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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Improving proteome coverage on a LTQ-Orbitrap using design of experiments.

Genna L Andrews1, Ralph A Dean, Adam M Hawkridge

  • 1W. M. Keck FT-ICR Mass Spectrometry Laboratory, Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA.

Journal of the American Society for Mass Spectrometry
|April 8, 2011
PubMed
Summary

Design of Experiments (DOE) optimized LTQ-Orbitrap XL settings, boosting Saccharomyces cerevisiae proteome coverage by 60%. This cost-effective method enhances mass spectrometry proteomics workflows.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Optimizing mass spectrometry (MS) instrument parameters is crucial for maximizing proteome coverage.
  • Traditional methods for parameter optimization can be time-consuming and inefficient.

Purpose of the Study:

  • To determine improved settings for the LTQ-Orbitrap XL mass spectrometer to maximize proteome coverage of Saccharomyces cerevisiae.
  • To evaluate the effectiveness of the Design of Experiments (DOE) approach for optimizing MS-based proteomics workflows.

Main Methods:

  • Design of Experiments (DOE) methodology, including fractional and full factorial designs, was employed using JMP software.
  • Nine instrument parameters were systematically evaluated for their impact on proteome coverage.
  • Nano liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) was used for randomized triplicate analysis of S. cerevisiae digest.

Main Results:

  • An approximate 60% increase in proteome coverage was achieved with optimized instrument settings.
  • Five parameters significantly influenced proteome coverage: maximum ion trap ionization time, monoisotopic precursor selection, number of MS/MS events, capillary temperature, and tube lens voltage.
  • Four parameters showed minimal influence on proteome coverage.

Conclusions:

  • The DOE approach provides a time- and cost-effective method for empirically optimizing MS-based proteomics workflows.
  • Optimized instrument parameters significantly enhance proteome coverage, leading to more comprehensive proteomic analyses.
  • This strategy is applicable to various aspects of proteomics, including sample preparation, LC conditions, and different instrument platforms.