skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome

Abstract

Large scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, which suggests that the false positive rates are highly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria weremore » compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-48), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-7). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications.« less

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
15020586
Report Number(s):
PNNL-SA-42815
400412000; TRN: US200609%%191
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Proteome Research, 4(1):53-62
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; ANIMAL CELLS; CATIONS; CHROMATOGRAPHY; EVALUATION; LIVER CELLS; MASS SPECTROSCOPY; MIXTURES; PEPTIDES; PLASMA; PROTEINS; SPECTRA

Citation Formats

Qian, Weijun, Liu, Tao, Monroe, Matthew E, Strittmatter, Eric F, Jacobs, Jon M, Kangas, Lars J, Petritis, Konstantinos, Camp, David G, and Smith, Richard D. Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome. United States: N. p., 2005. Web. doi:10.1021/pr0498638.
Qian, Weijun, Liu, Tao, Monroe, Matthew E, Strittmatter, Eric F, Jacobs, Jon M, Kangas, Lars J, Petritis, Konstantinos, Camp, David G, & Smith, Richard D. Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome. United States. https://doi.org/10.1021/pr0498638
Qian, Weijun, Liu, Tao, Monroe, Matthew E, Strittmatter, Eric F, Jacobs, Jon M, Kangas, Lars J, Petritis, Konstantinos, Camp, David G, and Smith, Richard D. Sat . "Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome". United States. https://doi.org/10.1021/pr0498638.
@article{osti_15020586,
title = {Probability-Based Evaluation of Peptide and Protein Identifications from Tandem Mass Spectrometry and SEQUEST Analysis: The Human Proteome},
author = {Qian, Weijun and Liu, Tao and Monroe, Matthew E and Strittmatter, Eric F and Jacobs, Jon M and Kangas, Lars J and Petritis, Konstantinos and Camp, David G and Smith, Richard D},
abstractNote = {Large scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, which suggests that the false positive rates are highly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-48), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-7). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications.},
doi = {10.1021/pr0498638},
url = {https://www.osti.gov/biblio/15020586}, journal = {Journal of Proteome Research, 4(1):53-62},
number = 1,
volume = 4,
place = {United States},
year = {2005},
month = {1}
}