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.
Highlights
From the ‡Bioinformatics Program, University of California, San Diego, La Jolla, CA; §Skaggs School of Pharmacy and Pharmaceutical Sciences, UCSD, San Diego, La Jolla, CA; ¶Center for Computational Mass Spectrometry, University of California, San Diego, La, Jolla, CA; ʈDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA
A tryptic digest of Saccharomyces cerevisiae was analyzed on an LTQ Orbitrap XL mass spectrometer (Thermo Fisher Scientific) and MS/MS spectra were acquired using a data-dependent scanning mode in which each full MS scan (m/z 300 –2000) was acquired on the Orbitrap at resolution 60,000, followed by eight MS/MS scans collected on the LTQ (see [27] for full details)
If we look further into those spectra that are only identified by M-SPLIT but not by database search methods, we find that for 60%, 30%, and 11% of the cases MixDB, InsPecT, and ProbIDtree, respectively, identify the same top hit as M-SPLIT
Summary
From the ‡Bioinformatics Program, University of California, San Diego, La Jolla, CA; §Skaggs School of Pharmacy and Pharmaceutical Sciences, UCSD, San Diego, La Jolla, CA; ¶Center for Computational Mass Spectrometry, University of California, San Diego, La, Jolla, CA; ʈDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA. We proposed a spectral library search tool, M-SPLIT, and showed that it is able to reliably and efficiently identify peptides from mixture spectra from co-eluting peptides in a yeast cell lysate [18]. Some database search tools approach the mixture spectra identification problem by reporting spectra with more than one significant single-peptide match and do not explicitly attempt to model the occurrence of fragment ions from two peptides in the same spectrum. Different from previous approaches, our new database search tool, MixDB, uses a scoring model designed for matching spectra against pairs of peptides and determines separate FDRs for identification of singlepeptide spectra and mixture spectra. Applying our method to a yeast lysate data set, we show that using efficient filtration techniques and a rigorous probabilistic scoring model, peptides can be reliably identified from mixture spectra while only considering a small fraction of all possible peptide pairs
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
