Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoprote-omics datasets depend on generic, gene-centric annotations such as Gene Ontology terms which do not account for the function of a protein in a particular phosphorylation state. Analysis of phos-phoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We pro-pose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associat-ed to potential functions using a random walk on heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSite Plus and were able to associate differentially regulated sites on the same pro-teins to their previously described specific functions. We further tested the algorithm on three pre-viously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.
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