DIA-CLIP：一种用于零样本DIA质谱的通用表示学习框架
DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics
AI总结：
DIA-CLIP通过预训练模型实现跨模态表示学习，提升零样本PSM推断精度，适用于多领域蛋白质组学研究。
摘要：
数据无关获取质谱（DIA-MS）已成为蛋白质组学分析和大规模系统生物学研究的基石，提供了前所未有的深度和可重复性。然而，当前的DIA分析框架需要在每次运行中进行半监督训练以重新评分肽谱匹配（PSM）。这种做法容易导致过拟合，并且在不同物种和实验条件下缺乏通用性。本文提出了DIA-CLIP，一种预训练模型，将DIA分析范式从半监督训练转向通用的跨模态表示学习。通过整合双编码器对比学习框架与编码器-解码器架构，DIA-CLIP建立了肽和相应光谱特征的统一跨模态表示，实现了高精度的零样本PSM推断。在多种基准上的广泛评估表明，DIA-CLIP在性能上始终优于最先进的工具，其在蛋白质鉴定方面提升了高达45%，同时在捕获鉴定方面减少了12%。此外，DIA-CLIP在单细胞和空间蛋白质组学等实际应用中具有巨大潜力，其增强的鉴定深度有助于发现新的生物标志物并揭示复杂的细胞机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
