Bidirectional de novo peptide sequencing using a transformer model
peer-reviewed · PLOS Computational Biology · 2024
| Date | 2024-02-28 |
| Type | peer-reviewed |
| Venue | PLOS Computational Biology |
| Publisher | PLoS Computational Biology |
| Contribution | algorithm |
| DOI | 10.1371/journal.pcbi.1011892 |
| Citations (OpenAlex) | 25 |
| Venue 2-year citedness | 4.07 |
Abstract
In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species.
Methods and tools
- NovoB: Bidirectional decoding
Cites (11)
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- BiATNovo: A Self-Attention based Bidirectional Peptide Sequencing Method (2023) crossref
- De novo mass spectrometry peptide sequencing with a transformer model (2022) both
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Cited by (11)
- AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing (2026) crossref
- Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides (2025) both
- Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025) both
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2025) crossref
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025) crossref
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) both
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (2024) both
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) both
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024) crossref