Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
preprint · bioRxiv · 2023
| Date | 2023-01-05 |
| Type | preprint |
| Venue | bioRxiv |
| Publisher | Cold Spring Harbor Laboratory |
| Contribution | post-processor |
| DOI | 10.1101/2023.01.05.522752 |
| Citations (OpenAlex) | 9 |
Peer-reviewed version: Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024-01-02, Nature Communications)
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 new de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a new 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 new 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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