Evidence map›Paper›PMID 21804553›Full record

ReviewNature nanotechnology2011

Nanomechanics of functional and pathological amyloid materials.

Tuomas P J Knowles, Markus J Buehler

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature nanotechnology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 220 papers.

0numbers the graph read from it
0cells of the map it votes in
220citing 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

220 citing papers in PubMed.

  1. Article
  2. Review
  3. Liquid-liquid phase separation enables chromatography-free purification and high-performance spidroin-amyloid hybrid silk fibers.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Article
  6. The temperature dependence of amyloidProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Catalytic Amyloids: Turning Fibrils Into Biocatalysts.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Modeling protein motions through reinforcement learning.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  20. Article

160 more citing papers are in PubMed but not listed here.

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.

Tuomas P J KnowlesDepartment of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. tpjk2@cam.ac.uk
Markus J Buehler

Funding

Medical Research Council MC_G1000734Wellcome Trust 089703
6 · The paper itself

Abstract

Amyloid or amyloid-like fibrils represent a general class of nanomaterials that can be formed from many different peptides and proteins. Although these structures have an important role in neurodegenerative disorders, amyloid materials have also been exploited for functional purposes by organisms ranging from bacteria to mammals. Here we review the functional and pathological roles of amyloid materials and discuss how they can be linked back to their nanoscale origins in the structure and nanomechanics of these materials. We focus on insights both from experiments and simulations, and discuss how comparisons between functional protein filaments and structures that are assembled abnormally can shed light on the fundamental material selection criteria that lead to evolutionary bias in multiscale material design in nature.

Indexed as

AmyloidBiocompatible MaterialsHumansMechanicsModels, BiologicalNanostructuresNanotechnologyAmyloidBiocompatible Materials

Identifiers

PMID21804553

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.