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De novo mass spectrometry peptide sequencing with a transformer model

Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Sewoong Oh, View ORCID ProfileWilliam Stafford Noble
doi: https://doi.org/10.1101/2022.02.07.479481
Melih Yilmaz
1Paul G. Allen School of Computer Science and Engineering, University of Washington
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William E. Fondrie
2Talus Bioscience
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Wout Bittremieux
3Skaggs School of Pharmacy and Pharmaceutical Science, University of California San Diego
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Sewoong Oh
1Paul G. Allen School of Computer Science and Engineering, University of Washington
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William Stafford Noble
4Department of Genome Sciences, University of Washington
1Paul G. Allen School of Computer Science and Engineering, University of Washington
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  • ORCID record for William Stafford Noble
  • For correspondence: noble@gs.washington.edu
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Abstract

Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids—the peptide—responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with out-of-distribution samples, i.e., spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/Noble-Lab/casanovo

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 09, 2022.
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De novo mass spectrometry peptide sequencing with a transformer model
Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Sewoong Oh, William Stafford Noble
bioRxiv 2022.02.07.479481; doi: https://doi.org/10.1101/2022.02.07.479481
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De novo mass spectrometry peptide sequencing with a transformer model
Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Sewoong Oh, William Stafford Noble
bioRxiv 2022.02.07.479481; doi: https://doi.org/10.1101/2022.02.07.479481

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