Scripps VIVO scripps research logo

  • Index
  • Log in
  • Home
  • People
  • Organizations
  • Research
  • Events
Search form

Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data

Academic Article
uri icon
  • Overview
  • Identity
  • Additional Document Info
  • View All
scroll to property group menus

Overview

authors

  • Lu, B. W.
  • Motoyama, A.
  • Ruse, C.
  • Venable, J.
  • Yates III, John

publication date

  • March 2008

journal

  • Analytical Chemistry  Journal

abstract

  • We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.

subject areas

  • Fourier Analysis
  • Peptide Mapping
  • Proteomics
  • Saccharomyces cerevisiae Proteins
  • Sensitivity and Specificity
  • Tandem Mass Spectrometry
scroll to property group menus

Identity

PubMed Central ID

  • PMC3509208

International Standard Serial Number (ISSN)

  • 0003-2700

Digital Object Identifier (DOI)

  • 10.1021/ac701697w

PubMed ID

  • 18275164
scroll to property group menus

Additional Document Info

start page

  • 2018

end page

  • 2025

volume

  • 80

issue

  • 6

©2021 The Scripps Research Institute | Terms of Use | Powered by VIVO

  • About
  • Contact Us
  • Support