Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential

Zhang, Zhuqing; Chen, Hao; Lai, Luhua

doi:10.1093/bioinformatics/btm325

Cited by 109 publications

(115 citation statements)

References 56 publications

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“…The same energy function used for simulated annealing allows us to rapidly scan any sequence for fragments that are likely to participate in such amyloid-like structure formation. The results of this scan are consistent with other bioinformatic tools for identifying amyloidogenic sequences (11,(20)(21)(22)(23)(24).…”

supporting

confidence: 84%

Frustration in the energy landscapes of multidomain protein misfolding

Zheng

Schafer

Wolynes

2013

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

112

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Frustration from strong interdomain interactions can make misfolding a more severe problem in multidomain proteins than in singledomain proteins. On the basis of bioinformatic surveys, it has been suggested that lowering the sequence identity between neighboring domains is one of nature's solutions to the multidomain misfolding problem. We investigate folding of multidomain proteins using the associative-memory, water-mediated, structure and energy model (AWSEM), a predictive coarse-grained protein force field. We find that reducing sequence identity not only decreases the formation of domain-swapped contacts but also decreases the formation of strong self-recognition contacts between β-strands with high hydrophobic content. The ensembles of misfolded structures that result from forming these amyloid-like interactions are energetically disfavored compared with the native state, but entropically favored. Therefore, these ensembles are more stable than the native ensemble under denaturing conditions, such as high temperature. Domainswapped contacts compete with self-recognition contacts in forming various trapped states, and point mutations can shift the balance between the two types of interaction. We predict that multidomain proteins that lack these specific strong interdomain interactions should fold reliably.aggregation | funnel P rotein misfolding and productive protein folding bear a yinyang relationship in the energy landscape theory of biomolecular self-organization (1). Only by comparing the strengths of the forces leading to proper structure to those that might, by chance, stabilize alternative structure can we quantitatively understand how proteins kinetically access their thermodynamically stable ordered states (1). In vivo and at low concentrations in vitro, unfolded small proteins avoid kinetic traps and generally find their way easily to their native state. Nevertheless, diseases caused by the misfolding of several specific proteins plague mankind (2, 3). Despite much effort, the patterns of interactions that allow pathological misfolding remain incompletely understood. Known pathological misfolding entails aggregation of specific proteins and thus the interactions of protein molecules with other copies of themselves. Energy landscape theory provides one natural explanation of this specificity in misfolding through the funneled nature of the monomeric protein energy landscape: Native-like interactions between different protein molecules like those found within a single protein are stronger than alternate nonnative interactions in the same molecule or interactions between peptide sequences chosen at random in the two molecules. Because of this intrinsic self-stickiness of foldable molecules, runaway domain swapping, in which native-like interactions are made between different copies of the same protein, provides a natural mechanism for aggregation (4-7). Indeed, transient protein aggregation during refolding at moderately high concentration does appear to be universal (8). Nevertheless this aggregati...

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supporting

confidence: 84%

Frustration in the energy landscapes of multidomain protein misfolding

Zheng

Schafer

Wolynes

2013

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

112

View full text Add to dashboard Cite

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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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“…These include both sequence-based approaches (Fernandez-Escamilla et al 2004;Trovato et al 2006;Tartaglia et al 2008;Bryan et al 2009;MaurerStroh et al 2010;O'Donnell et al 2011) and structurebased approaches (Thompson et al 2006;Zhang et al 2007;Goldschmidt et al 2010), the latter of which are designed to identify short amyloidogenic motifs based on their steric zipper-forming potential (Sawaya et al 2007). In particular, the Eisenberg group (Goldschmidt et al 2010) has used three-dimensional profiling to evaluate short (six-residue) protein segments to identify high fibrillation propensity (HP) segments.…”

Section: A Cell-based Methods For Evaluating Amyloidogenicitymentioning

confidence: 99%

Generating extracellular amyloid aggregates using E. coli cells

Sivanathan

Hochschild

2012

Genes Dev.

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Diverse proteins are known to be capable of forming amyloid aggregates, self-seeding fibrillar assemblies that may be biologically functional or pathological. Well-known examples include neurodegenerative disease-associated proteins that misfold as amyloid, fungal prion proteins that can transition to a self-propagating amyloid form and certain bacterial proteins that fold as amyloid at the cell surface and promote biofilm formation. To further explore the diversity of amyloidogenic proteins, generally applicable methods for identifying them are critical. Here we describe a cell-based method for generating amyloid aggregates that relies on the natural ability of Escherichia coli cells to elaborate amyloid fibrils at the cell surface. We use several different yeast prion proteins and the human huntingtin protein to show that protein secretion via this specialized export pathway promotes acquisition of the amyloid fold specifically for proteins that have an inherent amyloid-forming propensity. Furthermore, our findings establish the potential of this E. coli-based system to facilitate the implementation of high-throughput screens for identifying amyloidogenic proteins and modulators of amyloid aggregation.

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Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 109 publications

References 56 publications

Frustration in the energy landscapes of multidomain protein misfolding

Frustration in the energy landscapes of multidomain protein misfolding

Amyloid-based nanosensors and nanodevices

Generating extracellular amyloid aggregates using E. coli cells

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