pUniFind: a unified large pre-trained deep learning model pushing the limit of mass spectra interpretation

TL;DR

pUniFind is a large-scale, multimodal deep learning model that significantly improves peptide-spectrum matching and de novo sequencing in proteomics, outperforming traditional methods across various datasets.

Contribution

It introduces the first unified, pre-trained deep learning framework for mass spectra interpretation that integrates scoring and sequencing, with extensive training on over 100 million spectra.

Findings

42.6% increase in peptide identifications in immunopeptidomics

Identifies 60% more PSMs across 1,300 modifications

Recovers 38.5% additional peptides, including novel genome-mapped peptides

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.