ArticlePLoS computational biology2024
Bidirectional de novo peptide sequencing using a transformer model.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets.Methods in molecular biology (Clifton, N.J.) · 2027Article
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides.Molecular & cellular proteomics : MCP · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution.Environmental microbiome · 2025Article
- In Silico Discovery and Sensory Validation of Umami Peptides in Fermented Sausages: A Study Integrating Deep Learning and Molecular Modeling.Foods (Basel, Switzerland) · 2025Article
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing.Communications biology · 2025Article
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models.Scientific data · 2024Article
- A learned score function improves the power of mass spectrometry database search.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.