Sequence-to-sequence translation from mass spectra to peptides with a transformer model

peer-reviewed · Nature Communications · 2024

peer-reviewed · Nature Communications · 2024. Melih Yilmaz et al. A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that…
Date 2024-07-30
Type peer-reviewed
Venue Nature Communications
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s41467-024-49731-x
Citations (OpenAlex) 117
Venue 2-year citedness 17.60

Preprint version: Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023-01-03, bioRxiv)

Abstract

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information-de novo peptide sequencing-is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo’s superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

Authors

  1. Melih Yilmaz · University of Washington
  2. William E. Fondrie · Talus Bioscience
  3. Wout Bittremieux · Biomedical Informatics Research Center Antwerp, Indiana University, University of Antwerp, University of California San Diego, University of Washington
  4. Carlo F. Melendez · University of Washington
  5. Rowan Nelson · University of Washington
  6. Varun Ananth · University of Washington
  7. Sewoong Oh · University of Washington
  8. William Stafford Noble · University of Washington

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