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A Predictive Model for Identifying Proteins by a Single Peptide Match
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BibTeX
@MISC{Higdon_apredictive,
author = {Roger Higdon and Eugene Kolker and Alfonso Valencia},
title = {A Predictive Model for Identifying Proteins by a Single Peptide Match},
year = {}
}
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Abstract
Motivation: Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, two or more unique peptides identified within a single protein are generally recommended. Still, in a typical highthroughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68%-to-98 % of the correct single-hit proteins with an error rate of less than 2%. This results in a 22%-to-65 % increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. 1
Keyphrases
predictive model single peptide match tandem mass-spectrometry single-hit protein correct single-hit protein low abundance protein high-throughput proteomics study many crucial regulator certain scoring threshold single protein to-65 increase single peptide true single-hit protein database searching randomized database searching identified protein typical highthroughput experiment false identification unique peptide popular approach logistic regression model trypsin digest false positive protein identification error rate bacterial sample
