De novo mass spectrometry peptide sequencing with a transformer model

ML conference · ICML 2022 · 2022

ML conference · ICML 2022 · 2022. Melih Yilmaz et al. Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex…
Date 2022-07-17
Type ML conference
Venue ICML 2022
Publisher PMLR
Contribution algorithm
Link https://proceedings.mlr.press/v162/yilmaz22a.html
Citations (OpenAlex) 61

Preprint version: De novo mass spectrometry peptide sequencing with a transformer model (2022-02-07, bioRxiv)

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 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.

Authors

  1. Melih Yilmaz · University of Washington
  2. William E. Fondrie · Talus Bioscience
  3. Wout Bittremieux · Indiana University, University of Antwerp, University of California San Diego
  4. Sewoong Oh · University of Washington
  5. William Stafford Noble · University of Washington

Methods and tools

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