Comparison of Probability and Likelihood Models for Peptide Identification from Tandem Mass Spectrometry Data

Abstract

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

Evaluation of the influence of amino acid composition on the propensity for collision-induced dissociation of model peptides using molecular dynamics simulations

“…In most current fragmentation patterns that are used for identifying peptides [6,8], it is assumed that all ions from an ion series such as b and y ion series occur with the equal abundance. Recent work extended these fragmentation patterns to vary as a function of location of the peptide bond [4]. However, as will be suggested later, both position of the peptide bond and the sequence composition of the peptide are essential for accurate prediction of model spectra.…”

“…This is, of course, a convenient assumption computationally, but it is nevertheless a poor assumption. This assumption has been relaxed in a recent report by using fragmentation models in which each member of an ion series is expected with varying probability that depends on the position of the fragmenting bond along the peptide backbone [4]. However, this work does not take into account the amino acid composition of a peptide.…”

A Scalable Parallel Approach for Peptide Identification from Large-Scale Mass Spectrometry Data

“…Several programs exist for conducting peptide identification through database search [2], [5], [7], [8], [10], [11], [13]. Of these, Sequest [11] and Mascot [13] are two commercially available serial programs.…”

“…In 2005, Cannon et al [5] evaluated the effect of various probability and likelihood models on the accuracy of the peptide identification process using mass spectrometry data. Based on this qualitative study, they developed a program called MSPolygraph that implements a highly accurate database search.…”

“…An experimental spectrum for q is said to match with a candidate peptide if it can be shown that the candidate is most likely to generate a model spectrum similar to that of the experimental spectrum. This is achieved in MSPolygraph by generating two different spectra [5] -one a model spectrum for the candidate and the other being a spectrum generated for a random peptide -and then comparing both against the experimental spectrum. The result is a likelihood ratio score, and if the score is above a user-specified cutoff then the corresponding matching peptide is reported as a "hit".…”