# DeepNovoV2: Better de novo peptide sequencing with deep learning

arXiv: https://arxiv.org/abs/1904.08514 · HTML: https://mlsanity.com/paper/1904.08514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data](https://mlsanity.com/paper/2207.02980/) (2022)
- [DePS: An improved deep learning model for de novo peptide sequencing](https://mlsanity.com/paper/2203.08820/) (2022)

## Abstract (arXiv)

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.