ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing
peer-reviewed · AAAI 2024 · 2024
| Date | 2024-03-24 |
| Type | peer-reviewed |
| Venue | AAAI 2024 |
| Publisher | AAAI Press |
| Contribution | algorithm |
| DOI | 10.1609/aaai.v38i1.27765 |
| Citations (OpenAlex) | 22 |
Abstract
De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing.
Methods and tools
- ContraNovo: Contrastive learning
Cited by (12)
- Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads (2026) crossref
- 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) crossref
- Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025) 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
- 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
- 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