Evidence map›Paper›PMID 29874404›Full record

ArticleRapid communications in mass spectrometry : RCM2018

Unusual fragmentation of derivatized cysteine-containing peptides.

Nick Degraan-Weber, Bingqing Zhao, James P Reilly

Abstract read
In one paragraph

Article in Rapid communications in mass spectrometry : RCM, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Nick Degraan-WeberDepartment of Chemistry, Indiana University, 800 East Kirkwood Avenue, Bloomington, IN, 47405, USA.
Bingqing ZhaoDepartment of Chemistry, Indiana University, 800 East Kirkwood Avenue, Bloomington, IN, 47405, USA.
James P ReillyDepartment of Chemistry, Indiana University, 800 East Kirkwood Avenue, Bloomington, IN, 47405, USA.ORCID http://orcid.org/0000-0002-9784-1804

Funding

Interactions Regulating Translation and Protein Biogenesis in VivoU54GM105816 · NIGMS · UNIVERSITY OF NOTRE DAME · PI BUSKIRK, ALLEN ROWDON, CLARK, PATRICIA LOUISE · 2013 to 2017
$3.7M
Computational approaches to protein identification and quantification using MS/MSR01GM103725 · NIGMS · TRUSTEES OF INDIANA UNIVERSITY · PI RADIVOJAC, PREDRAG · 2012 to 2015
$1.7M
NIGMS NIH HHS R01 GM103725NIGMS NIH HHS U54 GM105816NIH Blueprint for Neuroscience Research R01GM103725
6 · The paper itself

Abstract

rationaleModification of cysteines by aminoethylation results in side chains similar to those of lysine. Trypsin cleaves at this modified residue and this labeling method can facilitate the analysis of proteins, specifically antibodies. In this work, the ability to identify peptides containing aminoethylated cysteines is investigated through digestion, covalent labeling, and low-energy ion fragmentation.

methodsA prototype antibody was reduced, aminoethylated, and digested with either Lys-N or Glu-C. The resulting peptides were amidinated with SMTA and analyzed by PSD in a MALDI-TOF/TOF mass spectrometer or by CID in an ESI ion trap/orbitrap mass spectrometer.

resultsPSD and CID fragmentation of peptides with an amidinated aminoethylated cysteine can produce an intense characteristic loss from this modified residue. A neutral loss of 118 Da or charged loss of 119 Da is observed when peptides have low charges. This fragment can form when the cysteine is located in any position in the peptide. The rationalization for this ion is that the amidino group can be initially neutral or protonated and initiates fragmentation.

conclusionsThe combination of a dual-labeling technique and low-energy fragmentation produces an abundant diagnostic ion for the analysis of cysteine-containing peptides. These 118 and 119 Da losses are observed when protons are sequestered.

Indexed as

CysteineDigestionMolecular WeightPeptide MappingPeptidesTandem Mass SpectrometryCysteinePeptides

Identifiers

PMID29874404
PMCPMC6430700

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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.