pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Deep learning has advanced mass spectrometry data interpretation, yet most
models remain feature extractors rather than unified scoring frameworks. We
present pUniFind, the first large-scale multimodal pre-trained model in
proteomics that integrates end-to-end peptide-spectrum scoring with open,
zero-shot de novo sequencing. Trained on over 100 million open search-derived
spectra, pUniFind aligns spectral and peptide modalities via cross modality
prediction and outperforms traditional engines across diverse datasets,
particularly achieving a 42.6 percent increase in the number of identified
peptides in immunopeptidomics. Supporting over 1,300 modifications, pUniFind
identifies 60 percent more PSMs than existing de novo methods despite a
300-fold larger search space. A deep learning based quality control module
further recovers 38.5 percent additional peptides including 1,891 mapped to the
genome but absent from reference proteomes while preserving full fragment ion
coverage. These results establish a unified, scalable deep learning framework
for proteomic analysis, offering improved sensitivity, modification coverage,
and interpretability.
