Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases

PTPN23-dependent activation of PI3KC2α is a therapeutic vulnerability of BRAF-mutant cancers

BRAF mutations drive initiation and progression of various tumors. While BRAF inhibitors are effective in BRAF-mutant melanoma patients, intrinsic or acquired resistance to these therapies is common. Here, we identify non-receptor-type protein tyrosine phosphatase 23 (PTPN23) as an alternative effective target in BRAF-mutant cancer cells. Silencing PTPN23 selectively kills BRAF-mutant melanoma cells but not those with wild-type BRAF. Mechanistically, PTPN23, a catalytically inactive phosphatase, intriguingly induces WNK3-mediated phosphorylation of phosphoinositide 3-kinase class II alpha (PI3KC2α) at serine 329, enhancing its catalytic activity. This activation promotes production of PI(3,4)P2 and subsequent AKT2 activation at endosomes to support cell survival. Genetic or pharmacological targeting of the PTPN23-PI3KC2α-AKT2 signaling axis, alone or in combination with BRAF inhibitors, effectively inhibits the growth of BRAF-mutant melanoma and other cancers in vitro and in vivo. We also demonstrate that melanocyte-specific knockout of PTPN23 significantly inhibits BRAFV600E-driven melanomagenesis. Altogether, our findings demonstrate that targeting PTPN23/PI3KC2α offers a new and viable therapeutic strategy for BRAF-mutant cancers.

A curated tissue-specific proteome, phosphoproteome, and kinome map of Drosophila melanogaster with an integrated outlook in circadian physiology

The fruit fly Drosophila melanogaster is a simple multicellular model system widely used in biomedical research. Here, we aimed to curate a comprehensive tissue and organ-specific proteome, phosphoproteome, and kinome atlas of D. melanogaster. Using information from published literature and databases, we have systematically curated the protein expression profiles, phosphorylation patterns, and the associated kinases and phosphatases in 11 tissue types across the different developmental stages and mature D. melanogaster and its derived cell lines. Gene annotation and pathway enrichment analysis were performed using the DAVID. Protein-protein interaction analysis was carried out using STRING, BioGrid, OmniPath, and InWeb-IM. Drosophila kinase and phosphatase gene orthologs in humans and mice were identified through the FlyBase database, utilizing the DRSC integrative ortholog prediction tool. We mapped a total of 18,377 proteins, 9021 phosphoproteins, 433 kinases, and 141 phosphatases in D. melanogaster. Subsequent categorization of the proteins into different tissue types indicated the enrichment of some tissue-specific pathways and expression clusters. We identified 295 and 289 Drosophila kinase orthologs in humans and mice through an ortholog screening. In the rhythmicity analysis, we observed 24-hour periodicity in 5289 transcripts, 678 proteins, 437 phosphoproteins, 166 kinases, and 89 phosphatases. The findings of our study are integrated as a convenient resource for understanding the proteome-level organizations in Drosophila, their oscillating expression, and their tissue-specific roles in maintaining cellular and physiological functions. We anticipate that this study will help to enhance the systems-level analysis of D. melanogaster as a model organism.

Effect of Kinases in Extracellular Vesicles from HIV-1-Infected Cells on Bystander Cells

As of 2023, there were 39.9 million people living with Human Immunodeficiency Virus type 1 (HIV-1). Although great strides have been made in treatment options for HIV-1, and our understanding of the HIV-1 life cycle has vastly improved since the start of this global health crisis, a functional cure remains elusive. One of the main barriers to a cure is latency, which allows the virus to persist despite combined antiretroviral therapy (cART). Recently, we have found that exosomes, which are small, membrane-enclosed particles released by virtually all cell types and known to mediate intercellular communication, caused an increase in RNA Polymerase II loading onto the HIV-1 promoter. This resulted in the production of both short- and long-length viral transcripts in infected cells under cART. This current study examines the effects of exosome-associated kinases on bystander cells. The phospho-kinase profiling of exosomes revealed differences in the kinase payload of exosomes derived from uninfected and HIV-1-infected cells, with CDK10, GSK3β, and MAPK8 having the largest concentration differences. These kinases were shown to be biologically active and capable of phosphorylating substrates, and they modulated changes in the cell cycle dynamics of exposed cells. Given the relevance of such effects for the immune response, our results implicate exosome-associated kinases as new possible key contributors to HIV-1 pathogenesis that affect bystander cells. These findings may guide new therapeutic avenues to improve the current antiretroviral treatment regimens.

