Evidence map›Paper›PMID 34491750›Full record

ArticleThe journal of physical chemistry letters2021

High Water Density at Non-Ice-Binding Surfaces Contributes to the Hyperactivity of Antifreeze Proteins.

Akash Deep Biswas, Vincenzo Barone, Isabella Daidone

Open access · hybridAbstract read
In one paragraph

Article in The journal of physical chemistry letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

4 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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

3 authors at 2 institutions in 1 country.

Akash Deep BiswasScuola Normale Superiore di Pisa, Piazza dei Cavalieri 7, Pisa 56126, Italy.ORCID https://orcid.org/0000-0003-1108-8848
Vincenzo BaroneScuola Normale Superiore di Pisa, Piazza dei Cavalieri 7, Pisa 56126, Italy.ORCID https://orcid.org/0000-0001-6420-4107
Isabella DaidoneDepartment of Physical and Chemical Sciences, University of L'Aquila, via Vetoio (Coppito 1), 67010 L'Aquila, Italy.ORCID https://orcid.org/0000-0001-8970-8408
Scuola Normale Superiore · ITUniversity of L'Aquila · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antifreeze proteins (AFPs) can bind to ice nuclei thereby inhibiting their growth and their hydration shell is believed to play a fundamental role. Here, we use molecular dynamics simulations to characterize the hydration shell of four moderately-active and four hyperactive AFPs. The local water density around the ice-binding-surface (IBS) is found to be lower than that around the non-ice-binding surface (NIBS) and this difference correlates with the higher hydrophobicity of the former. While the water-density increase (with respect to bulk) around the IBS is similar between moderately-active and hyperactive AFPs, it differs around the NIBS, being higher for the hyperactive AFPs. We hypothesize that while the lower water density at the IBS can pave the way to protein binding to ice nuclei, irrespective of the antifreeze activity, the higher density at the NIBS of the hyperactive AFPs contribute to their enhanced ability in inhibiting ice growth around the bound AFPs.

Indexed as

AeromonadaceaeAntifreeze ProteinsBacterial ProteinsBasidiomycotaCrystallizationGranulovirusHydrophobic and Hydrophilic InteractionsIceIsomerismMolecular Dynamics SimulationProtein BindingProtein ConformationSurface PropertiesTemperatureAntifreeze ProteinsBacterial ProteinsIce

Identifiers

PMID34491750
PMCPMC8450935
OpenAlexW3197598340

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.