Deep Learning Methods for De Novo Peptide Sequencing

peer-reviewed · Mass Spectrometry Reviews · 2024

peer-reviewed · Mass Spectrometry Reviews · 2024. Wout Bittremieux et al. Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a…
Date 2024-11-29
Type peer-reviewed
Venue Mass Spectrometry Reviews
Publisher Wiley
Contribution review
DOI 10.1002/mas.21919
Citations (OpenAlex) 29
Venue 2-year citedness 3.62

Abstract

Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi-layer neural networks to translate from observed mass spectra to corresponding peptide sequences. Here, we describe these deep learning methods, outline procedures for evaluating their performance, and discuss the challenges in the field, both in terms of methods development and evaluation protocols.

Authors

  1. Wout Bittremieux · Indiana University, University of Antwerp, University of California San Diego
  2. Varun Ananth · University of Washington
  3. William E. Fondrie · Talus Bioscience
  4. Carlo F. Melendez · University of Washington
  5. Marina Pominova · University of Antwerp
  6. Justin Sanders · University of Washington
  7. Bo Wen · Baylor College of Medicine, University of Washington
  8. Melih Yilmaz · University of Washington
  9. William Stafford Noble · University of Washington

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