Translational, rotational and internal dynamics of amyloid beta-peptides (Abeta40 and Abeta42) from molecular dynamics simulations

The growth rate of senile plaques is determined by the competition between the rate of deposition of free Aβ aggregates into plaques and the autocatalytic production of free Aβ aggregates

The formation of amyloid beta (Aβ) deposits (senile plaques) is one of the hallmarks of Alzheimer's disease (AD). This study investigates what processes are primarily responsible for their formation. A model is developed to simulate the diffusion of amyloid beta (Aβ) monomers, the production of free Aβ aggregates through nucleation and autocatalytic processes, and the deposition of these aggregates into senile plaques. The model suggests that efficient degradation of Aβ monomers alone may suffice to prevent the growth of senile plaques, even without degrading Aβ aggregates and existing plaques. This is because the degradation of Aβ monomers interrupts the supply of reactants needed for plaque formation. The impact of Aβ monomer diffusivity is demonstrated to be small, enabling the application of the lumped capacitance approximation and the derivation of approximate analytical solutions for limiting cases with both small and large rates of Aβ aggregate deposition into plaques. It is found that the rate of plaque growth is governed by two competing processes. One is the deposition rate of free Aβ aggregates into senile plaques. If this rate is small, the plaque grows slowly. However, if the rate of deposition of Aβ aggregates into senile plaques is very large, the free Aβ aggregates are removed from the intracellular fluid by deposition into the plaques, leaving insufficient free Aβ aggregates to catalyze the production of new aggregates. This suggests that under certain conditions, Aβ plaques may offer neuroprotection and impede their own growth. Additionally, it indicates that there exists an optimal rate of deposition of free Aβ aggregates into the plaques, at which the plaques attain their maximum size.

Numerical modeling of senile plaque development under conditions of limited diffusivity of amyloid-β monomers

This paper introduces a new model to simulate the progression of senile plaques, focusing on scenarios where concentrations of amyloid beta (Aβ) monomers and aggregates vary between neurons. Extracellular variations in these concentrations may arise due to limited diffusivity of Aβ monomers and a high rate of Aβ monomer production at lipid membranes, requiring a substantial concentration gradient for diffusion-driven transport of Aβ monomers. The dimensionless formulation of the model is presented, which identifies four key dimensionless parameters governing the solutions for Aβ monomer and aggregate concentrations, as well as the radius of a growing Aβ plaque within the control volume. These parameters include the dimensionless diffusivity of Aβ monomers, the dimensionless rate of Aβ monomer production, and the dimensionless half-lives of Aβ monomers and aggregates. A dimensionless parameter is then introduced to evaluate the validity of the lumped capacitance approximation. An approximate solution is derived for the scenario involving large diffusivity of Aβ monomers and dysfunctional protein degradation machinery, resulting in infinitely long half-lives for Aβ monomers and aggregates. In this scenario, the concentrations of Aβ aggregates and the radius of the Aβ plaque depend solely on a single dimensionless parameter that characterizes the rate of Aβ monomer production. According to the approximate solution, the concentration of Aβ aggregates is linearly dependent on the rate of monomer production, and the radius of an Aβ plaque is directly proportional to the cube root of the rate of monomer production. However, when departing from the conditions of the approximate solution (e.g., finite half-lives), the concentrations of Aβ monomers and aggregates, along with the plaque radius, exhibit complex dependencies on all four dimensionless parameters. For instance, under physiological half-life conditions, the plaque radius reaches a maximum value and stabilizes thereafter.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

Relaxation dynamics measure the aggregation propensity of amyloid-β and its mutants

Atomistic molecular dynamics simulations are employed to investigate the global and segmental relaxation dynamics of the amyloid-β protein and its causative and protective mutants. Amyloid-β exhibits significant global/local dynamics that span a broad range of length and time scales due to its intrinsically disordered nature. The relaxation dynamics of the amyloid-β protein and its mutants is quantitatively correlated with its experimentally measured aggregation propensity. The protective mutant has slower relaxation dynamics, whereas the causative mutants exhibit faster global dynamics compared with that of the wild-type amyloid-β. The local dynamics of the amyloid-β protein or its mutants is governed by a complex interplay of the charge, hydrophobicity, and change in the molecular mass of the mutated residue.

Simulating the effect of formation of amyloid plaques on aggregation of tau protein

In this paper, we develop a mathematical model that enables the investigation of the production and intracellular transport of amyloid precursor protein (APP) and tau protein in a neuron. We also investigate the aggregation of APP fragments into amyloid-β (Aβ) as well as tau aggregation into tau oligomers and neurofibrillary tangles. Using the developed model, we investigate how Aβ aggregation can influence tau transport and aggregation in both the soma and the axon. We couple the Aβ and tau agglomeration processes by assuming that the value of the kinetic constant that describes the autocatalytic growth (self-replication) reaction step of tau aggregation is proportional to the Aβ concentration. The model predicts that APP and tau are distributed differently in the axon. While APP has a uniform distribution along the axon, tau's concentration first decreases and then increases towards the synapse. Aβ is uniformly produced along the axon while misfolded tau protein is mostly produced in the proximal axon. The number of Aβ and tau polymers originating from the axon is much smaller than the number of Aβ and tau polymers originating from the soma. The rate of production of misfolded tau polymers depends on how strongly their production is facilitated by Aβ.

