Abstract
Quantifying the similarity of spectra is an important task in various areas of spectroscopy, for example, to identify a compound by comparing sample spectra to those of reference standards. In mass spectrometry based discovery proteomics, spectral comparisons are used to infer the amino acid sequence of peptides. In targeted proteomics by selected reaction monitoring (SRM) or SWATH MS, predetermined sets of fragment ion signals integrated over chromatographic time are used to identify target peptides in complex samples. In both cases, confidence in peptide identification is directly related to the quality of spectral matches. In this study, we used sets of simulated spectra of well-controlled dissimilarity to benchmark different spectral comparison measures and to develop a robust scoring scheme that quantifies the similarity of fragment ion spectra. We applied the normalized spectral contrast angle score to quantify the similarity of spectra to objectively assess fragment ion variability of tandem mass spectrometric datasets, to evaluate portability of peptide fragment ion spectra for targeted mass spectrometry across different types of mass spectrometers and to discriminate target assays from decoys in targeted proteomics. Altogether, this study validates the use of the normalized spectral contrast angle as a sensitive spectral similarity measure for targeted proteomics, and more generally provides a methodology to assess the performance of spectral comparisons and to support the rational selection of the most appropriate similarity measure. The algorithms used in this study are made publicly available as an open source toolset with a graphical user interface.
Highlights
In “bottom-up” proteomics, peptide sequences are identified by the information contained in their fragment ion spectra [1]
Using a Benchmark of Simulated Spectra Set of Controlled Dissimilarity to Assess the Performance of Spectral Similarity Measures—So far, the performance of methods to assess spectral similarity has mostly been performed by using empirical data, for example, by comparing score distributions of database searches for spectra from similar or dissimilar peptide identifications [29, 32] or by assessing the recall characteristics of the peptide identifications by spectral library searches [18, 20, 24, 26, 29, 32]
Using the described benchmarked perturbation spectra set, the performance of any spectral similarity measure can be objectively tested stepwise across the range of 0 to 100% perturbation for the relative fragment ion intensities
Summary
In “bottom-up” proteomics, peptide sequences are identified by the information contained in their fragment ion spectra [1]. Dot product (18 –24), its corresponding arccosine spectral contrast angle [25,26,27] and (Pearson-like) spectral correlation (28 –31), and other geometrical distance measures [18, 32], have been used in the literature for assessing spectral similarity These measures have been used in different contexts including shotgun spectra clustering [19, 26], spectral library searching [18, 20, 21, 24, 25, 27,28,29], cross-instrument fragmentation comparisons [22, 30] and for scoring transitions in targeted proteomics analyses such as selected reaction monitoring (SRM)1 [23, 31]. We used the method to probe the fragmentation patterns of peptides carrying a post-translation modification
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