Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
peer-reviewed · Nature Methods · 2019
peer-reviewed · Nature Methods · 2019. Siegfried Gessulat et al.
| Date | 2019-05-27 |
| Type | peer-reviewed |
| Venue | Nature Methods |
| Publisher | Springer Science and Business Media LLC |
| Contribution | adjacent |
| DOI | 10.1038/s41592-019-0426-7 |
| Citations (OpenAlex) | 994 |
| Venue 2-year citedness | 20.00 |
Methods and tools
- Prosit: Deep-learning (bidirectional RNN + attention) predictor of peptide fragment-ion intensities and retention times: widely used to validate and rescore de novo sequencing candidates against predicted spectra.
Cited by (23)
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- 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
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025) crossref
- pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation (2025) semanticscholar
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025) crossref
- PSMtags improve peptide sequencing and throughput in sensitive proteomics (2025) crossref
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) both
- PepGo: a deep learning and tree search-based model for de novo peptide sequencing (2025) crossref
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) both
- pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads (2024) both
- MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer (2024) both
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024) both
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) both
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2023) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023) crossref
- Deep Learning in Proteomics (2020) both
- Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework (2019) both