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Characterization of Disulfide-Linked Peptides Using Tandem Mass Spectrometry Coupled with Automated Data Analysis Software

Overview of attention for article published in Journal of the American Society for Mass Spectrometry, January 2018
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Title
Characterization of Disulfide-Linked Peptides Using Tandem Mass Spectrometry Coupled with Automated Data Analysis Software
Published in
Journal of the American Society for Mass Spectrometry, January 2018
DOI 10.1007/s13361-017-1855-0
Pubmed ID
Authors

Zhidan Liang, Kenneth N. McGuinness, Alejandro Crespo, Wendy Zhong

Abstract

Disulfide bond formation is critical for maintaining structure stability and function of many peptides and proteins. Mass spectrometry has become an important tool for the elucidation of molecular connectivity. However, the interpretation of the tandem mass spectral data of disulfide-linked peptides has been a major challenge due to the lack of appropriate tools. Developing proper data analysis software is essential to quickly characterize disulfide-linked peptides. A thorough and in-depth understanding of how disulfide-linked peptides fragment in mass spectrometer is a key in developing software to interpret the tandem mass spectra of these peptides. Two model peptides with inter- and intra-chain disulfide linkages were used to study fragmentation behavior in both collisional-activated dissociation (CAD) and electron-based dissociation (ExD) experiments. Fragments generated from CAD and ExD can be categorized into three major types, which result from different S-S and C-S bond cleavage patterns. DiSulFinder is a computer algorithm that was newly developed based on the fragmentation observed in these peptides. The software is vendor neutral and capable of quickly and accurately identifying a variety of fragments generated from disulfide-linked peptides. DiSulFinder identifies peptide backbone fragments with S-S and C-S bond cleavages and, more importantly, can also identify fragments with the S-S bond still intact to aid disulfide linkage determination. With the assistance of this software, more comprehensive disulfide connectivity characterization can be achieved. Graphical Abstract ᅟ.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 30%
Researcher 3 13%
Student > Master 2 9%
Student > Doctoral Student 1 4%
Other 1 4%
Other 1 4%
Unknown 8 35%
Readers by discipline Count As %
Chemistry 8 35%
Biochemistry, Genetics and Molecular Biology 5 22%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Medicine and Dentistry 1 4%
Agricultural and Biological Sciences 1 4%
Other 0 0%
Unknown 7 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 May 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Journal of the American Society for Mass Spectrometry
#2,946
of 3,835 outputs
Outputs of similar age
#325,947
of 450,332 outputs
Outputs of similar age from Journal of the American Society for Mass Spectrometry
#35
of 57 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,835 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 450,332 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.