Dissecting the role of glutamine in seeding peptide aggregation

Barrera, Exequiel; Zonta, Francesco; Pantano, Sergio

doi:10.1016/j.csbj.2021.02.014

Cited by 20 publications

(17 citation statements)

References 57 publications

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“…Each side chain was modeled specifically based on a combination of physicochemical characteristics. The SIRAH FF is a relatively new force field that was recently used to study the process of seeding peptide aggregation [23] . SIRAH was chosen as an alternative to MARTINI since it has been shown that the MARTINI FF overestimates protein-protein interaction (PPI) for membrane proteins [24] .…”

Section: Introductionmentioning

confidence: 99%

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

Ko¹,

Berner²,

Kulakova³

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

Ko¹,

Berner²,

Kulakova³

et al. 2022

Computational and Structural Biotechnology Journal

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“…A detailed description of the protocol followed to generate the primary data is reported in the associated paper [1] . Briefly, for each system we started from fully atomistic peptide copies that were uniformly distributed in simulation boxes listed in Table 1 .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The dataset also comprises MD trajectories of the gliadin related p31-43 peptide, and Insulin's C-peptide at pH=7 and pH=3.2, which constitute examples of Q-rich and Q-poor aggregating peptides. The dataset grants molecular insights on the role of glutamines in spontaneous and unbiased ab-initio aggregation of a series of peptides using a homogeneous set of simulations [1] . The trajectory files are provided in Protein Data Bank (PDB) format containing the Cartesian coordinates of all heavy atoms in the aggregating peptides.…”

mentioning

confidence: 99%

A homogeneous dataset of polyglutamine and glutamine rich aggregating peptides simulations

2021

Self Cite

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This dataset contains a collection of molecular dynamics (MD) simulations of polyglutamine (polyQ) and glutamine-rich (Q-rich) peptides in the multi-microsecond timescale. Primary data from coarse-grained simulations performed using the SIRAH force field has been processed to provide fully atomistic coordinates. The dataset encloses MD trajectories of polyQs of 4 (Q4), 11 (Q11), and 36 (Q36) amino acids long. In the case of Q11, simulations in presence of Q5 and QEQQQ peptides, which modulate aggregation, are also included. The dataset also comprises MD trajectories of the gliadin related p31-43 peptide, and Insulin's C-peptide at pH=7 and pH=3.2, which constitute examples of Q-rich and Q-poor aggregating peptides. The dataset grants molecular insights on the role of glutamines in spontaneous and unbiased ab-initio aggregation of a series of peptides using a homogeneous set of simulations [1] . The trajectory files are provided in Protein Data Bank (PDB) format containing the Cartesian coordinates of all heavy atoms in the aggregating peptides. Further analyses of the trajectories can be performed directly using any molecular visualization/analysis software suites.

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“…At the highest one, oligomers interact with each other, forming large linear arrangements (Herrera et al 2020). Barrera et al have recently dissected the role of glutamines in the self-assembly of this peptide, showing that this process could be divided into three phases that ended in the formation of a 50-mer aggregate with an increase of the β-extended structure (Barrera et al 2021).…”

Section: The P31-43 Peptide Has a Polyproline Structure And Self-assembles Into Oligomers Inducing Nlrp3 Inflammasome Activation In A Moumentioning

confidence: 99%

Gliadin proteolytical resistant peptides: the interplay between structure and self-assembly in gluten-related disorders

Herrera

Dodero

2021

Biophys Rev

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In recent years, the evaluation of the structural properties of food has become of crucial importance in the understanding of food-related disorders. One of the most exciting systems is gliadin, a protein in wheat gluten, that plays a protagonist role in gluten-related disorders with a worldwide prevalence of 5%, including autoimmune celiac disease (CeD) (1%) and non-celiac wheat sensitivity (0.5–13%). It is accepted that gliadin is not fully digested by humans, producing large peptides that reach the gut mucosa. The gliadin peptides cross the lamina propria eliciting different immune responses in susceptible patients. Many clinical and biomedical efforts aim to diagnose and understand gluten-related disorders; meanwhile, the early stages of the inflammatory events remain elusive. Interestingly, although the primary sequence of many gliadin peptides is well known, it was only recently revealed the self-assembly capability of two pathogenic gliadin fragments and their connection to the early stage of diseases. This review is dedicated to the most relevant biophysical characterization of the complex gliadin digest and the two most studied gliadin fragments, the immunodominant 33-mer peptide and the toxic p31-43 in connection with inflammation and innate immune response. Here, we want to emphasize that combining different biophysical methods with cellular and in vivo models is of key importance to get an integrative understanding of a complex biological problem, as discussed here.

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Dissecting the role of glutamine in seeding peptide aggregation

Abstract: Graphical abstract

Cited by 20 publications

References 57 publications

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations

A homogeneous dataset of polyglutamine and glutamine rich aggregating peptides simulations

Gliadin proteolytical resistant peptides: the interplay between structure and self-assembly in gluten-related disorders

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