Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications
peer-reviewed · Journal of Proteome Research · 2017
| Date | 2017-01-23 |
| Type | peer-reviewed |
| Venue | Journal of Proteome Research |
| Publisher | American Chemical Society (ACS) |
| Contribution | algorithm |
| DOI | 10.1021/acs.jproteome.6b00716 |
| Citations (OpenAlex) | 37 |
| Venue 2-year citedness | 3.83 |
Abstract
De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.
Methods and tools
- Open-pNovo: pNovo with thousands of PTMs
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Cited by (15)
- A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data (2026) crossref
- pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation (2025) semanticscholar
- De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics (2024) crossref
- A Handle on Mass Coincidence Errors in De Novo Sequencing of Antibodies by Bottom-up Proteomics (2024) both
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) both
- Highly Robust de Novo Full-Length Protein Sequencing (2021) both
- Full-length Protein Sequencing Based on Continuous Digestion Using Non-specific Proteases (2021) crossref
- De novo sequencing of proteins by mass spectrometry (2020) both
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) both
- Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics (2019) both
- Antioxidative Effects and Mechanism Study of Bioactive Peptides from Defatted Walnut ( Juglans regia L. ) Meal Hydrolysate (2019) both
- Quick and clean: Cracking sentences encoded in E. coli by LC–MS/MS, de novo sequencing, and dictionary search (2019) both
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) crossref
- De Novo Sequencing of Proteins and Peptides: Algorithms, Applications, Perspectives (2018) crossref
- pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization (2017) crossref