Evidence map›Paper›PMID 33919640›Full record

ArticleBiomolecules2021

Effect of Surface Roughness on Aggregation of Polypeptide Chains: A Monte Carlo Study.

Nguyen Truong Co, Mai Suan Li

Abstract read
In one paragraph

Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Amyloidogenesis: What Do We Know So Far?International journal of molecular sciences · 2022
    Review
  4. Article
  5. Changes in Myosin from Silver Carp (Foods (Basel, Switzerland) · 2022
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nguyen Truong CoInstitute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland.ORCID 0000-0001-5642-3641
Mai Suan LiInstitute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland.

Funding

Department of Science and Technology, Ho Chi Minh city, Vietnam 02/2018/Đ2/HĐ-KHCNTTMinistry of Education and Training, Vietnam B2019.SPD.03Narodowe Centrum Nauki 2015/19/P/ST3/03541
6 · The paper itself

Abstract

The self-assembly of amyloidogenic peptides and proteins into fibrillar structures has been intensively studied for several decades, because it seems to be associated with a number of neurodegenerative diseases, such as Alzheimer's and Parkinson's disease. Therefore, understanding the molecular mechanisms of this phenomenon is important for identifying an effective therapy for the corresponding diseases. Protein aggregation in living organisms very often takes place on surfaces like membranes and the impact of a surface on this process depends not only on the surface chemistry but also on its topology. Our goal was to develop a simple lattice model for studying the role of surface roughness in the aggregation kinetics of polypeptide chains and the morphology of aggregates. We showed that, consistent with the experiment, an increase in roughness slows down the fibril formation, and this process becomes inhibited at a very highly level of roughness. We predicted a subtle catalytic effect that a slightly rough surface promotes the self-assembly of polypeptide chains but does not delay it. This effect occurs when the interaction between the surface and polypeptide chains is moderate and can be explained by taking into account the competition between energy and entropy factors.

Indexed as

Amyloidogenic ProteinsHumansModels, TheoreticalMonte Carlo MethodPolymerizationSurface PropertiesAmyloidogenic Proteinsaggregation of polypeptide chainsfibril formationlattice modelneurodegenerative diseasesprotein aggregationsurface roughness

Identifiers

PMID33919640
PMCPMC8072528

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.