DeepNovoV2: Better de novo peptide sequencing with deep learning
preprint · arXiv · 2019
| Date | 2019-04-17 |
| Type | preprint |
| Venue | arXiv |
| Publisher | arXiv |
| Contribution | algorithm |
| DOI | 10.48550/arXiv.1904.08514 |
| Citations (OpenAlex) | 15 |
Abstract
Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2, the state-of-the-art model for peptide sequencing. In DeepNovoV2, a spectrum is directly represented as a set of (m/z, intensity) pairs, therefore it does not suffer from the accuracy-speed/memory trade-off problem. The model combines an order invariant network structure (T-Net) and recurrent neural networks and provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on a wide variety of data from different species show that DeepNovoV2 outperforms previous state-of-the-art methods, achieving 13.01-23.95\% higher accuracy at the peptide level.
Methods and tools
- DeepNovo V2: Improved CNN+LSTM model
Cited by (5)
- PepGo: a deep learning and tree search-based model for de novo peptide sequencing (2025) semanticscholar
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) semanticscholar
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (2023) both
- DePS: An improved deep learning model for de novo peptide sequencing (2022) semanticscholar