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling

Protein-protein interactions (PPIs) underlie most biological functions. Devastating human conditions like cancers, neurological disorders, and infections, hijack PPI networks to initiate disease, and to drive disease progression. Understanding precisely how diseases remodel PPI networks can, therefore, help clarify disease mechanisms and identify therapeutic targets. Protein kinases control most cellular processes through protein phosphorylation. The 518 human kinases, known as the kinome, are frequently dysregulated in disease and highly druggable with ATP-competitive inhibitors. Kinase activity, localization, and substrate recognition are regulated by dynamic PPI networks composed of scaffolding and adapter proteins, other signaling enzymes like small GTPases and E3 ligases, and phospho-substrates. Accordingly, mapping kinase PPI networks can help determine kinome activation states, and, in turn, cellular activation states; this information can be used for studying kinase-mediated cell signaling, and for prioritizing kinases for drug discovery. Previously, we have developed a high-throughput method for kinome PPI mapping based on mass spectrometry (MS)-based chemoproteomics that we named kinobead competition and correlation analysis (kiCCA). Here, we introduce 2 nd generation (gen) kiCCA which utilizes data-independent acquisition (dia) with parallel accumulation serial fragmentation (PASEF) MS and a re-designed CCA algorithm with improved selection criteria and the ability to predict multiple kinase interaction partners of the same proteins. Using neuroblastoma cell line models of the noradrenergic-mesenchymal transition (NMT), we demonstrate that 2 nd gen kiCCA (1) identified 6.1-times more kinase PPIs in native cell extracts compared to our 1 st gen approach, (2) determined kinase-mediated signaling pathways that underly the neuroblastoma NMT, and (3) accurately predicted pharmacological targets for manipulating NMT states. Our 2 nd gen kiCCA method is broadly useful for cell signaling research and kinase drug discovery.

Discovery and significance of protein-protein interactions in health and disease

The identification of individual protein-protein interactions (PPIs) began more than 40 years ago, using protein affinity chromatography and antibody co-immunoprecipitation. As new technologies emerged, analysis of PPIs increased to a genome-wide scale with the introduction of intracellular tagging methods, affinity purification (AP) followed by mass spectrometry (MS), and co-fractionation MS (CF-MS). Now, combining the resulting catalogs of interactions with complementary methods, including crosslinking MS (XL-MS) and cryogenic electron microscopy (cryo-EM), helps distinguish direct interactions from indirect ones within the same or between different protein complexes. These powerful approaches and the promise of artificial intelligence applications like AlphaFold herald a future where PPIs and protein complexes, including energy-driven protein machines, will be understood in exquisite detail, unlocking new insights in the contexts of both basic biology and disease.

Synthesis and evaluation of chemical linchpins for highly selective CK2α targeting

Casein kinase-2 (CK2) are serine/threonine kinases with dual co-factor (ATP and GTP) specificity, that are involved in the regulation of a wide variety of cellular functions. Small molecules targeting CK2 have been described in the literature targeting different binding pockets of the kinase with a focus on type I inhibitors such as the recently published chemical probe SGC-CK2-1. In this study, we investigated whether known allosteric inhibitors binding to a pocket adjacent to helix αD could be combined with ATP mimetic moieties defining a novel class of ATP competitive compounds with a unique binding mode. Linking both binding sites requires a chemical linking moiety that would introduce a 90-degree angle between the ATP mimetic ring system and the αD targeting moiety, which was realized using a sulfonamide. The synthesized inhibitors were highly selective for CK2 with binding constants in the nM range and low micromolar activity. While these inhibitors need to be further improved, the present work provides a structure-based design strategy for highly selective CK2 inhibitors.

A web portal for exploring kinase-substrate interactions

Interactions between protein kinases and their substrates are critical for the modulation of complex signaling pathways. Currently, there is a large amount of information available about kinases and their substrates in disparate public databases. However, these data are difficult to interpret in the context of cellular systems, which can be facilitated by examining interactions among multiple proteins at once, such as the network of interactions that constitute a signaling pathway. We present KiNet, a user-friendly web portal that integrates and shares information about kinase-substrate interactions from multiple databases of post-translational modifications. KiNet enables the visual exploration of these interactions in systems contexts, such as pathways, domain families, and custom protein set inputs, in an interactive fashion. We expect KiNet to be useful as a knowledge discovery tool for kinase-substrate interactions, and the aggregated KiNet dataset to be useful for protein kinase studies and systems-level analyses. The portal is available at https://kinet.kinametrix.com/ .