Monomer Dynamics of Alzheimer Peptides and Kinetic Control of Early Aggregation in Alzheimer's Disease

The rate of reconfiguration-or intramolecular diffusion-of monomeric Alzheimer (Aβ) peptides is measured and, under conditions that aggregation is more likely, peptide diffusion slows down significantly, which allows bimolecular associations to be initiated. By using the method of Trp-Cys contact quenching, the rate of reconfiguration is observed to be about five times faster for Aβ₄₀ , which aggregates slowly, than that for Aβ₄₂ , which aggregates quickly. Furthermore, the rate of reconfiguration for Aβ₄₂ speeds up at higher pH, which slows aggregation, and in the presence of the aggregation inhibitor curcumin. The measured reconfiguration rates are able to predict the early aggregation behavior of the Aβ peptide and provide a kinetic basis for why Aβ₄₂ is more prone to aggregation than Aβ₄₀ , despite a difference of only two amino acids.

Mechanisms of peptide hydrolysis by aspartyl and metalloproteases

Peptide hydrolysis has been involved in a wide range of biological, biotechnological, and industrial applications.

Discrete molecular dynamics study of oligomer formation by N-terminally truncated amyloid β-protein

In Alzheimer's disease (AD), amyloid β-protein (Aβ) self-assembles into toxic oligomers. Of the two predominant Aβ alloforms, Aβ1-40 and Aβ1-42, the latter is particularly strongly linked to AD. N-terminally truncated and pyroglutamated Aβ peptides were recently shown to seed Aβ aggregation and contribute significantly to Aβ-mediated toxicity, yet their folding and assembly were not explored computationally. Discrete molecular dynamics approach previously captured in vitro-derived distinct Aβ1-40 and Aβ1-42 oligomer size distributions and predicted that the more toxic Aβ1-42 oligomers had more flexible and solvent-exposed N-termini than Aβ1-40 oligomers. Here, we examined oligomer formation of Aβ3-40, Aβ3-42, Aβ11-40, and Aβ11-42 by the discrete molecular dynamics approach. The four N-terminally truncated peptides showed increased oligomerization propensity relative to the full-length peptides, consistent with in vitro findings. Conformations formed by Aβ3-40/42 had significantly more flexible and solvent-exposed N-termini than Aβ1-40/42 conformations. In contrast, in Aβ11-40/42 conformations, the N-termini formed more contacts and were less accessible to the solvent. The compactness of the Aβ11-40/42 conformations was in part facilitated by Val12. Two single amino acid substitutions that reduced and abolished hydrophobicity at position 12, respectively, resulted in a proportionally increased structural variability. Our results suggest that Aβ11-40 and Aβ11-42 oligomers might be less toxic than Aβ1-40 and Aβ1-42 oligomers and offer a plausible explanation for the experimentally observed increased toxicity of Aβ3-40 and Aβ3-42 and their pyroglutamated forms.

Connecting macroscopic observables and microscopic assembly events in amyloid formation using coarse grained simulations

The pre-fibrillar stages of amyloid formation have been implicated in cellular toxicity, but have proved to be challenging to study directly in experiments and simulations. Rational strategies to suppress the formation of toxic amyloid oligomers require a better understanding of the mechanisms by which they are generated. We report Dynamical Monte Carlo simulations that allow us to study the early stages of amyloid formation. We use a generic, coarse-grained model of an amyloidogenic peptide that has two internal states: the first one representing the soluble random coil structure and the second one the -sheet conformation. We find that this system exhibits a propensity towards fibrillar self-assembly following the formation of a critical nucleus. Our calculations establish connections between the early nucleation events and the kinetic information available in the later stages of the aggregation process that are commonly probed in experiments. We analyze the kinetic behaviour in our simulations within the framework of the theory of classical nucleated polymerisation, and are able to connect the structural events at the early stages in amyloid growth with the resulting macroscopic observables such as the effective nucleus size. Furthermore, the free-energy landscapes that emerge from these simulations allow us to identify pertinent properties of the monomeric state that could be targeted to suppress oligomer formation.

A number of normally soluble proteins can form amyloid structures in a process associated with neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Mature amyloid structures consist of large fibrils containing thousands of individual proteins aggregated into linear nanostructures; there is increasing evidence, however, that the toxic species responsible for neurodegeneration are not the mature fibrils themselves but rather lower molecular weight precursors commonly known as amyloid oligomers. Unfortunately, these early oligomers are commonly thermodynamically unstable and of nanometer scale dimensions, factors which make them highly challenging to probe in detail in experiments. We have used computer simulations of a model inspired by Alzheimer's Abeta peptide to investigate the early stages of protein aggregation. The results that we obtain were shown to fit Oosawa's polymerization theory, a finding which allows us to provide a connection between the microscopic molecular parameters and macroscopic growth. One crucial parameter is size of the nucleus, i.e. the basic oligomer existing at origin of the formation of each fiber. We have revealed a path for the formation of this nucleus and validate its size by several methods. Our results provide fundamental information for influencing the early stages of amyloid formation in a rational manner.

