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[Preprint]. 2024 Dec 18:arXiv:2402.05016v4.

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Osho Rawal  1   2 , Berk Turhan  1   3   2 , Irene Font Peradejordi  1   4 , Shreya Chandrasekar  1   4 , Selim Kalayci  1 , Sacha Gnjatic  5   6 , Jeffrey Johnson  7 , Mehdi Bouhaddou  8   9 , Zeynep H Gümüş  1   5   10
Affiliations

PhosNetVis: A web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data

Osho Rawal et al. ArXiv. .

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Abstract

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/.

Keywords: 3D visualization; CPTAC; Fast kinase-substrate enrichment analysis; interactive visualization; kinase-substrate interaction; network visualization; phosphoproteomics; phosphorylation.

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Conflict of interest statement

Declaration of interests SG reports other research funding from Boehringer-Ingelheim, Bristol-Myers Squibb, Celgene, Genentech, Regeneron, and Takeda, and consulting from Taiho Pharmaceuticals, not related to this study. All other authors declare no competing interests.

Figures

Figure 1.
Figure 1.. PhosNetVis overall architecture and main components.
Figure 2.
Figure 2.. fKSEA page snapshot.
A.) Input File Format section describes input .CSV file format for fKSEA input. B.) Adjust Parameters section enables users to adjust parameters based on their needs before running the FGSEA algorithm. C.) Upload Data & Run analysis section enables users to upload their input file from their local directory; run fKSEA; download the generated KSI network file and visualize the generated network.
Figure 3.
Figure 3.. Snapshot of the Network Visualization Input Page
(descriptive text not shown). This page allows users to upload network files for visualization. Users can upload their custom network files or customize those generated by the fKSEA page. For comparative analyses across networks, users can upload multiple files by selecting the number of datasets they want to upload. This page also displays a table that provides the required network data format. To visualize network data, users need a .CSV file with at least two columns: 1) Kinases (KinaseID) and 2) Target Nodes (TargetID) for substrates. Optionally, users can add additional columns to customize the node and edge attributes.
Figure 4.
Figure 4.. Snapshots of network visualization interface featuring the phosphorylation landscape of SARS-CoV-2 infection.
This page allows users to view and interact with the network curated from the Global Phosphorylation Landscape of SARS-CoV-2 Infection (Bouhaddou et al., 2020). Users can rotate the network, zoom in/out, pan through, see a node information by double clicking on it, or reset the view by double clicking on the background. Additionally, it provides options for the user to switch between 3D or 2D network. Users can also send their genes list to the Enrichr tool– for gene enrichment analyses, for further exploration of enriched pathways and functional gene groups. Double-clicking on any node opens an interactive table that displays detailed information on the node as shown in Panel A bottom left for node HMGA1. A) Snapshot of the 3D view of the network, including a magnified view of a CSNK2A1-central subnetwork in 3D. This subnetwork highlights phosphorylation sites known to be associated with cytoskeleton remodeling, filtered from the main network. B) Snapshot of the 2D view of the same network. In each panel, the left corner shows the control panel; the middle displays the network; the bottom right corner contains the legend. The starred section indicates the CSNK2A1 phosphorylation subnetwork, including a magnified view of the same CSNK2A1 node with select interactors in a subnetwork in 2D. These features enable comprehensive exploration and analysis of the phosphorylation events during SARS-CoV-2 infection.
Figure 5.
Figure 5.. Snapshots of interactive 2D network visualizations for CPTAC pan-cancer immune subtypes.
Kinases, and their specific altered phosphosites, their phosphorylation levels and positions in each altered substrate are clearly shown A) CD8+/IFNG+ network. Visualization clearly depicts upregulation in the global abundance of the kinase PRKACA (center) and the phospho-abundance of its substrates in red; B) CD8−/IFNG− subnetwork. The global proteomic expression of cell-cycle kinases such as CDK1 and the phospho-abundance of its substrates are upregulated in red.
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