Abstract
Coeluting peptides are still a major challenge for the identification and validation of MS/MS spectra, but carry great potential. To tackle these problems, we have developed the here presented CharmeRT workflow, combining a chimeric spectra identification strategy implemented as part of the MS Amanda algorithm with the validation system Elutator, which incorporates a highly accurate retention time prediction algorithm. For high-resolution data sets this workflow identifies 38–64% chimeric spectra, which results in up to 63% more unique peptides compared to a conventional single search strategy.
Highlights
Advancements in mass spectrometer instrument precision and acquisition time[1,2] made mass spectrometry the primary instrument in proteomics analyses
Even for narrow isolation widths (2 m/z) and small gradient times (1 h) we observed a considerable number of validated chimeric spectra, which increased the number of identified unique peptides by 41% (5360 unique peptides)
Similar results can be achieved for an external data set:[35] analyzing label-free data acquired at 1.4 m/z isolation width we see an average increase in PSMs of 75%, whereas for a TMT data set measured at a very narrow isolation width of 0.4 m/z only a small amount of chimeric spectra can be identified
Summary
Advancements in mass spectrometer instrument precision and acquisition time[1,2] made mass spectrometry the primary instrument in proteomics analyses. The validation of more than one peptide match per spectrum (here called mPSM) is an important task,[15] as the confidence score for the most abundant peptide in a spectrum is not comparable to the score of a second coeluting peptide present in the spectrum. Through ignoring this valuable information a large amount of unique peptides remains unidentified, as recent studies show that about 50% of all spectra contain more than one peptide.[7,15]. Detecting highly abundant proteins is a lot simpler than identifying the least abundant part of the proteome.[16,17] Many approaches have been conducted to increase proteome coverage and enable deep proteome analysis,[18−25] being more or less straightforward and affordable techniques for an everyday proteomics workflow
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