Evidence map›Paper›PMID 31140788›Full record

ArticleBiochemistry2019

Design of a Protein Motif Responsive to Tyrosine Nitration and an Encoded Turn-Off Sensor of Tyrosine Nitration.

Andrew R Urmey, Neal J Zondlo

Open access · greenAbstract read
In one paragraph

Article in Biochemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. 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

2 authors at 1 institution in 1 country.

Andrew R UrmeyDepartment of Chemistry and Biochemistry , University of Delaware , Newark , Delaware 19716 , United States.
Neal J ZondloDepartment of Chemistry and Biochemistry , University of Delaware , Newark , Delaware 19716 , United States.ORCID 0000-0002-7417-8085
University of Delaware · US

Funding

Novel Protein SensorsR01GM093225 · NIGMS · UNIVERSITY OF DELAWARE · PI ZONDLO, NEAL J · 2010 to 2014
$1.4M
NIGMS NIH HHS R01 GM093225
6 · The paper itself

Abstract

Tyrosine nitration is a protein post-translational modification that is predominantly non-enzymatic and is observed to be increased under conditions of nitrosative stress and in numerous disease states. A small protein motif (14-18 amino acids) responsive to tyrosine nitration has been developed. In this design, nitrotyrosine replaced the conserved Glu12 of an EF-hand metal-binding motif. Thus, the non-nitrated peptide bound terbium weakly. In contrast, tyrosine nitration resulted in a 45-fold increase in terbium affinity. Nuclear magnetic resonance spectroscopy indicated direct binding of nitrotyrosine to the metal and EF-hand-like metal contacts in this designed peptide. Nitrotyrosine is an efficient quencher of fluorescence. To develop a sensor of tyrosine nitration, the initial design was modified to incorporate Glu residues at EF-hand positions 9 and 16 as additional metal-binding residues, to increase the terbium affinity of the peptide with unmodified tyrosine. This peptide with a tyrosine at residue 12 bound terbium and effectively sensitized terbium luminescence. Tyrosine nitration resulted in a 180-fold increase in terbium affinity ( K

Indexed as

Protein Processing, Post-TranslationalAmino Acid MotifsAmino Acid SequenceEscherichia coliLuminescenceMaltose-Binding ProteinsPeptidesPeroxynitrous AcidProof of Concept StudyProtein BindingProtein EngineeringRecombinant Fusion ProteinsTerbiumTyrosineMaltose-Binding ProteinsPeptidesPeroxynitrous AcidRecombinant Fusion ProteinsTerbiumTyrosine

Identifiers

PMID31140788
PMCPMC6688601
OpenAlexW2946950665

What OpenQuestion holds

Textmetadata
LicenceTDM
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.