A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data

Abstract

Background: Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.

Results: A probability based statistical scoring model for assessing peptide and protein matches in tandem MS database search was derived. The statistical scores in the model represent the probability that a peptide match is a random occurrence based on the number or the total abundance of matched product ions in the experimental spectrum. The model also calculates probability based scores to assess protein matches. Thus the protein scores in the model reflect the significance of protein matches and can be used to differentiate true from random protein matches.

Conclusion: The model is sensitive to high mass accuracy and implicitly takes mass accuracy into account during scoring. High mass accuracy will not only reduce false positives, but also improves the scores of true positive matches. The algorithm is incorporated in an automated database search program MassMatrix.

Figure 1

Examples of spectral influences on scoring. High quality data for peptides of short (a) and long (b) lengths. Poorer quality data from a low signal-to-noise spectrum (c), a spectrum from a peptide with a single dominant product ion due to fragmentation at the N-terminal side of Pro (d) and a spectrum of a peptide with a single dominant product ion due to a extensive neutral loss of water at the N-terminal Glu (e) [30].

Figure 2

Effect of mass accuracy on the theoretical probability density function (f(pp)) of pp and pp2 values for random matches (β ≥ β_c).