Application of de Novo Sequencing to Large-Scale Complex Proteomics Data Sets

Dependent on
concise, predefined protein sequence databases, traditional search
algorithms perform poorly when analyzing mass spectra derived from
wholly uncharacterized protein products. Conversely, de novo peptide
sequencing algorithms can interpret mass spectra without relying on
reference databases. However, such algorithms have been difficult
to apply to complex protein mixtures, in part due to a lack of methods
for automatically validating de novo sequencing results. Here, we
present novel metrics for benchmarking de novo sequencing algorithm
performance on large-scale proteomics data sets and present a method
for accurately calibrating false discovery rates on de novo results.
We also present a novel algorithm (LADS) that leverages experimentally
disambiguated fragmentation spectra to boost sequencing accuracy and
sensitivity. LADS improves sequencing accuracy on longer peptides
relative to that of other algorithms and improves discriminability
of correct and incorrect sequences. Using these advancements, we demonstrate
accurate de novo identification of peptide sequences not identifiable
using database search-based approaches.