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Related Experiment Video

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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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Expediting SRM assay development for large-scale targeted proteomics experiments.

Chaochao Wu1, Tujin Shi, Joseph N Brown

  • 1Biological Sciences Division and ‡Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory , Richland, Washington 99352, United States.

Journal of Proteome Research
|August 23, 2014
PubMed
Summary
This summary is machine-generated.

Accelerating selected reaction monitoring (SRM) assay development is crucial for proteomics. This study shows that using HCD fragmentation and a new collision energy equation can expedite the creation of high-sensitivity SRM assays.

Keywords:
CE predictionHCDMRMQQQSRMoptimizationtargeted quantificationtransition selection

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Selected reaction monitoring (SRM)-based targeted proteomics offers high sensitivity and specificity.
  • Assay development, including transition selection and collision energy (CE) optimization, is critical but labor-intensive for large-scale applications.

Purpose of the Study:

  • To explore methods for accelerating SRM assay development.
  • To improve the throughput and quality of SRM measurements in proteomics.

Main Methods:

  • Evaluated HCD fragmentation versus CID in triple quadrupole (QQQ) instrumentation.
  • Assessed existing collision energy (CE) prediction tools and developed a new CE prediction equation.
  • Tested methods on 215 synthetic peptide targets.

Main Results:

  • HCD fragmentation closely mimics CID fragmentation in QQQ instruments.
  • Selecting the top 6 y fragment ions from HCD spectra covers over 86% of top transitions optimized via direct infusion.
  • A new CE prediction equation significantly increased transition intensity for 3+ precursors compared to existing tools.

Conclusions:

  • Expediting SRM assay development is feasible through automated transition selection and accurate CE prediction.
  • Optimized methods enhance SRM throughput and measurement quality for large-scale proteomics.
  • The findings facilitate the development of more high-sensitivity SRM assays.