Rawal Osho, Turhan Berk, Peradejordi Irene Font, Chandrasekar Shreya, Kalayci Selim, Gnjatic Sacha, Johnson Jeffrey, Bouhaddou Mehdi, Gümüş Zeynep H
Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
These authors contributed equally.
ArXiv. 2024 Dec 18:arXiv:2402.05016v4.
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/.
蛋白质磷酸化涉及一种蛋白质（激酶）对另一种蛋白质（底物）残基的可逆修饰。液相色谱 - 质谱研究正在迅速生成多种条件下的大量蛋白质磷酸化数据集。研究人员随后必须推断出负责每种底物磷酸化位点变化的激酶。然而，推断激酶 - 底物相互作用（KSI）的工具并未针对交互式探索由此产生的大型复杂网络、重要磷酸化位点和状态进行优化。因此，迫切需要一种便于对磷酸化蛋白质组学数据集进行用户友好型分析、交互式探索、可视化和交流的工具。我们展示了PhosNetVis，这是一种基于网络的工具，适用于所有计算技能水平的研究人员，通过在单个工具中简化磷酸化蛋白质组学数据分析步骤，轻松推断、生成并以二维或三维方式交互式探索KSI网络。PhostNetVis降低了研究人员快速生成高质量可视化以从其磷酸化蛋白质组学数据集中获取生物学见解的障碍。可通过以下网址获取：https://gumuslab.github.io/PhosNetVis/ 。
