Properties of Average Score Distributions of SEQUEST

Abstract

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments. Molecular & Cellular Proteomics 7:1135-1145, 2008.Modern proteomics is mainly based on the analysis of proteins by mass spectrometry. With the increasing use of multidimensional peptide separation coupled to tandem mass spectrometry as an alternative to gel-based approaches for protein expression analysis and high throughput protein identification, there is a growing interest in the development of scoring systems for automated, large scale peptide identification.SEQUEST (1-3), one of the first and most popular scoring schemes, measures the degree of correlation between the experimentally observed and the theoretical MS/MS spectra of peptides present in protein databases and determines the peptide sequence yielding the best correlation score, or Xcorr. Another related SEQUEST parameter is the delta score, or ⌬C n , which measures the difference between the best and second best scores (1-3). SEQUEST results must be further processed to determine whether the peptide identification is correct or not. Filtering of SEQUEST...

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