title: Multi-spectra peptide sequencing and its applications to multistage mass spectrometry.
abstract: Despite a recent surge of interest in database-independent peptide identifications, accurate de novo peptide sequencing remains an elusive goal. While the recently introduced spectral network approach resulted in accurate peptide sequencing in low-complexity samples, its success depends on the chance of presence of spectra from overlapping peptides. On the other hand, while multistage mass spectrometry (collecting multiple MS 3 spectra from each MS 2 spectrum) can be applied to all spectra in a complex sample, there are currently no software tools for de novo peptide sequencing by multistage mass spectrometry. We describe a rigorous probabilistic framework for analyzing spectra of overlapping peptides and show how to apply it for multistage mass spectrometry. Our software results in both accurate de novo peptide sequencing from multistage mass spectra (despite the inferior quality of MS 3 spectra) and improved interpretation of spectral networks. We further study the problem of de novo peptide sequencing with accurate parent mass (but inaccurate fragment masses), the protocol that may soon become the dominant mode of spectral acquisition. Most existing peptide sequencing algorithms (based on the spectrum graph approach) do not track the accurate parent mass and are thus not equipped for solving this problem. We describe a de novo peptide sequencing algorithm aimed at this experimental protocol and show that it improves the sequencing accuracy on both tandem and multistage mass spectrometry.
authors.affiliation: Department of Computer Science and Engineering, University of California, San Diego, USA. bandeira@ucsd.edu
authors.forename: Nuno
authors.initials: N
authors.lastname: Bandeira
authors.forename: Jesper V
authors.initials: JV
authors.lastname: Olsen
authors.forename: Jesper V
authors.initials: JV
authors.lastname: Mann
authors.forename: Matthias
authors.initials: M
authors.lastname: Mann
authors.forename: Pavel A
authors.initials: PA
authors.lastname: Pevzner
mesh_terms: D019295:Computational Biology; D005656:Fungal Proteins / Q000032:analysis*; D010455:Peptides / Q000032:analysis*; D020539:Sequence Analysis, Protein / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000379:methods*; D015003:Yeasts / Q000737:chemistry*
chemical_list: D005656:Fungal Proteins; D010455:Peptides
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