Biology subjects

Yaron-Barir, T. M.

Publications and source records attributed to Yaron-Barir, T. M..

6 recordsLinked to original sources

bioRxiv2025

A Structure-Guided Kinase-Transcription Factor Interactome Atlas Reveals Docking Landscapes of the Kinome

AbstractProtein kinases orchestrate cellular processes through phosphorylation, yet the structural basis for their specific binding partner interactions remains largely unmapped. Here, we present a structure-guided atlas of the human and Drosophila kinome, built by applying a new interface-aware scoring framework (iLIS) to AlphaFold-Multimer predictions. The resulting atlas recapitulates hallmark sequence preferences, confirms previously reported and functionally related protein-protein interactions, and uncovers unrecognized docking interactions. Notably, our analysis predicts a potentially widespread docking motif on homeodomain transcription factors that mediates interactions with basophilic kinases. Furthermore, we map putative allosteric interaction hotspots across the kinome and provide proof-of-concept evidence that targeting these surfaces can inhibit kinase activity. Finally, we demonstrate the physiological utility of the atlas by identifying a novel regulatory mechanism between Sgg/GSK3 and Hnf4 that controls lipid metabolism in vivo. This resource provides a blueprint for dissecting signaling networks and for the rational design of docking-site-specific kinase modulators.

bioinformatics↗

bioRxiv2025

Atlas of the Bacterial Serine-Threonine Kinases expands the functional diversity of the kinome

Bacterial serine-threonine protein kinases (STKs) regulate diverse cellular processes associated with cell growth, virulence, and pathogenicity. They are evolutionarily related to the druggable eukaryotic STKs. However, an incomplete knowledge of how bacterial STKs differ from their eukaryotic counterparts and how they have diverged to regulate diverse bacterial signaling functions presents a bottleneck in targeting them for drug discovery efforts. Here, we classified over 300,000 bacterial STK sequences from the NCBI RefSeq non-redundant and UniProt protein databases into 35 canonical and seven non-canonical (pseudokinase) families based on the patterns of evolutionary constraints in the conserved catalytic domain and flanking regulatory domains. Through statistical comparisons, we identified distinguishing features of bacterial STKs, including a distinctive arginine residue in a regulatory helix (C-Helix) that dynamically couples ATP and substrate binding lobes of the kinase domain. Biochemical and peptide-library screens demonstrated that constrained residues contribute to substrate specificity and kinase activation in the Mycobacterium tuberculosis kinase PknB. Collectively, these findings open new avenues for investigating bacterial STK functions in cellular signaling and for the development of selective bacterial STK inhibitors.

bioinformatics↗

bioRxiv2024

Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT

Dysregulation of intracellular signaling networks underpins cancer. However, a systems-level elucidation of how signaling networks within distinct cell subpopulations drive cancer progression in vivo has been unattainable due to technical limitations. We developed INSIGHT (INvestigating SIGnaling network of specific cell subpopulation in Heterogeneous Tissue), a new platform technology combining fluorescence-activated cell sorting with ultra-sensitive mass spectrometry to enable phosphoproteomic characterization of rare and discrete cell subpopulations from fixed tissues. We demonstrated the broad utility of INSIGHT by analyzing the oligodendroglial cell-specific signaling network in the mouse brain. We then applied INSIGHT to investigate the rare, disseminated tumor cell subpopulation in glioblastoma patient-derived xenograft models. INSIGHT uncovered a global rewiring of signaling networks with tumor cell dissemination, marked by a transition from proliferation-associated signaling in the primary tumor cells to signaling associated with postsynapse, neuronal migration, and ion homeostasis in disseminated tumor cells. We reveal interconnections between signaling circuitries within the networks, with numerous proteins, including GluA2, exhibiting altered phosphorylation without protein expression changes, emphasizing the role of post-translational modifications in glioblastoma dissemination. We validated key phosphorylation changes and inferred differentially active kinases with tumor spread to offer new systems-level insights into glioblastoma dissemination mechanisms in vivo. INSIGHT is generally applicable to a wide range of biological systems without genetic engineering and provides quantitative phosphorylation and protein expression data for selected cell subpopulations from heterogeneous tissues.

systems biology↗

bioRxiv2024

PSKH1 kinase activity is differentially modulated via allosteric binding of Ca2+ sensor proteins

Protein Serine Kinase H1 (PSKH1) was recently identified as a crucial factor in kidney development and is overexpressed in prostate, lung and kidney cancers. However, little is known about PSKH1 regulatory mechanisms, leading to its classification as a "dark" kinase. Here, we used biochemistry and mass spectrometry to define PSKH1s consensus substrate motif, protein interactors, and how interactors, including Ca2+ sensor proteins, promote or suppress activity. Intriguingly, despite the absence of a canonical Calmodulin binding motif, Ca2+-Calmodulin activated PSKH1 while, in contrast, the ER-resident Ca2+ sensor of the CREC family, Reticulocalbin-3, suppressed PSKH1 catalytic activity. In addition to antagonistic regulation of the PSKH1 kinase domain by Ca2+ sensing proteins, we identified UNC119B as a protein interactor that activates PSKH1 via direct engagement of the kinase domain. Our findings identify complementary allosteric mechanisms by which regulatory proteins tune PSKH1s catalytic activity, and raise the possibility that different Ca2+ sensors may act more broadly to tune kinase activities by detecting and decoding extremes of intracellular Ca2+ concentrations.

biochemistry↗

bioRxiv2024

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/601117v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@44de1org.highwire.dtl.DTLVardef@9510a3org.highwire.dtl.DTLVardef@775225org.highwire.dtl.DTLVardef@1b2256f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

cancer biology↗