Nature Communications
17 papers in the catalog
17 de novo peptide sequencing papers published in Nature Communications, catalogued with authors, methods and citation counts.
| 2-year mean citedness | 17.60 |
| h-index | 754 |
| Works indexed | 92809 |
From OpenAlex. The 2-year mean citedness is computed the same way as the Journal Impact Factor, but over the open citation graph.
Papers
- High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques (2026, peer-reviewed)
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics (2026, peer-reviewed)
- Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates (2026, peer-reviewed)
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning (2026, peer-reviewed)
- XA-Novo: an accurate and high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures (2026, peer-reviewed)
- Ultra-sensitive metaproteomics redefines the dark metaproteome, uncovering host-microbiome interactions and drug targets in intestinal diseases (2025, peer-reviewed)
- Interferon-α promotes HLA-B-restricted presentation of conventional and alternative antigens in human pancreatic β-cells (2025, peer-reviewed)
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025, peer-reviewed)
- De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination (2024, peer-reviewed)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024, peer-reviewed)
- MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer (2024, peer-reviewed)
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing (2024, peer-reviewed)
- Accurate de novo peptide sequencing using fully convolutional neural networks (2023, peer-reviewed)
- Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023, peer-reviewed)
- A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics (2022, peer-reviewed)
- Protein identification by 3D OrbiSIMS to facilitate in situ imaging and depth profiling (2020, peer-reviewed)
- Evolutionary instability of CUG-Leu in the genetic code of budding yeasts (2018, peer-reviewed)