostatic attraction between the negative GAG charges and the positively charged K16 of Aβ16–22. While these interactions have only minor effects on the GAG and Aβ16–22 conformations at the 1 Aβ16–22/1 GAG ratio, at the 2:2 stoichiometry the aggregation of Aβ16–22 is considerably changed. In solution, the aggregation of Aβ16–22 is strongly influenced by K16–E22 attraction, leading to antiparallel β-sheets. In the presence of GAGs, on the other hand, the interaction of K16 with the GAGs increases the importance of the hydrophobic interactions during Aβ16–22 aggregation, which in turn yields parallel alignments. A templating and ordering effect of the GAGs on the Aβ16–22 aggregates is observed. In summary, this study provides new insight at the atomic level on GAG–amyloid interactions, strengthening the view that sulfation of the GAGs plays a major role in this context.

The Influences of Sulphation, Salt Type, and Salt Concentration on the Structural Heterogeneity of Glycosaminoglycans

“…All MD simulations were realized with GROMACS Version 2018.3 [ 64 , 65 , 66 ]. For the analysis of the simulation trajectories, we employed a combination of standard GROMACS tools, VMD [ 67 ], and in-house Python scripts [ 68 ] invoking the MDAnalysis [ 69 ] and MDTraj [ 70 ] libraries. The workflow to perform a dimensionality reduction, clustering based on internal GAG coordinates, and calculation of the free energy profile of the GAGs is available at https://github.com/suman-samantray/GAG-clustering-FES (accessed 22 July 2021).…”

Transition Networks Unveil Disorder-to-Order Transformations in Aβ Caused by Glycosaminoglycans or Lipids

“…CHARMM36m is a polypeptide force field that can be combined with the original force field CHARMM36 [41] for modeling the POPC lipids. For the GAGs, we used the parameters as available through the Glycan Reader & Modeler module [42][43][44] of the CHARMM-GUI web server [45], as in our previous studies of GAGs alone and in interaction with multiple Aβ 16−22 peptides [23,46]. The preparation of the systems followed the same standard protocol: First the solute(s) were placed in the simulation box, which was then filled with TIP3P water molecules [47] together with Na + and Cl − ions to neutralize the systems and achieve a physiological salt concentration of 150 mM.…”