Evidence map›Paper›PMID 29925016›Full record

ArticleBiophysical journal2018

Principles for Optimal Cooperativity in Allosteric Materials.

Le Yan, Riccardo Ravasio, Carolina Brito, Matthieu Wyart

Abstract read
In one paragraph

Article in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Evolutionary pathways in epistatic mechanical networks.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Learning to learn by using nonequilibrium training protocols for adaptable materials.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Allostery in Its Many Disguises: From Theory to Applications.Structure (London, England : 1993) · 2019
    Review
  9. Article
  10. Designed Elastic Networks: Models of Complex Protein Machinery.International journal of molecular sciences · 2018
    Review
  11. 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

4 authors.

Le YanKavli Institute for Theoretical Physics, University of California, Santa Barbara, Santa Barbara, California.
Riccardo RavasioInstitute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Carolina BritoInstituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Matthieu WyartInstitute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Electronic address: matthieu.wyart@epfl.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Allosteric proteins transmit a mechanical signal induced by binding a ligand. However, understanding the nature of the information transmitted and the architectures optimizing such transmission remains a challenge. Here we show, using an in silico evolution scheme and theoretical arguments, that architectures optimized to be cooperative, which efficiently propagate energy, qualitatively differ from previously investigated materials optimized to propagate strain. Although we observe a large diversity of functioning cooperative architectures (including shear, hinge, and twist designs), they all obey the same principle of displaying a mechanism, i.e., an extended soft mode. We show that its optimal frequency decreases with the spatial extension L of the system as L

Indexed as

Models, BiologicalAllosteric RegulationComputational BiologyProtein BindingProteinsThermodynamicsProteins

Identifiers

PMID29925016
PMCPMC6026448

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.