Accounting for Digestion Enzyme Bias in Casanovo
peer-reviewed · Journal of Proteome Research · 2024
| Date | 2024-09-04 |
| Type | peer-reviewed |
| Venue | Journal of Proteome Research |
| Publisher | ACS |
| Contribution | algorithm |
| DOI | 10.1021/acs.jproteome.4c00422 |
| Citations (OpenAlex) | 9 |
| Venue 2-year citedness | 3.48 |
Abstract
A key parameter of any bottom-up proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids “K” or “R”. This bias is desirable when Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identity of the digestion enzyme alongside each observed spectrum. We then train Casanovo with data generated by using several different enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.
Methods and tools
- Casanovo: First Transformer
Cites (6)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) crossref
- Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics (2023) crossref
- De novo mass spectrometry peptide sequencing with a transformer model (2022) crossref
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) crossref
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) crossref
- De novo peptide sequencing by deep learning (2017) crossref
Cited by (6)
- Learning Fragmentation Physics or Exploiting Sequence Priors? Benchmarking Bias in Deep Learning Models for De Novo Peptide Sequencing (2026) crossref
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026) crossref
- Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025) crossref
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025) crossref
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2025) crossref
- Orthrus: an AI-powered, cloud-ready, and open-source hybrid approach for metaproteomics (2024) crossref