Dimer formation enhances structural differences between amyloid β-protein (1-40) and (1-42): an explicit-solvent molecular dynamics study

Amyloid β-protein (Aβ) is central to the pathology of Alzheimer's disease. A 5% difference in the primary structure of the two predominant alloforms, Aβ(1-40) and Aβ(1-42), results in distinct assembly pathways and toxicity properties. Discrete molecular dynamics (DMD) studies of Aβ(1-40) and Aβ(1-42) assembly resulted in alloform-specific oligomer size distributions consistent with experimental findings. Here, a large ensemble of DMD-derived Aβ(1-40) and Aβ(1-42) monomers and dimers was subjected to fully atomistic molecular dynamics (MD) simulations using the OPLS-AA force field combined with two water models, SPCE and TIP3P. The resulting all-atom conformations were slightly larger, less compact, had similar turn and lower β-strand propensities than those predicted by DMD. Fully atomistic Aβ(1-40) and Aβ(1-42) monomers populated qualitatively similar free energy landscapes. In contrast, the free energy landscape of Aβ(1-42) dimers indicated a larger conformational variability in comparison to that of Aβ(1-40) dimers. Aβ(1-42) dimers were characterized by an increased flexibility in the N-terminal region D1-R5 and a larger solvent exposure of charged amino acids relative to Aβ(1-40) dimers. Of the three positively charged amino acids, R5 was the most and K16 the least involved in salt bridge formation. This result was independent of the water model, alloform, and assembly state. Overall, salt bridge propensities increased upon dimer formation. An exception was the salt bridge propensity of K28, which decreased upon formation of Aβ(1-42) dimers and was significantly lower than in Aβ(1-40) dimers. The potential relevance of the three positively charged amino acids in mediating the Aβ oligomer toxicity is discussed in the light of available experimental data.

Solid-support electron paramagnetic resonance (EPR) studies of Aβ40 monomers reveal a structured state with three ordered segments

Alzheimer disease is associated with the pathological accumulation of amyloid-β peptide (Aβ) in the brain. Soluble Aβ oligomers formed during early aggregation process are believed to be neurotoxins and causative agents in Alzheimer disease. Aβ monomer is the building block for amyloid assemblies. A comprehensive understanding of the structural features of Aβ monomer is crucial for delineating the mechanism of Aβ oligomerization. Here we investigated the structures of Aβ40 monomer using a solid-support approach, in which Aβ40 monomers are tethered on the solid support via an N-terminal His tag to prevent further aggregation. EPR spectra of tethered Aβ40 with spin labels at 18 different positions show that Aβ40 monomers adopt a completely disordered structure under denaturing conditions. Under native conditions, however, EPR spectra suggest that Aβ40 monomers adopt both a disordered state and a structured state. The structured state of Aβ40 monomer has three more ordered segments at 14-18, 29-30, and 38-40. Interactions between these segments may stabilize the structured state, which likely plays an important role in Aβ aggregation.

Molecular insight into conformational transition of amyloid β-peptide 42 inhibited by (-)-epigallocatechin-3-gallate probed by molecular simulations

Considerable experimental evidence indicates that (-)-epigallocatechin-3-gallate (EGCG) inhibits the fibrillogenesis of Aβ(42) and alleviates its associated cytotoxicity. However, the molecular mechanism of the inhibition effect of EGCG on the conformational transition of Aβ(42) remains unclear due to the limitations of current experimental techniques. In this work, molecular dynamics simulations and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) analysis were coupled to better understand the issue. It was found that the direct interactions between EGCG and the peptide are the origin of its inhibition effects. Specifically, EGCG molecules expel water from the surface of the Aβ(42), cluster with each other, and interact directly with the peptide. The results of free energy decomposition calculated by MM-PBSA indicate that the nonpolar term contributes more than 71% to the binding free energy of the EGCG-Aβ(42) complex, while polar interactions (i.e., hydrogen bonding) play a minor role. It was identified that there are 12 important residues of Aβ(42) that strongly interact with EGCG (Phe4, Arg5, Phe19, Phe20, Glu22, Lys28, Gly29, Leu34-Gly37, and Ile41), while nonpolar interactions are mainly provided by the side chains of some hydrophobic residues (Phe, Met and Ile) and the main chains of some nonhydrophobic residues (Lys28 and Gly29). On the contrary, polar interactions are mainly formed by the main chain of Aβ(42), of which the main chains of Gly29 and Gly37 contribute greatly. The work has thus elucidated the molecular mechanism of the inhibition effect of EGCG on the conformational transition of Aβ(42), and the findings are considered critical for exploring more effective agents for the inhibition of Aβ(42) fibrillogenesis.