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

peer-reviewed · 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2012

peer-reviewed · 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2012. James Paul Cleveland et al. The effectiveness of de novo peptide sequencing algorithms depends on the quality of MS/MS spectra. Since…
Date 2012-10-01
Type peer-reviewed
Venue 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Contribution adjacent
DOI 10.1109/BIBM.2012.6392625
Citations (OpenAlex) 2

Abstract

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.

Authors

  1. James Paul Cleveland · University of South Carolina
  2. John Rose · University of South Carolina

Methods and tools

  • b-/y-ion staged neural network: Staged neural network that models ion fragmentation patterns and estimates the posterior probability of each ion type, used to pick the informative peaks out of an MS/MS spectrum before sequencing. The motivation is search-space control: too many peaks and the candidate peptide space grows exponentially, too few and the ion ladder has gaps that can only be explained by permutations of amino acid combinations, so either way candidate quality drops. Reported to beat other preprocessing techniques and to cut the candidate search space substantially without losing candidate quality. The authors note the step matters to any interpretation of MS/MS spectra, de novo or not. Two papers a year apart: the BIBM 2012 classifier, and the two-stage version in Proteome Science 2013 that states it improves on it.

Cites (5)

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