Mechanical and thermodynamic properties of Aβ₄₂, Aβ₄₀, and α-synuclein fibrils: a coarse-grained method to complement experimental studies

Abstract

We perform molecular dynamics simulation on several relevant biological fibrils associated with neurodegenerative diseases such as Aβ40, Aβ42, and α-synuclein systems to obtain a molecular understanding and interpretation of nanomechanical characterization experiments. The computational method is versatile and addresses a new subarea within the mechanical characterization of heterogeneous soft materials. We investigate both the elastic and thermodynamic properties of the biological fibrils in order to substantiate experimental nanomechanical characterization techniques that are quickly developing and reaching dynamic imaging with video rate capabilities. The computational method qualitatively reproduces results of experiments with biological fibrils, validating its use in extrapolation to macroscopic material properties. Our computational techniques can be used for the co-design of new experiments aiming to unveil nanomechanical properties of biological fibrils from a point of view of molecular understanding. Our approach allows a comparison of diverse elastic properties based on different deformations , i.e., tensile (Y L), shear (S), and indentation (Y T) deformation. From our analysis, we find a significant elastic anisotropy between axial and transverse directions (i.e., Y T > Y L) for all systems. Interestingly, our results indicate a higher mechanostability of Aβ42 fibrils compared to Aβ40, suggesting a significant correlation between mechanical stability and aggregation propensity (rate) in amyloid systems. That is, the higher the mechanical stability the faster the fibril formation. Finally, we find that α-synuclein fibrils are thermally less stable than β-amyloid fibrils. We anticipate that our molecular-level analysis of the mechanical response under different deformation conditions for the range of fibrils considered here will provide significant insights for the experimental observations.

Quantitative determination of mechanical stability in the novel coronavirus spike protein

“…To further demonstrate the mechanical stability of the RBD, we employed a validated structure-based modelling approach. 15,28,29,31,32 Our computational structure-based study (see Fig. 3) shows that the RBD is a mechanically stable component in the spike (see next section) and also identifies the structural motif (see Fig.…”

“…The nanomechanical simulations are based on the Gō-like model 23,24 that has been used to sample conformational changes in proteins and calculate the elastic parameters under force deformation in single proteins, protein filaments, cellulose, and protein-protein, protein-polysaccharide and protein-lipid interfaces. 15,[25][26][27][28] At first we pull each chain of the trimer to identify the mechanostable protein domains. Our results for CoV2 show the RBD to be last domain to unfold.…”

Differentiating between Inactive and Active States of Rhodopsin by Atomic Force Microscopy in Native Membranes

“…To confirm FD-AFM results indicating an increased stiffness in the structure of rhodopsin upon activation, nanoindentation studies were carried out computationally on coarse-grained (CG) models of inactive and active rhodopsin, which were based on crystal structures [30][31] . Similar computational nanoindentation experiments were successfully conducted previously on CG models of cellulose and other protein systems to obtain estimates of Young's moduli [32][33] . Nanoindentation was performed by pressing a sphere into the extracellular surface of rhodopsin ( Fig.…”

The Role of Structural Polymorphism in Driving the Mechanical Performance of the Alzheimer's Beta Amyloid Fibrils

“…The present study employs steered molecular dynamics (SMD) simulations to provide a detailed mechanical characterization of U- and S-shaped Aβ 17−42 small fibrils. The computational workflow here employed was already successfully applied for similar system in recent literature (Ndlovu et al, 2012; Paul et al, 2016; Poma et al, 2019).…”