Mechanism of Initiation, Association, and Formation of Amyloid Fibrils Modeled with the N-Terminal Peptide Fragment, IKYLEFIS, of Myoglobin G-Helix

Multistep molecular mechanisms of Aβ16-22 fibril formation revealed by lattice Monte Carlo simulations

As a model of self-assembly from disordered monomers to fibrils, the amyloid-β fragment Aβ16-22 was subject to past numerous experimental and computational studies. Because dynamics information between milliseconds and seconds cannot be assessed by both studies, we lack a full understanding of its oligomerization. Lattice simulations are particularly well suited to capture pathways to fibrils. In this study, we explored the aggregation of 10 Aβ16-22 peptides using 65 lattice Monte Carlo simulations, each simulation consisting of 3 × 109 steps. Based on a total of 24 and 41 simulations that converge and do not converge to the fibril state, respectively, we are able to reveal the diversity of the pathways leading to fibril structure and the conformational traps slowing down the fibril formation.

Investigating the Sequence Determinants of the Curling of Amyloid Fibrils Using Ovalbumin as a Case Study

Highly ordered, straight amyloid fibrils readily lend themselves to structure determination techniques and have therefore been extensively characterized. However, the less ordered curly fibrils remain relatively understudied, and the structural organization underlying their specific characteristics remains poorly understood. We found that the exemplary curly fibril-forming protein ovalbumin contains multiple aggregation prone regions (APRs) that form straight fibrils when isolated as peptides or when excised from the full-length protein through hydrolysis. In the context of the intact full-length protein, however, the regions separating the APRs facilitate curly fibril formation. In fact, a meta-analysis of previously reported curly fibril-forming proteins shows that their inter-APRs are significantly longer and more hydrophobic when compared to straight fibril-forming proteins, suggesting that they may cause strain in the amyloid state. Hence, inter-APRs driving curly fibril formation may not only apply to our model protein but rather constitute a more general mechanism.

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Effect of TMAO and Urea on Dimers and Tetramers of Amyloidogenic Heptapeptides (23FGAILSS29)

Human islet amyloid polypeptide (hIAPP) (1-37) is an intrinsically disordered protein that is released with insulin by β-cells found in the pancreas. Under certain environmental conditions, hIAPP can aggregate, which leads to β-cell death. FGAILSS (23-29) residues of the hIAPP protein form β sheets, which may be toxic species in type 2 diabetes (T2D) patients. All-atom molecular dynamics (MD) simulations have been used to analyze the effect of two distinct types of osmolytes trimethylamine N-oxide (TMAO) and urea on two and four FGAILSS heptapeptides. TMAO leads the individual peptide toward an extended conformation with a higher radius of gyration and favors the formation of antiparallel β-sheets with an increase in its concentration. However, urea mostly shows compaction of individual peptides except at 4.0 M in the case of a tetramer but does not show aggregation behavior as a whole. TMAO leads both the dimer and tetramer toward the native state with an increase in its concentration. Moreover, both the dimer and tetramer show irregular behavior in urea. The tetramer in 4.0 M urea shows the maximum fraction of native contacts due to the formation of antiparallel β-sheets. This formation of antiparallel β-sheets favors the aggregation of peptides. TMAO forms a smaller number of hydrogen bonds with peptides as compared to urea as the exclusion of TMAO and accumulation of urea around the peptides have occurred in the first solvation shell (FSS). Principal component analysis (PCA) results suggest that the minima in the free energy landscape (FEL) plot are homogeneous for a particular conformation in TMAO with smaller basins, while in urea, the dimer shows minima mostly for extended conformations. For a 4.0 M urea concentration, the tetramer shows the minimum for antiparallel β-sheets, which indicates the aggregation behavior of the tetramer, and for a higher concentration, it shows minima with wider basins of extended conformations.