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Title: Method for predicting peptide detection in mass spectrometry

Abstract

A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

Inventors:
 [1];  [2];  [2]
  1. West Richland, WA
  2. Richland, WA
Issue Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1013000
Patent Number(s):
7756646
Application Number:
11/394,839
Assignee:
Battelle Memorial Institute (Richland, WA)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01N - INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
DOE Contract Number:  
AC06-76RL01830
Resource Type:
Patent
Country of Publication:
United States
Language:
English

Citation Formats

Kangas, Lars, Smith, Richard D, and Petritis, Konstantinos. Method for predicting peptide detection in mass spectrometry. United States: N. p., 2010. Web.
Kangas, Lars, Smith, Richard D, & Petritis, Konstantinos. Method for predicting peptide detection in mass spectrometry. United States.
Kangas, Lars, Smith, Richard D, and Petritis, Konstantinos. Tue . "Method for predicting peptide detection in mass spectrometry". United States. https://www.osti.gov/servlets/purl/1013000.
@article{osti_1013000,
title = {Method for predicting peptide detection in mass spectrometry},
author = {Kangas, Lars and Smith, Richard D and Petritis, Konstantinos},
abstractNote = {A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {7}
}

Works referenced in this record:

Comparison of Probability and Likelihood Models for Peptide Identification from Tandem Mass Spectrometry Data
journal, October 2005


Probability-Based Validation of Protein Identifications Using a Modified SEQUEST Algorithm
journal, November 2002


Empirical Statistical Model To Estimate the Accuracy of Peptide Identifications Made by MS/MS and Database Search
journal, October 2002