A simple algorithm locates β‐strands in the amyloid fibril core of α‐synuclein, Aβ, and tau using the amino acid sequence alone

Zibaee, Shahin; Makin, O. Sumner; Goedert, Michel; Serpell, Louise C.

doi:10.1110/ps.062624507

Cited by 108 publications

(105 citation statements)

References 109 publications

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“…The approaches range from special physico-chemical property windows to position-specific sequence matrices, explicit structural models, and combinations thereof. 108,116,[172][173][174][175][176][177][178][179][180][181][182][183][184][185] Many methods base their prediction on the potential of hexapeptides to form amyloid fibres. This characteristic length has been found to cover several of the core motifs for amyloid fiber formation.…”

Section: Predicting Amyloid Propensity and Peptide Designmentioning

confidence: 99%

Amyloid-based nanosensors and nanodevices

2014

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Self-assembling amyloid-like peptides and proteins give rise to promising biomaterials with potential applications in many fields. Amyloid structures are formed by the process of molecular recognition and self-assembly, wherein a peptide or protein monomer spontaneously self-associates into dimers and oligomers and subsequently into supramolecular aggregates, finally resulting in condensed fibrils. Mature amyloid fibrils possess a quasi-crystalline structure featuring a characteristic fiber diffraction pattern and have well-defined properties, in contrast to many amorphous protein aggregates that arise when proteins misfold. Core sequences of four to seven amino acids have been identified within natural amyloid proteins. They are capable to form amyloid fibers and fibrils and have been used as amyloid model structures, simplifying the investigations on amyloid structures due to their small size. Recent studies have highlighted the use of self-assembled amyloid-based fibers as nanomaterials. Here, we discuss the latest advances and the major challenges in developing amyloids for future applications in nanotechnology and nanomedicine, with the focus on development of sensors to study protein-ligand interactions.

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Section: Predicting Amyloid Propensity and Peptide Designmentioning

confidence: 99%

Amyloid-based nanosensors and nanodevices

2014

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“…Calculation of Fibrillogenic Properties-The M␤P, MHL, MNC, and MTC were calculated in the same way as in our previous papers (33,34), using data from (Refs. 41 and 42).…”

Section: Expression and Purification Of Wild-type And Mutantmentioning

confidence: 99%

“…SALSA ␤-SC plots were produced as described (34). The integrals of the SALSA ␤-SC (͐␤-SC) plots measure the area covered by all peaks from the whole sequence.…”

Section: Expression and Purification Of Wild-type And Mutantmentioning

confidence: 99%

“…Subsequently, we performed a systematic analysis of sequence determinants for ␣-syn assembly using deletion mutants, amino-terminally truncated fragments, ␣-syn/␤-syn chimeras, and point mutants (33). This revealed that the fibrillogenic propensity of ␣-syn is determined by both amino acid composition (a "global" property) and ␤-strand contiguity (a "local" property) (33,34).…”

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Human β-Synuclein Rendered Fibrillogenic by Designed Mutations

Zibaee

Fraser

Jakes

et al. 2010

Journal of Biological Chemistry

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Filamentous inclusions made of ␣-synuclein are found in nerve cells and glial cells in a number of human neurodegenerative diseases, including Parkinson disease, dementia with Lewy bodies, and multiple system atrophy. The assembly and spreading of these inclusions are likely to play an important role in the etiology of common dementias and movement disorders. Both ␣-synuclein and the homologous ␤-synuclein are abundantly expressed in the central nervous system; however, ␤-synuclein is not present in the pathological inclusions. Previously, we observed a poor correlation between filament formation and the presence of residues 73-83 of ␣-synuclein, which are absent in ␤-synuclein. Instead, filament formation correlated with the mean ␤-sheet propensity, charge, and hydrophilicity of the protein (global physicochemical properties) and ␤-strand contiguity calculated by a simple algorithm of sliding averages (local physicochemical property). In the present study, we rendered ␤-synuclein fibrillogenic via one set of point mutations engineered to enhance global properties and a second set engineered to enhance predominantly ␤-strand contiguity. Our findings show that the intrinsic physicochemical properties of synucleins influence their fibrillogenic propensity via two distinct but overlapping modalities. The implications for filament formation and the pathogenesis of neurodegenerative diseases are discussed.

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“…A variety of algorithms have been developed to predict a peptide's propensity to form amyloid fibrils based on its amino acid sequence, including BETASCAN (6), TANGO (17), Zyggregator (51), SALSA (62), and PASTA (55). These algorithms have been successful at identifying regions prone to amyloid aggregation and predicting the effects of mutations on aggregation propensity for many amyloid-forming proteins.…”

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confidence: 99%

Compositional Determinants of Prion Formation in Yeast

Toombs

McCarty

Ross

2010

Molecular and Cellular Biology

147

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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A simple algorithm locates β‐strands in the amyloid fibril core of α‐synuclein, Aβ, and tau using the amino acid sequence alone

Cited by 108 publications

References 109 publications

Amyloid-based nanosensors and nanodevices

Amyloid-based nanosensors and nanodevices

Human β-Synuclein Rendered Fibrillogenic by Designed Mutations

Compositional Determinants of Prion Formation in Yeast

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