Improvements to CasaNovo, a deep learning de novo peptide sequencer

preprint · bioRxiv · 2025

preprint · bioRxiv · 2025. Gwenneth Straub et al. Casanovo is a state-of-the-art deep learning model for de novo peptide sequencing from mass spectrometry…
Date 2025-07-25
Type preprint
Venue bioRxiv
Publisher Cold Spring Harbor Laboratory
Contribution algorithm
DOI 10.1101/2025.07.25.666826
Citations (OpenAlex) 2

Peer-reviewed version: Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025-12-30, Journal of Proteome Research)

Abstract

Casanovo is a state-of-the-art deep learning model for de novo peptide sequencing from mass spectrometry proteomics data. Here we report on a series of enhancements to Casanovo, aimed at improving the interpretability of the scores assigned to predicted peptides, generalizing the software for use in database search, speeding up training and prediction runtimes, and providing workflows and visualization tools to facilitate adoption of Casanovo and interpretation of its results. Our goal is to make Casanovo accurate and easy to use for applications such as metaproteomics, antibody sequencing, immunopeptidomics, and discovery of novel peptide sequences in standard proteomics analyses. Casanovo is available as open source at https://github.com/Noble-Lab/casanovo.

Authors

  1. Gwenneth Straub · University of Washington
  2. Varun Ananth · University of Washington
  3. William E. Fondrie · Talus Bioscience
  4. Chris Hsu · University of Washington
  5. Daniela Klaproth-Andrade · Technical University of Munich
  6. Michael Riffle · University of Washington
  7. Justin Sanders · University of Washington
  8. Bo Wen · Baylor College of Medicine, University of Washington
  9. Lingwen Xu · University of Washington
  10. Melih Yilmaz · University of Washington
  11. Michael J. MacCoss · University of Washington
  12. Sewoong Oh · University of Washington
  13. Wout Bittremieux · Indiana University, University of Antwerp, University of California San Diego
  14. William Stafford Noble · University of Washington

Methods and tools

Cites (11)

Cited by (4)

Seen in the charts

Back to the full map

Back to top