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Record W2272768955

Coarse-Grained Molecular Dynamics Simulations of Peptide Aggregation on Surfaces

2014· article· en· W2272768955 on OpenAlexfundno aff
Herbert Alexander Morriss-Andrews

Bibliographic record

VenueeScholarship (California Digital Library) · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsnot available
FundersLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of Canada
KeywordsFibrilPeptideChemistryCrystallographyMolecular dynamicsBiophysicsChemical physicsProtein aggregationComputational chemistryBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

Protein aggregation involves self-assembly of normally soluble proteins or peptides into supramolecular structures. This process is particularly important due to its involvement in several amyloid diseases, such as Parkinson's, Alzheimer's, and Type II diabetes. Several fibrillization mechanisms have been proposed, including a condensation-ordering mechanism where ordered fibril structures emerge from disordered oligomers and a dock-lock mechanism where a growing fibril induces attached polypeptides to organize individually into fibril-compatible conformations.We present a series of computational studies using a coarse-grained peptide aggregate model that exhibits a rich diversity of structures: amorphous/disordered aggregates, beta-barrels, multi-layered fibrils, and aggregates of mixed type. Our model has a tunable backbone stiffness that governs the propensity to form fibrils in bulk solution. In this work, we investigate how this beta-sheet propensity couples with the properties of a surface template to influence the mechanism of aggregation. Here, we focus on peptide aggregation in the presence of three templates: a solid surface, the surface of a pre-existing aggregate seed, and a lipid bilayer. Aggregation on solid hydrophilic or hydrophobic surfaces frequently occurs in many experimental setups. We find that the solid surface strongly biases toward the formation of fibrillar aggregates. Peptide-peptide interactions and surface attraction couple cooperatively on a solid surface to influence the binding/aggregation transition. Aggregation and binding occur almost simultaneously since the surface's crystal symmetry enforces a preferred direction of bound fibril growth, thus accelerating the process.Seeding peptides with compatible aggregates removes the nucleation barrier for aggregation. We find that the aggregation mechanism is strongly dependent on the beta-sheet propensity of both the seed and bulk peptides. Additionally, bulk peptides that exhibit polymorphism can have multiple pathways to aggregation depending on which class of aggregate they initially form. We find that a fibrillar seed can induce amorphous-prone peptides into fibrillar structures via a condensation-ordering mechanism, thus sequestering potentially cytotoxic oligomers into a more inert form.We simulate aggregation on lipid bilayers in an effort to approximate the complexity of the cellular milieu. While aggregation in vivo would occur in the presence of membrane surfaces, few simulation studies have been conducted on this combined system due to its computational complexity. We have determined that a membrane surface, like a crystal surface, biases toward fibrillar aggregates. However, membrane undulations disturb multi-layered fibrils into non-planar beta-sheet structures, such as beta-barrels. The presence of fibrils on the membrane also affects its fluid properties, creating a hexagonally packed lipid ordering underneath the fibrils, locally increasing its bending modulus and aligning lipid tilt to the orientation of the peptides. Thus peptide aggregation and membrane fluidity affect each other's structure and dynamics.The key general features of a surface that control its modulation of peptide aggregation are its structural order and fluidity. An ordered, rigid template biases more strongly toward fibrillar structures and restricts the set of aggregation pathways and morphologies. The dynamic nature of a fluid surface biases less toward fibrils and enhances the range of aggregation dynamics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.221
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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