Alternatively, the previous elegant study showed it is feasible to build a direct DIA library from DIA data alone for deep phosphoproteome profiling. Although project-specific DDA libraries afford a higher proteome coverage (i.e., covering a larger number of protein and peptide identifications) than other experimental libraries, they are built at the expense of time, sample, and considerable efforts with pre-fractionation 10. In almost all reported DIA phophoproteomic analysis, a project-specific DDA library was built through DDA analysis of extensively pre-fractionated or repeatedly injected samples 3, 6– 9. However, the current DIA phosphoproteomic workflow faces a significant limitation which is the need of a high-quality spectral library to be constructed prior to data processing. has demonstrated that DIA phosphoproteomics achieves a larger dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification than DDA phosphoproteomics 3. Importantly, a benchmark study by Olsen J et al. With the advent of data-independent acquisition (DIA) to enable proteome profiling of large cohorts of samples with superior quantification accuracy and reproducibility 4, 5, DIA-based phosphoproteomics has emerged as a powerful technology for cell signaling study 6, proteogenomic characterization of clinical cancer tissues 7 and anti-viral drug discovery 8. However, conventional phosphoproteomics based on data-dependent acquisition (DDA) often suffers from limited throughput and low reproducibility due to the current MS sequencing speed and semi-stochastic sampling of DDA 3. Mass spectrometry (MS)-based phosphoproteomics has become the method of choice for the genome-wide study of protein phosphorylation and dynamic cell signaling 2. Protein phosphorylation is a widespread post-translational modification (PTM) that regulates essentially all cellular signaling networks 1.
#Apa itu filezilla ftp client Offline#
DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation. 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.