Author

L. Käll

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#machine learning Preprint Oct 2026

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo sequencing of peptides. This invariance exploits the physical relationship between precursor properties (mass and charge) and fragment-ion evidence, without requiring peptid...

Alfred Nilsson, Joel Lapin, Samuel H. Payne et al. · 0 citations

Open access Aug 2026

Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads

Reading a protein’s sequence from tandem mass spectra without a reference is limited by single-spectrum accuracy, most acutely across the hypervariable complementaritydetermining regions of antibodies. Broadly specific proteases tile a protein with long, overlapping peptides, so every residue is covered by many indepen...

Alfred Nilsson, Emil Sporre, Douwe Schulte et al. · bioRxiv · 0 citations