Qscore: An algorithm for evaluating SEQUEST database search results

Abstract

A scoring procedure is described for measuring the quality of the results for protein identifications obtained from spectral matching of MS/MS data using the Sequest database search program. The scoring system is essentially probabilistic and operates by estimating the probability that a protein identification has come about by chance. The probability is based on the number of identified peptides from the protein, the total number of identified peptides, and the fraction of distinct tryptic peptides from the database that are present in the identified protein. The score is not strictly a probability, as it also incorporates information about the quality of the individual peptide matches. The result of using Qscore on a large test set of data was similar to that achieved using approaches that validate individual spectral matches, with only a narrow overlap in scores between identified proteins and false positive matches. In direct comparison with a published method of evaluating Sequest results, Qscore was able to identify an equivalent number of proteins without any identifiable false positive assignments. Qscore greatly reduces the number of Sequest protein identifications that have to be validated manually. (J Am Soc Mass Spectrom 2002, 13, 378 -386) © 2002 American Society for Mass Spectrometry P roteolytic digestion followed by mass spectrometry and database searching has become the premier approach to sensitive identification of proteins. High throughput approaches to protein identification depend on minimizing human time investment in this analysis. A variety of techniques, including robotic gel band excision and digestion, automated matrix-assisted laser desorption/ionization (MALDI) spotting, autosampled nano-liquid chromatography tandem mass spectrometry (LC/MS/MS) analysis, and automated database searching, have been developed to further this aim. To make automated database searching possible, it is necessary to use a search program that can process spectra without need for human interpretation. In theory, it is also desirable to have an automated scheme to determine the significance and reliability of the database search results.

Proteins and Protein Pattern Differences between Glioma Cell Lines and Glioblastoma Multiforme

“…Briefly, it is possible that a peptide sequence is deemed correct (i.e., the top match) based on statistical and/or subjective criteria and not actually be correct due to incompleteness of genomic databases (15) …”

Inference and Validation of Protein Identifications

“…PeptideProphet (49) by resorting to Expectation Maximization (50). Recently, the target-decoy strategy became very popular to estimate the peptide spectrum match false discovery rate (51). A decoy database with nonsense protein sequences is searched in addition to the (target) protein database of the studied organism.…”

Rapid and Accurate Peptide Identification from Tandem Mass Spectra

“…When identifying peptides from tandem mass spectra, a commonly used null model is to search the given set of spectra against a decoy database. A decoy database is a database of amino acid sequences that is derived from the original protein database (called the target database) by reversing the target sequences, 10 shuffling the target sequences, 11 or generating the decoy sequences at random using a Markov model with parameters derived from the target sequences. 12 Ideally, the decoy database should contain peptide-like amino acid sequences that are not in the target database.…”