DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics
This work addresses a bottleneck in proteomic profiling for researchers by providing a more generalizable and accurate tool, though it is incremental as it builds on existing contrastive learning and encoder-decoder methods.
The paper tackles the problem of overfitting and lack of generalizability in data-independent acquisition mass spectrometry (DIA-MS) analysis by introducing DIA-CLIP, a pre-trained model that shifts from semi-supervised training to universal cross-modal representation learning, resulting in up to a 45% increase in protein identification and a 12% reduction in entrapment identifications.
Data-independent acquisition mass spectrometry (DIA-MS) has established itself as a cornerstone of proteomic profiling and large-scale systems biology, offering unparalleled depth and reproducibility. Current DIA analysis frameworks, however, require semi-supervised training within each run for peptide-spectrum match (PSM) re-scoring. This approach is prone to overfitting and lacks generalizability across diverse species and experimental conditions. Here, we present DIA-CLIP, a pre-trained model shifting the DIA analysis paradigm from semi-supervised training to universal cross-modal representation learning. By integrating dual-encoder contrastive learning framework with encoder-decoder architecture, DIA-CLIP establishes a unified cross-modal representation for peptides and corresponding spectral features, achieving high-precision, zero-shot PSM inference. Extensive evaluations across diverse benchmarks demonstrate that DIA-CLIP consistently outperforms state-of-the-art tools, yielding up to a 45% increase in protein identification while achieving a 12% reduction in entrapment identifications. Moreover, DIA-CLIP holds immense potential for diverse practical applications, such as single-cell and spatial proteomics, where its enhanced identification depth facilitates the discovery of novel biomarkers and the elucidates of intricate cellular mechanisms.