Evidence map›Paper›PMID 35089292›Full record

ArticleNanoscale2022

High affinity protein surface binding through co-engineering of nanoparticles and proteins.

Moumita Ray, Giorgia Brancolini, David C Luther, Ziwen Jiang, Roberto Cao-Milán, Alejandro M Cuadros, Andrew Burden, Vincent Clark, Subinoy Rana, Rubul Mout and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanoscale, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
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  8. 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

13 authors.

Moumita RayDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.
Giorgia BrancoliniCenter S3, CNR Institute of Nanoscience, via Campi 213/A, 41125 Modena, Italy.ORCID http://orcid.org/0000-0002-1083-9198
David C LutherDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0002-4697-8683
Ziwen JiangDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.
Roberto Cao-MilánDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0002-5935-2313
Alejandro M CuadrosDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0002-5900-5143
Andrew BurdenDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.
Vincent ClarkDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0001-9707-8004
Subinoy RanaDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0002-8039-1149
Rubul MoutDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0001-6125-5873
Ryan F LandisDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.
Stefano CorniCenter S3, CNR Institute of Nanoscience, via Campi 213/A, 41125 Modena, Italy.ORCID http://orcid.org/0000-0001-6707-108X
Vincent M RotelloDepartment of Chemistry, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003, USA. rotello@chem.umass.edu.ORCID http://orcid.org/0000-0002-5184-5439

Funding

CHEMISTRY-BIOLOGY INTERFACE PREDOCTORAL TRAINING GRANTST32GM008515 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI THOMPSON, LYNMARIE K. · 1995 to 2020
$4.3M
Rapid Multi-Channel Serum Profiling for Liver Disease using Fluorescent NanosensorsR01DK121351 · NIDDK · UNIVERSITY OF MASSACHUSETTS AMHERST · PI VINCENT M. ROTELLO · 2020 to 2026
$3.1M
Targeting of Bio-orthogonal Chemotherapeutic Nanozymes to Tumor-Associated MacrophagesR01EB022641 · NIBIB · UNIVERSITY OF MASSACHUSETTS AMHERST · PI ROTELLO, VINCENT M. · 2017 to 2024
$3.0M
NIBIB NIH HHS R01 EB022641NIDDK NIH HHS R01 DK121351NIGMS NIH HHS T32 GM008515
6 · The paper itself

Abstract

Control over supramolecular recognition between proteins and nanoparticles (NPs) is of fundamental importance in therapeutic applications and sensor development. Most NP-protein binding approaches use 'tags' such as biotin or His-tags to provide high affinity; protein surface recognition provides a versatile alternative strategy. Generating high affinity NP-protein interactions is challenging however, due to dielectric screening at physiological ionic strengths. We report here the co-engineering of nanoparticles and protein to provide high affinity binding. In this strategy, 'supercharged' proteins provide enhanced interfacial electrostatic interactions with complementarily charged nanoparticles, generating high affinity complexes. Significantly, the co-engineered protein-nanoparticle assemblies feature high binding affinity even at physiologically relevant ionic strength conditions. Computational studies identify both hydrophobic and electrostatic interactions as drivers for these high affinity NP-protein complexes.

Indexed as

NanoparticlesHydrophobic and Hydrophilic InteractionsProtein BindingProteinsStatic ElectricityProteins

Identifiers

PMID35089292
PMCPMC8941649

What OpenQuestion holds

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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.