A palmitoyl transferase chemical-genetic system to map ZDHHC-specific S-acylation

The 23 human zinc finger Asp-His-His-Cys motif-containing (ZDHHC) S-acyltransferases catalyze long-chain S-acylation at cysteine residues across an extensive network of hundreds of proteins important for normal physiology or dysregulated in disease. Here we present a technology to directly map the protein substrates of a specific ZDHHC at the whole-proteome level, in intact cells. Structure-guided engineering of paired ZDHHC 'hole' mutants and 'bumped' chemically tagged fatty acid probes enabled probe transfer to specific protein substrates with excellent selectivity over wild-type ZDHHCs. Chemical-genetic systems were exemplified for five human ZDHHCs (3, 7, 11, 15 and 20) and applied to generate de novo ZDHHC substrate profiles, identifying >300 substrates and S-acylation sites for new functionally diverse proteins across multiple cell lines. We expect that this platform will elucidate S-acylation biology for a wide range of models and organisms.

Phylloquinone improves endothelial function, inhibits cellular senescence, and vascular inflammation

Phylloquinon (PK) and menaquinones (MK) are both naturally occurring compounds belonging to vitamin K group. Present study aimed to comprehensively analyze the influence of PK in several models of vascular dysfunction to determine whether PK has vasoprotective properties, similar to those previously described for MK. Effects of PK and MK on endothelial dysfunction were studied in ApoE/LDLR-/- mice in vivo, in the isolated aorta incubated with TNF, and in vascular cells as regard inflammation and cell senescence (including replicative and stress-induced models of senescence). Moreover, the vascular conversion of exogenous vitamins to endogenous MK-4 was analyzed. PK, as well as MK, given for 8 weeks in diet (10 mg/kg) resulted in comparable improvement in endothelial function in the ApoE/LDLR-/- mice. Similarly, PK and MK prevented TNF-induced impairment of endothelium-dependent vasorelaxation in the isolated aorta. In in vitro studies in endothelial and vascular smooth muscle cells, we identified that both PK and MK displayed anti-senescence effects via decreasing DNA damage while in endothelial cells anti-inflammatory activity was ascribed to the modulation of NFκB activation. The activity of PK and MK was comparable in terms of their effect on senescence and inflammation. Presence of endogenous synthesis of MK-4 from PK in aorta and endothelial and smooth muscle cells suggests a possible involvement of MK in vascular effects of PK. In conclusion, PK and MK display comparable vasoprotective effects, which may be ascribed, at least in part, to the inhibition of cell senescence and inflammation. The vasoprotective effect of PK in the vessel wall can be related to the direct effects of PK, as well as to the action of MK formed from PK in the vascular wall.

Proteomic approaches for protein kinase substrate identification in Apicomplexa

Apicomplexa is a phylum of protist parasites, notable for causing life-threatening diseases including malaria, toxoplasmosis, cryptosporidiosis, and babesiosis. Apicomplexan pathogenesis is generally a function of lytic replication, dissemination, persistence, host cell modification, and immune subversion. Decades of research have revealed essential roles for apicomplexan protein kinases in establishing infections and promoting pathogenesis. Protein kinases modify their substrates by phosphorylating serine, threonine, tyrosine, or other residues, resulting in rapid functional changes in the target protein. Post-translational modification by phosphorylation can activate or inhibit a substrate, alter its localization, or promote interactions with other proteins or ligands. Deciphering direct kinase substrates is crucial to understand mechanisms of kinase signaling, yet can be challenging due to the transient nature of kinase phosphorylation and potential for downstream indirect phosphorylation events. However, with recent advances in proteomic approaches, our understanding of kinase function in Apicomplexa has improved dramatically. Here, we discuss methods that have been used to identify kinase substrates in apicomplexan parasites, classifying them into three main categories: i) kinase interactome, ii) indirect phosphoproteomics and iii) direct labeling. We briefly discuss each approach, including their advantages and limitations, and highlight representative examples from the Apicomplexa literature. Finally, we conclude each main category by introducing prospective approaches from other fields that would benefit kinase substrate identification in Apicomplexa.

