InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments

peer-reviewed · Nature Machine Intelligence · 2025

peer-reviewed · Nature Machine Intelligence · 2025. Kevin Eloff et al. Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum…
Date 2025-04-01
Type peer-reviewed
Venue Nature Machine Intelligence
Publisher Springer Science and Business Media LLC
Contribution algorithm
DOI 10.1038/s42256-025-01019-5
Citations (OpenAlex) 41
Venue 2-year citedness 23.00

Preprint version: De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023-08-30, bioRxiv)

Abstract

Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome.

Authors

  1. Kevin Eloff · InstaDeep Ltd
  2. Konstantinos Kalogeropoulos · Delft University of Technology, Kavli Institute of Nanoscience, Technical University of Denmark
  3. Amandla Mabona · InstaDeep Ltd
  4. Oliver Morell · Technical University of Denmark
  5. Rachel Catzel · InstaDeep Ltd
  6. Esperanza Rivera-de-Torre · Technical University of Denmark
  7. Jakob Berg Jespersen · Technical University of Denmark
  8. Wesley Williams · InstaDeep Ltd
  9. Sam P. B. van Beljouw · Delft University of Technology, Kavli Institute of Nanoscience
  10. Marcin J. Skwark · InstaDeep Ltd
  11. Andreas Hougaard Laustsen · Technical University of Denmark
  12. Stan J. J. Brouns · Delft University of Technology, Kavli Institute of Nanoscience
  13. Anne Ljungars · Technical University of Denmark
  14. Erwin M. Schoof · Technical University of Denmark
  15. Jeroen Van Goey · InstaDeep Ltd
  16. Ulrich auf dem Keller · Technical University of Denmark
  17. Karim Beguir · InstaDeep Ltd
  18. Nicolas Lopez Carranza · InstaDeep Ltd
  19. Timothy P. Jenkins · Technical University of Denmark

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