Abstract

Protein phosphorylation is a key regulator of signaling, with mass spectrometry (MS) based phosphoproteomics serving as the premier technology for its analysis. However, phosphorylation profiling is hindered by acquisition biases: Data-Dependent Acquisition (DDA) suffers from stochastic undersampling and missing values, while Data-Independent Acquisition (DIA) faces computational bottlenecks and inefficiencies from vast spectral libraries. We present PhosSight, a unified deep learning framework designed to augment identification depth and accelerate search efficiency. PhosSight features PhosDetect, a model that explicitly encodes phosphorylation-specific physicochemical features to accurately predict peptide detectability. For DDA, PhosSight leverages predicted retention time, fragment intensity, and detectability to refine site localization and rescoring, recovering marginal, low-abundance spectra. For DIA, PhosSight utilizes detectability-guided library pruning to remove non-detectable noise, accelerating search speeds without compromising sensitivity. Benchmarking on synthetic and real-world datasets confirms PhosSight's superior performance in both modes. Applying PhosSight to a large-scale Uterine Corpus Endometrial Carcinoma (UCEC) cohort improved data completeness and expanded the quantifiable phosphoproteome. This enhanced completeness enabled the discovery of novel prognosis-associated kinase targets, such as MARK2, underscoring PhosSight as a powerful tool for biological discovery in precision oncology. Trial Registration : Not applicable. This study did not prospectively assign human participants to any health-related intervention and therefore does not constitute a clinical trial requiring registration.