A novel algorithm for validating peptide identification from a shotgun proteomics search engine.

Jian L, Niu X, Xia Z, Samir P, Sumanasekera C, Mu Z, Jennings JL, Hoek KL, Allos T, Howard LM, Edwards KM, Weil PA, Link AJ
J Proteome Res. 2013 12 (3): 1108-19

PMID: 23402659 · PMCID: PMC3608465 · DOI:10.1021/pr300631t

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has revolutionized the proteomics analysis of complexes, cells, and tissues. In a typical proteomic analysis, the tandem mass spectra from a LC-MS/MS experiment are assigned to a peptide by a search engine that compares the experimental MS/MS peptide data to theoretical peptide sequences in a protein database. The peptide spectra matches are then used to infer a list of identified proteins in the original sample. However, the search engines often fail to distinguish between correct and incorrect peptides assignments. In this study, we designed and implemented a novel algorithm called De-Noise to reduce the number of incorrect peptide matches and maximize the number of correct peptides at a fixed false discovery rate using a minimal number of scoring outputs from the SEQUEST search engine. The novel algorithm uses a three-step process: data cleaning, data refining through a SVM-based decision function, and a final data refining step based on proteolytic peptide patterns. Using proteomics data generated on different types of mass spectrometers, we optimized the De-Noise algorithm on the basis of the resolution and mass accuracy of the mass spectrometer employed in the LC-MS/MS experiment. Our results demonstrate De-Noise improves peptide identification compared to other methods used to process the peptide sequence matches assigned by SEQUEST. Because De-Noise uses a limited number of scoring attributes, it can be easily implemented with other search engines.

MeSH Terms (6)

Algorithms Chromatography, Liquid Databases, Protein Humans Proteomics Tandem Mass Spectrometry

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