Evidence map›Paper›PMID 30786713›Full record

ArticleJournal of proteome research2019

Evaluation of Different Stationary Phases in the Separation of Inter-Cross-Linked Peptides.

Zixiang Fang, Yehia Z Baghdady, Kevin A Schug, Saiful M Chowdhury

Open access · greenAbstract read
In one paragraph

Article in Journal of proteome research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. 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 at 1 institution in 1 country.

Zixiang FangDepartment of Chemistry & Biochemistry , The University of Texas at Arlington , Arlington , Texas 76019 , United States.
Yehia Z BaghdadyDepartment of Chemistry & Biochemistry , The University of Texas at Arlington , Arlington , Texas 76019 , United States.
Kevin A SchugDepartment of Chemistry & Biochemistry , The University of Texas at Arlington , Arlington , Texas 76019 , United States.ORCID 0000-0003-4817-9508
Saiful M ChowdhuryDepartment of Chemistry & Biochemistry , The University of Texas at Arlington , Arlington , Texas 76019 , United States.ORCID 0000-0002-2553-985X
The University of Texas at Arlington · US

Funding

Identifying host-defense interactome caused by environmental and external agentsUA5GM113216 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI CHOWDHURY, SAIFUL M · 2015 to 2015
$355k
NIGMS NIH HHS UA5 GM113216
6 · The paper itself

Abstract

Chemical cross-linking coupled with mass spectrometry (MS) is becoming a routinely and widely used technique for depicting and constructing protein structures and protein interaction networks. One major challenge for cross-linking/MS is the determination of informative low-abundant inter-cross-linked products, generated within a sample of high complexity. A C18 stationary phase is the conventional means for reversed-phase (RP) separation of inter-cross-linked peptides. Various RP stationary phases, which provide different selectivities and retentions, have been developed as alternatives to C18 stationary phases. In this study, two phenyl-based columns, biphenyl and fluorophenyl, were investigated and compared with a C18 phase for separating BS

Indexed as

Biphenyl CompoundsChromatography, Reverse-PhasePeptidesProteomicsSequence Analysis, ProteinbiphenylBiphenyl CompoundsPeptideschemical cross-linkingcross-linked peptidesfluorophenyl columnmass spectrometrypeptide separation and enrichmentphenyl-based stationary phases

Identifiers

PMID30786713
PMCPMC6542356
OpenAlexW2915680876

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.