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A neural network approach to the identification of b-/y-ions in MS/MS spectra

The effectiveness of de novo peptide sequencing algorithms depends on the quality of MS/MS spectra. Since most of the peaks in a spectrum are uninterpretable `noise' peaks it is necessary to carefully pre-filter the spectra to identify the `signal' peaks that likely correspond to b-/y-ions. Selecting the optimal set of peaks for candidate peptide generation is essential for obtaining accurate results. A careful balance must be maintained between the precision and recall of peaks that are selected for further processing and candidate peptide generation. If too many peaks are selected the search space will be too large and the problem becomes intractable. If too few peaks are selected cleavage sites will be missed, the resulting candidate peptides will have large gaps, and sequencing results will be poor. For this reason pre-filtering of MS/MS spectra and accurate selection of peaks for peptide candidate generation is essential to any de novo peptide sequencing algorithm. We present a novel neural network approach for the selection of b-/y-ions using known fragmentation characteristics, and leveraging neural network probability estimates of flanking and complementary ions. We show a significant improvement in precision and recall of peaks corresponding to b-/y-ions and a reduction in search space over approaches used by other de novo peptide sequencing algorithms.