Protein aggregate turbidity: Simulation of turbidity profiles for mixed-aggregation reactions

Hall, Damien; Zhao, Ran; Dehlsen, Ian; Bloomfield, Nathaniel J.; Williams, Steven R.; Arisaka, Fumio; Goto, Yuji; Carver, John A.

doi:10.1016/j.ab.2015.11.021

Cited by 56 publications

(58 citation statements)

References 51 publications

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“…3b) and υ refers to protein partial specific volume8 (Lee et al 2009). As noted (Hall et al 2016a), defining α I in the manner outlined by Eq. Eq.…”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…8 of Hall 2012). d Average angle of deviation (θ av ) for an individual fibre as determined by Hall et al (2016a) using successive calculation of the dot product between projection vectors that trace along the backbone of the amyloid fibre (adapted, with permission, from Fig. 3a of Hall 2012).…”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…An extension to this geometrical description, useful for modelling aggregates in solution (Hall et al 2016a), involves representing aggregate geometry in terms of similarly limited shape possibilities, along with an extra variable relating to the internal volume packing fraction. Using this approach, amorphous, crystalline and fibrous protein aggregates can all be represented (Fig.…”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…a greater internal density (schematic is adapted, with permission, from Fig. 1 of Hall et al 2016a, b) …”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…As such, our review differs from many others on the topic of amyloid biophysics (Hall and Edskes 2012; Kashchiev 2015; Ma and Nussinov 2006; Mezzenga and Fischer 2013; Sasahara and Goto 2013; So et al 2016; Tycko and Wickner 2013) by its restriction to matters directly related to achieving an understanding of the turbidimetric method. Towards this goal our examination will pay particular attention to recent articles concerned with ultra-microscope image analysis (Hall 2012; Usov and Mezzenga 2015), light scattering and turbidity development by protein aggregates (Garcia-Lopez and Garcia-Rubio 2008; Garcia-Lopez et al 2006; Hall et al 2016a) and simulation of the kinetics of amyloid (Ghosh et al 2010; Hall et al 2016b; Kaschiev 2015) and other aggregate types (Adachi et al 2015; Hall et al 2015). Although placing the focus of the review on a single type of assay procedure may seem like a retreat from the bigger questions, such as the relation between amyloid and disease (Hall and Edskes 2012; Walker and Jucker 2015), we contend that a thorough understanding of principles associated with the turbidimetric monitoring of amyloid growth will sharpen our collective ability to make informed judgements about the biological implications of results gained from in vitro protein aggregation assays.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of amyloid formation by turbidity assay—seeing through the cloud

Zhao

Maat

et al. 2016

Biophys Rev

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Detection of amyloid growth is commonly carried out by measurement of solution turbidity, a low-cost assay procedure based on the intrinsic light scattering properties of the protein aggregate. Here, we review the biophysical chemistry associated with the turbidimetric assay methodology, exploring the reviewed literature using a series of pedagogical kinetic simulations. In turn, these simulations are used to interrogate the literature concerned with in vitro drug screening and the assessment of amyloid aggregation mechanisms.

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“…3b) and υ refers to protein partial specific volume8 (Lee et al 2009). As noted (Hall et al 2016a), defining α I in the manner outlined by Eq. Eq.…”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

“…a greater internal density (schematic is adapted, with permission, from Fig. 1 of Hall et al 2016a, b) …”

Section: (I) Consensus Physical Models Of Protein Aggregatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Measurement of amyloid formation by turbidity assay—seeing through the cloud

Zhao

Maat

et al. 2016

Biophys Rev

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On the nature of the optimal form of the holdase‐type chaperone stress response

Hall

2019

FEBS Letters

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The holdase paradigm of chaperone action involves preferential binding by the chaperone to the unfolded protein state, thereby preventing it from either, associating with other unstable proteins (to form large dysfunctional aggregates), or being degraded by the proteolytic machinery of the cell/organism. In this paper, we examine the necessary physical constraints imposed upon the holdase chaperone response in a cell-like environment and use these limitations to comment on the likely nature of the optimal form of chaperone response in vivo.

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Effect of ferric citrate on amyloid‐beta peptides behavior

et al. 2018

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Amyloid beta (Aβ) aggregation and oxidative stress are two of the central events in Alzheimer's Disease (AD). Both these phenomena can be caused by the interaction of Aβ with metal ions. In the last years the interaction between Zn , Cu , and Aβ was much studied, but between iron and Aβ it is still little known. In this work we determine how three Aβ peptides, present in AD, interact with Fe -citrate. The three Aβ peptides are: full length Aβ1-42, an isoform truncated at Glutamic acid in position three, Aβ3-42, and its pyroglutamated form AβpE3-42. Conformation and morphology of the three peptides, aggregated with and without Fe -citrate were studied. Besides, we have determined the strength of the interactions Aβ/Fe -citrate studying the effect of ethylenediaminetetraacetic acid as chelator. Results reported here demonstrate that Fe -citrate promotes the aggregation in all the three peptides. Moreover, Aspartic acid 1, Glutamic acid 3, and Tyrosine 10 have an important role in the coordination with iron, generating a more stable complex for Aβ1-42 compared to that for the truncated peptides.

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Protein aggregate turbidity: Simulation of turbidity profiles for mixed-aggregation reactions

Cited by 56 publications

References 51 publications

Measurement of amyloid formation by turbidity assay—seeing through the cloud

Measurement of amyloid formation by turbidity assay—seeing through the cloud

On the nature of the optimal form of the holdase‐type chaperone stress response

Effect of ferric citrate on amyloid‐beta peptides behavior

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