Development of Selected Reaction Monitoring Methods to Systematically Quantify Kinase Abundance and Phosphorylation Stoichiometry in Human Samples

Systematic Evaluation of Chromatographic Peak Quality for Targeted Mass Spectrometry via Variational Autoencoder

Targeted mass spectrometry is a powerful technique for quantifying specific proteins or metabolites in complex biological samples. Accurate peak picking is a critical step as it determines the absolute abundance of each analyte by integrating the area under the picked peaks. Although automated software exists for handling such complex tasks, manual intervention is often required to rectify potential errors like misclassification or mis-picking events, which can significantly affect quantification accuracy. Therefore, it is necessary to develop objective scoring functions to evaluate peak-picking results and to identify problematic cases for further inspection. In this study, we present targeted mass spectrometry quality encoder (TMSQE), a data-driven scoring function that summarizes peak quality in three types: transition level, peak group level, and consistency level across samples. Through unsupervised learning from large data sets containing 1,703,827 peak groups, TMSQE establishes a reliable standard for systematic and objective evaluations of chromatographic peak quality in targeted mass spectrometry. TMSQE shows a high degree of consistency with expert experiences and can efficiently capture problematic cases after the automated software. Furthermore, we demonstrate the generalizability of TMSQE by successfully applying it to various data sets, including both peptide and metabolite data sets. Our proposed scoring approach provides a reliable solution for consistent and accurate peak quality evaluation, facilitating peak quality control for targeted mass spectrometry.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.