Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways.
Panshak P DakupSong FengTujin ShiJon M JacobsH Steven WileyWei-Jun QianPublished in: Molecules (Basel, Switzerland) (2023)
Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.
Keyphrases
- mass spectrometry
- signaling pathway
- protein kinase
- protein protein
- high resolution
- amino acid
- binding protein
- single cell
- liquid chromatography
- multiple sclerosis
- pi k akt
- epithelial mesenchymal transition
- small molecule
- machine learning
- transcription factor
- high throughput
- mesenchymal stem cells
- induced apoptosis
- artificial intelligence
- endoplasmic reticulum stress
- deep learning
- label free