Exploring the aggregation free energy landscape of the amyloid-β protein (1–40)

Abstract

A predictive coarse-grained protein force field [associative memory, water-mediated, structure, and energy model for molecular dynamics (AWSEM)-MD] is used to study the energy landscapes and relative stabilities of amyloid-β protein in the monomer and all of its oligomeric forms up to an octamer. We find that an isolated monomer is mainly disordered with a short α-helix formed at the central hydrophobic core region (L17-D23). A less stable hairpin structure, however, becomes increasingly more stable in oligomers, where hydrogen bonds can form between neighboring monomers. We explore the structure and stability of both prefibrillar oligomers that consist of mainly antiparallel β-sheets and fibrillar oligomers with only parallel β-sheets. Prefibrillar oligomers are polymorphic but typically take on a cylindrin-like shape composed of mostly antiparallel β-strands. At the concentration of the simulation, the aggregation free energy landscape is nearly downhill. We use umbrella sampling along a structural progress coordinate for interconversion between prefibrillar and fibrillar forms to identify a conversion pathway between these forms. The fibrillar oligomer only becomes favored over its prefibrillar counterpart in the pentamer where an interconversion bottleneck appears. The structural characterization of the pathway along with statistical mechanical perturbation theory allow us to evaluate the effects of concentration on the free energy landscape of aggregation as well as the effects of the Dutch and Arctic mutations associated with early onset of Alzheimer's disease.misfolding | amyloid funnel | nucleation A lzheimer's disease is associated with the deposition of amyloid-β (Aβ) protein aggregates in the brain (1). Soluble Aβ oligomers, intermediates formed early in the aggregation process, can cause synaptic dysfunction, whereas the later-formed insoluble fibrils may function as reservoirs of the toxic oligomers (2). Owing to their stoichiometric complexity and transience, the early oligomeric forms are difficult to study in the laboratory. Nevertheless, distinct forms of oligomers, described as prefibrillar and fibrillar, have been found to bind differently to conformation-dependent antibodies (3): the fibrillar oligomers and mature fibrils both display a common epitope that is absent from the prefibrillar oligomers. The study of the secondary structure of Aβ species using Fourier transform infrared spectroscopy suggests that fibrillar forms of Aβ are organized in a parallel β-sheet conformation, much like in the complete fibril structure constructed from solid-state NMR data by Petkova et al. (4), whereas the prefibrillar oligomers contain mainly antiparallel β-sheets (5). Numerous computer simulation studies of both the monomer and higher aggregates using models ranging in complexity from fully atomistic simulations in solvent to lattice models have been undertaken to fill the knowledge gap (6-8). It remains, however, unclear what the exact tertiary arrangements of the β-sheets in the Aβ prefibrillar oligome...

Distinct oligomerization and fibrillization dynamics of amyloid core sequences of amyloid-beta and islet amyloid polypeptide

“…Computational studies of full length peptides or their aggregation-prone amyloidogenic fragments are often limited to the observation of early events of misfolding and self-association corresponding to the pre-nucleation step 17 or the energetics of fibrillar oligomers with different sizes corresponding to post-nucleation step, i.e., the fibril elongation 18 . Using umbrella sampling simulations with the fibril structure as the template, the free energy landscape of amyloid aggregation can be inferred 19–21 . However, a direct observation of the nucleation process from pre-fibrillar oligomers to protofibrils in atomistic simulations is still lacking.…”

Aggregation landscapes of Huntingtin exon 1 protein fragments and the critical repeat length for the onset of Huntington’s disease

“…To overcome these problems, we use coarse-grained simulations of the aggregation of HTT exon 1-encoded fragments that use the AWSEM force field. While being efficient to simulate, the AWSEM force field can predict the structures of protein monomers (22-24) and predict details of assembly into oligomers (9,(25)(26)(27)(28). We have already used the model to explore the detailed mechanisms of aggregation of pure polyQ peptides (9) and the peptide implicated in Alzheimer's disease (28), Aβ40.…”

The AWSEM-Amylometer: predicting amyloid propensity and fibril topology using an optimized folding landscape model

“…36 In the case of Aβ fibers, the AWSEM-Amylometer suggests that Aβ can adopt there is a profound change in amyloidogenicity even from point mutations using only the N N QQN Y topology. 28 Our results show that these point mutations can generate similar changes in an antiparallel topology ( Figure 4B): increased hydrophobicity at site 22 elevates the amyloidogenicity of the hexapeptides that contain this site, and E22V is more amyloidogenic compared to E22G and E22Q in both parallel topology and antiparallel topology.…”

“…20 AWSEM has been fruitfully applied in recent years to many different problems of protein structure prediction, [20][21][22] protein association, 23 allosteric mechanism 24 and protein aggregation. [25][26][27][28][29] The AWSEM-Amylometer is based on the same energy model that is used in AWSEM molecular dynamics simulations but is able to detect amyloidogenic segments using a simple and efficient threading scheme over multiple fiber template structures. This scheme not only allows for the detection of amyloidogenic segments but also the prediction of the relative orientation of the amyloid β-strands in the fibril core.…”

“…29 Simulations using the AWSEM force field not only capture the local signatures seen from the AWSEM-Amylometer calculations (i.e. that Aβ 40 could have both parallel and antiparallel structures in simulations 28 ), but also allow one to find the most favorable structure that lies in the "amyloid funnel". 6: The AWSEM-Amylometer can be used to predict the propensity for forming α-helical amyloid fibers.…”