De novo mass spectrometry peptide sequencing with a transformer model
ML conference · ICML 2022 · 2022
| Date | 2022-07-17 |
| Type | ML conference |
| Venue | ICML 2022 |
| Publisher | PMLR |
| Contribution | algorithm |
| Link | https://proceedings.mlr.press/v162/yilmaz22a.html |
| Citations (OpenAlex) | 55 |
Preprint version: De novo mass spectrometry peptide sequencing with a transformer model (2022-02-07, bioRxiv)
Abstract
Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids—the peptide—responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.
Methods and tools
- Casanovo: First Transformer
Cites (9)
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Cited by (20)
- Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics (2026) crossref
- Transformer Architectures for De Novo Peptide Sequencing and Peptide Property Prediction in LC–MS/MS Proteomics (2026) crossref
- Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads (2026) crossref
- DLDN-Bench: A Benchmark Framework for Deep Learning de Novo Peptide Sequencing in Proteomics (2026) crossref
- LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (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
- Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction (2025) crossref
- DyCoNovo: a De Novo Peptide Prediction Model Based on Dynamic Convolution and Phased Contrastive Learning (2025) crossref
- RT-GCTnovo: A Peptide De Novo Sequencing Model Incorporating Gated Multi-scale Features and Dynamic Mass Masks (2025) crossref
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024) crossref
- BERT model for de novo protein sequencing using tandem mass spectra (2024) crossref
- De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination (2024) crossref
- A Handle on Mass Coincidence Errors in De Novo Sequencing of Antibodies by Bottom-up Proteomics (2024) crossref
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) crossref
- NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024) semanticscholar
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) crossref
- SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq (2023) crossref
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly (2023) crossref