The
conjugation of complex post-translational modifications (PTMs)
such as glycosylation and Small Ubiquitin-like Modification (SUMOylation)
to a substrate protein can substantially change the resulting peptide
fragmentation pattern compared to its unmodified counterpart, making
current database search methods inappropriate for the identification
of tandem mass (MS/MS) spectra from such modified peptides. Traditionally
it has been difficult to develop new algorithms to identify these
atypical peptides because of the lack of a large set of annotated
spectra from which to learn the altered fragmentation pattern. Using
SUMOylation as an example, we propose a novel approach to generate
large MS/MS training data from modified peptides and derive an algorithm
that learns properties of PTM-specific fragmentation from such training
data. Benchmark tests on data sets of varying complexity show that
our method is 80–300% more sensitive than current state-of-the-art
approaches. The core concepts of our method are readily applicable
to developing algorithms for the identifications of peptides with
other complex PTMs.
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