Peptide Identification by Database Search of Mixture Tandem Mass Spectra

Abstract

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

“…When interpreting an MS/MS spectrum as a mixture spectrum M, we construct two PRM spectra, M H and M L , each generated using the corresponding scoring models for high and low-abundance peptides present in a mixture spectrum. As shown in MixDB (8), different scoring models are needed for high and low-abundance peptides because they exhibit substantially different fragmentation statistics in mixture spectra. Intuitively, this is because the low-abundance peptides will generate less intense peaks in the mixture spectrum and, in general, it also has less number of detectable peaks above noise level.…”

“…Several recent analyses show that as many as 50% of the MS/MS spectra collected in typical proteomics experiments come from more than one peptide precursor (4, 5). The presence of multiple peptides in mixture spectra can decrease their identification rate to as low as one half of that for MS/MS spectra generated from only one peptide (6,7,8). In addition, there have been numerous developments in data independent acquisition (DIA) technologies where multiple peptide precursors are intentionally selected to cofragment in each MS/MS spectrum (9,10,11,12,13,14,15).…”

“…low reproducibility (16)) and potentially increase the throughput of peptide identification 5-10 fold (4,17). However, despite the growing importance of mixture spectra in various contexts, there are still only a few computational tools that can analyze mixture spectra from more than one peptide (18,19,20,21,8,22). Our recent analysis indicated that current database search methods for mixture spectra still have relatively low sensitivity compared with their single-peptide counterpart and the main bottleneck is their limited ability to separate true matches from false positive matches (8).…”

“…However, despite the growing importance of mixture spectra in various contexts, there are still only a few computational tools that can analyze mixture spectra from more than one peptide (18,19,20,21,8,22). Our recent analysis indicated that current database search methods for mixture spectra still have relatively low sensitivity compared with their single-peptide counterpart and the main bottleneck is their limited ability to separate true matches from false positive matches (8). Traditionally problem of peptide identification from MS/MS spectra involves two sub-problems: 1) define a Peptide-Spectrum-Match (PSM) scoring function that assigns each MS/MS spectrum to the peptide sequence that most likely generated the spectrum; and 2) given a set of top-scoring PSMs, select a subset that corresponds to statistical significance PSMs.…”

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra

“…For cross-linked peptides one evaluates how well a pair of peptides matches to an MS/MS spectrum. In our previous method, MixDB (42), we introduced a probabilistic model to score how well a pair of peptides matches to a mixture MS/MS spectrum from co-eluting peptides. The statistical framework used here extends that of MixDB by further capturing the specific fragmentation pattern of linked peptides.…”