Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
peer-reviewed · Nature Communications · 2024
| Date | 2024-01-02 |
| Type | peer-reviewed |
| Venue | Nature Communications |
| Publisher | Nature Communications |
| Contribution | post-processor |
| DOI | 10.1038/s41467-023-44323-7 |
| Citations (OpenAlex) | 44 |
| Venue 2-year citedness | 15.88 |
Preprint version: Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2023-01-05, bioRxiv)
Abstract
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.
Methods and tools
- Spectralis: AA-gapped convolutional layer
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Cited by (13)
- AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing (2026) crossref
- 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
- De Novo Peptide Sequencing for Data-independent Acquisition (DIA) Using Deep Learning (2025) crossref
- Metaproteomics Beyond Databases: Addressing the Challenges and Potentials of De Novo Sequencing (2025) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) crossref
- 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) crossref
- Transforming de novo peptide sequencing by explainable AI (2024) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) crossref
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) crossref