Mass‐spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom‐up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.
Identification and characterization of N-glycopeptides
from complex
samples are usually based on tandem mass spectrometric measurements.
Experimental settings, especially the collision energy selection method,
fundamentally influence the obtained fragmentation pattern and hence
the confidence of the database search results (“score”).
Using standards of naturally occurring glycoproteins, we mapped the
Byonic and pGlyco search engine scores of almost 200 individual N-glycopeptides
as a function of collision energy settings on a quadrupole time of
flight instrument. The resulting unprecedented amount of peptide-level
information on such a large and diverse set of N-glycopeptides revealed
that the peptide sequence heavily influences the energy for the highest
score on top of an expected general linear trend with m/z. Search engine dependence may also be noteworthy.
Based on the trends, we designed an experimental method and tested
it on HeLa, blood plasma, and monoclonal antibody samples. As compared
to the literature, these notably lower collision energies in our workflow
led to 10–50% more identified N-glycopeptides, with higher
scores. We recommend a simple approach based on a small set of reference
N-glycopeptides easily accessible from glycoprotein standards to ease
the precise determination of optimal methods on other instruments.
Data sets can be accessed via the MassIVE repository (MSV000089657
and MSV000090218).
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