Evidence map›Paper›PMID 38702306›Full record

ArticleNature communications2024

Cell-penetrating protein-recognizing polymeric nanoparticles through dynamic covalent chemistry and double imprinting.

Avijit Ghosh, Mansi Sharma, Yan Zhao

Abstract read
In one paragraph

Article in Nature communications, 2024. 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
–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

4 citing papers in PubMed.

  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.

Avijit GhoshDepartment of Chemistry, Iowa State University, Ames, IA, 50011-3111, USA.ORCID http://orcid.org/0000-0002-8974-9565
Mansi SharmaDepartment of Chemistry, Iowa State University, Ames, IA, 50011-3111, USA.
Yan ZhaoDepartment of Chemistry, Iowa State University, Ames, IA, 50011-3111, USA. zhaoy@iastate.edu.ORCID http://orcid.org/0000-0003-1215-2565

Funding

National Science Foundation (NSF) DMR-2002659National Science Foundation (NSF) DMR-2308625
6 · The paper itself

Abstract

Molecular recognition of proteins is key to their biological functions and processes such as protein-protein interactions (PPIs). The large binding interface involved and an often relatively flat binding surface make the development of selective protein-binding materials extremely challenging. A general method is reported in this work to construct protein-binding polymeric nanoparticles from cross-linked surfactant micelles. Preparation involves first dynamic covalent chemistry that encodes signature surface lysines on a protein template. A double molecular imprinting procedure fixes the binding groups on the nanoparticle for these lysine groups, meanwhile creating a binding interface complementary to the protein in size, shape, and distribution of acidic groups on the surface. These water-soluble nanoparticles possess excellent specificities for target proteins and sufficient affinities to inhibit natural PPIs such as those between cytochrome c (Cytc) and cytochrome c oxidase (CcO). With the ability to enter cells through a combination of energy-dependent and -independent pathways, they intervene apoptosis by inhibiting the PPI between Cytc and the apoptotic protease activating factor-1 (APAF1). Generality of the preparation and the excellent molecular recognition of the materials have the potential to make them powerful tools to probe protein functions in vitro and in cellulo.

Indexed as

Cytochromes cElectron Transport Complex IVNanoparticlesPolymersAnimalsApoptosisHeLa CellsHumansMicellesMolecular ImprintingProtein BindingCytochromes cElectron Transport Complex IVMicellesPolymers

Identifiers

PMID38702306
PMCPMC11068882

What OpenQuestion holds

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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.