The amyloid stretch hypothesis: Recruiting proteins toward the dark side

Esteras-Chopo, Alexandra; Serrano, Luís; Paz, Manuela López de la

doi:10.1073/pnas.0505905102

Cited by 128 publications

(118 citation statements)

References 40 publications

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“…These algorithms are designed to identify the short, highly amyloid-prone segments thought to characterize most amyloid domains (16), not the large disordered segments with modest prion propensity that characterize yeast PFDs.…”

Section: Discussionmentioning

confidence: 99%

Compositional Determinants of Prion Formation in Yeast

Toombs

McCarty

Ross

2010

Molecular and Cellular Biology

148

256

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Numerous prions (infectious proteins) have been identified in yeast that result from the conversion of soluble proteins into ␤-sheet-rich amyloid-like protein aggregates. Yeast prion formation is driven primarily by amino acid composition. However, yeast prion domains are generally lacking in the bulky hydrophobic residues most strongly associated with amyloid formation and are instead enriched in glutamines and asparagines. Glutamine/asparagine-rich domains are thought to be involved in both disease-related and beneficial amyloid formation. These domains are overrepresented in eukaryotic genomes, but predictive methods have not yet been developed to efficiently distinguish between prion and nonprion glutamine/asparagine-rich domains. We have developed a novel in vivo assay to quantitatively assess how composition affects prion formation. Using our results, we have defined the compositional features that promote prion formation, allowing us to accurately distinguish between glutamine/asparagine-rich domains that can form prion-like aggregates and those that cannot. Additionally, our results explain why traditional amyloid prediction algorithms fail to accurately predict amyloid formation by the glutamine/asparagine-rich yeast prion domains.

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Section: Discussionmentioning

confidence: 99%

Compositional Determinants of Prion Formation in Yeast

Toombs

McCarty

Ross

2010

Molecular and Cellular Biology

148

256

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“…We, therefore, see a variety of peptides and proteins that are not related in sequence, structure, and function that all assemble into highly regular amyloid fibrils. Nevertheless, in solution, sequences containing amyloidogenic regions are needed to initiate the process of aggregation (26)(27)(28), and these local sequence patterns are selected against. Most evolved proteins contain only a few such segments.…”

Section: Mutational Effects On the Aggregation Free Energymentioning

confidence: 99%

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

Zheng

Tsai

Chen

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

131

193

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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...

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“…Increasing evidence suggests that limited portions of the sequences of amyloidogenic proteins play a key role in the aggregation process and take part in the formation of the b-core of the resulting fibrils (Ventura et al 2004;Esteras-Chopo et al 2005;Bemporad et al 2006). We investigate this issue here by comparing the aggregation propensities of asyn and bsyn, in the presence of SDS.…”

mentioning

confidence: 99%

Molecular determinants of the aggregation behavior of α‐ and β‐synuclein

et al. 2008

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a-and b-synuclein are closely related proteins, the first of which is associated with deposits formed in neurodegenerative conditions such as Parkinson's disease while the second appears to have no relationship to any such disorders. The aggregation behavior of a-and b-synuclein as well as a series of chimeric variants were compared by exploring the structural transitions that occur in the presence of a widely used lipid mimetic, sodium dodecyl sulfate (SDS). We found that the aggregation rates of all these protein variants are significantly enhanced by low concentrations of SDS. In particular, we inserted the 11-residue sequence of mainly hydrophobic residues from the non-amyloid-b-component (NAC) region of a-synuclein into b-synuclein and show that the fibril formation rate of this chimeric protein is only weakly altered from that of b-synuclein. These intrinsic propensities to aggregate are rationalized to a very high degree of accuracy by analysis of the sequences in terms of their associated physicochemical properties. The results begin to reveal that the differences in behavior are primarily associated with a delicate balance between the positions of a range of charged and hydrophobic residues rather than the commonly assumed presence or absence of the highly aggregation-prone region of the NAC region of a-synuclein. This conclusion provides new insights into the role of a-synuclein in disease and into the factors that regulate the balance between solubility and aggregation of a natively unfolded protein.

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The amyloid stretch hypothesis: Recruiting proteins toward the dark side

Cited by 128 publications

References 40 publications

Compositional Determinants of Prion Formation in Yeast

Compositional Determinants of Prion Formation in Yeast

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

Molecular determinants of the aggregation behavior of α‐ and β‐synuclein

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