Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 163

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Feasibility of Large-Scale Phosphoproteomics with Higher Energy Collisional Dissociation Fragmentation

Open access
Nagaraj, Nagarjuna, D’Souza, Rochelle C. J., Cox, Juergen, Olsen, Jesper V., Mann, Matthias·2010·Journal of Proteome Research

Abstract

Mass spectrometry (MS)-based proteomics now enables the analysis of thousands of phosphorylation sites in single projects. Among a wide range of analytical approaches, the combination of high resolution MS scans in an Orbitrap analyzer with low resolution MS/MS scans in a linear ion trap has proven to be particularly successful ("high-low" strategy). Here we investigate if the improved sensitivity of higher energy collisional dissociation (HCD) on an LTQ-Orbitrap Velos instrument allows a "high-high" strategy. A high resolution MS scan was followed by up to 10 HCD MS/MS scans, and we achieved cycle times of about 3 s making the method compatible with chromatographic time scales. Fragment mass accuracy increased about 50-fold compared to the "high-low" strategy. Unexpectedly, the HCD approach mapped up to 16,000 total phosphorylation sites in one day's measuring time--the same or better than the standard high-low strategy. Reducing the target values from a standard of 30,000 to 5000 ions did not severely affect identification rates but did decrease identification and localization scores for phosphorylation sites. We conclude that HCD in the new configuration is now a viable method for large-scale phosphoproteome analysis alongside collisional induced dissociation, (CID) and electron capture/transfer dissociation (ECD/ETD).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/pr100637q

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

Metadata

Authors
Nagaraj, N., D’Souza, R. C. J., Cox, J., Olsen, J. V., & Mann, M.
Venue
Journal of Proteome Research
Published
2010-10-26
Volume
9
Issue
12
Pages
6786-6794
ISSN
  • 1535-3893 (ISSN-L)
  • 1535-3907
OpenAlex
W2018247248
Cited by
163
References
4
FWCI
10.64
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.