Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach.

By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics.

DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. The DeepPhospho web server is available at. The source code, documents, and related scripts are stored on GitHub ( ) and Zenodo (10.5281/zenodo.5594736) 46. Source data are provided with this paper.ĭeepPhospho is written in Python and uses PyTorch to implement deep neural networks. Databases used in this work are: UniProt 45 ( ), EPSD 25 ( ), PhosphoSitePlus 41 ( ), PhosPhAt 38 ( ), Reactome 42 ( ).

All MS raw data were downloaded from PRIDE FTP site via FileZilla (v3.51.0) or from jPOST via Mozilla Firefox. Public MS data used in this work are as follows: PXD006637 (mouse brain DDA) 29, PXD019113 (Vero E6 DIA) 8, PXD013453 (yeast R2P2) 6, PXD014525 (RPE1, two-proteome) 3, PXD017476 (U2OS) 9, PXD009227 (U-87 DDA) 1, PXD019797 (human synthetic phosphopeptide dataset) 27, PXD004573 (yeast synthetic phosphopeptide dataset) 28. Raw DDA and PRM data from synthetic phosphopeptide analysis, DeepPhospho generated spectral libraries, and DIA search results have been deposited to the ProteomeXchange Consortium 43 via the iProX 44 partner repository with the dataset identifier IPX0003513000, which is equivalent to PXD028601.

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