Comprehensive landscape of gastric cancer-targeted therapy and identification of CSNK2A1 as a potential target

Objective

To conduct a comprehensive analysis of the landscape of gastric cancer (GC)-targeted therapy clinical trials and identify potential therapeutic targets.

Methods

A systematic search and analysis of the Cochrane Central Register of Controlled Trials (CENTRAL) was performed to retrieve all GC clinical trials published up to June 30, 2022. Approved therapeutic targets for 11 common cancers were compiled and analyzed. The role of CSNK2A1 in GC was investigated using bioinformatics tools such as GEPIA, KMPLOT, SangerBox, STRING, ACLBI, and TIMER. Four gastric cancer cell lines (AGS, HGC, MGC, BGC) and one normal gastric mucosa cell line (GES-1) were utilized to assess the sensitivity to the CSNK2A1 inhibitor CX-4945. Quantitative real-time polymerase chain reaction (qPCR) was employed to quantify the cellular expression of CSNK2A1. Cellular apoptosis was evaluated using flow cytometry and Western blot analysis.

Results

The failure rate of GC randomized controlled clinical trials (RCTs) was strikingly high, accounting for 74.29 % (26/35) of the trials. Among the 35 approved targets in 11 different cancers, 13 targets were rigorously evaluated and identified as potential therapeutic targets for GC. Bioinformatics analysis revealed that CSNK2A1 is closely associated with multiple biological characteristics in GC, and its increased expression correlated significantly with enhanced sensitivity to CX-4945 treatment. Flow cytometry and Western blot analysis consistently demonstrated concentration-dependent apoptosis induced by CX-4945 in GC cell lines.

Conclusions

The high failure rate of GC clinical trials highlights the need for a more scientific and precise approach in target identification and clinical trial design. CSNK2A1 emerges as a promising therapeutic target for GC, and its expression level could potentially serve as a biomarker for predicting sensitivity to CX-4945 treatment. Further research is warranted to elucidate the underlying molecular mechanisms and validate the clinical significance of CSNK2A1 in GC therapy.

DEAD box RNA helicases are pervasive protein kinase interactors and activators

DEAD box (DDX) RNA helicases are a large family of ATPases, many of which have unknown functions. There is emerging evidence that besides their role in RNA biology, DDX proteins may stimulate protein kinases. To investigate if protein kinase-DDX interaction is a more widespread phenomenon, we conducted three orthogonal large-scale screens, including proteomics analysis with 32 RNA helicases, protein array profiling, and kinome-wide in vitro kinase assays. We retrieved Ser/Thr protein kinases as prominent interactors of RNA helicases and report hundreds of binary interactions. We identified members of ten protein kinase families, which bind to, and are stimulated by, DDX proteins, including CDK, CK1, CK2, DYRK, MARK, NEK, PRKC, SRPK, STE7/MAP2K, and STE20/PAK family members. We identified MARK1 in all screens and validated that DDX proteins accelerate the MARK1 catalytic rate. These findings indicate pervasive interactions between protein kinases and DEAD box RNA helicases, and provide a rich resource to explore their regulatory relationships.

The TSC22D, WNK, and NRBP gene families exhibit functional buffering and evolved with Metazoa for cell volume regulation

The ability to sense and respond to osmotic fluctuations is critical for the maintenance of cellular integrity. We used gene co-essentiality analysis to identify an unappreciated relationship between TSC22D2, WNK1, and NRBP1 in regulating cell volume homeostasis. All of these genes have paralogs and are functionally buffered for osmo-sensing and cell volume control. Within seconds of hyperosmotic stress, TSC22D, WNK, and NRBP family members physically associate into biomolecular condensates, a process that is dependent on intrinsically disordered regions (IDRs). A close examination of these protein families across metazoans revealed that TSC22D genes evolved alongside a domain in NRBPs that specifically binds to TSC22D proteins, which we have termed NbrT (NRBP binding region with TSC22D), and this co-evolution is accompanied by rapid IDR length expansion in WNK-family kinases. Our study reveals that TSC22D, WNK, and NRBP genes evolved in metazoans to co-regulate rapid cell volume changes in response to osmolarity.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Multiplexed Profiling of CDK Kinase Activities in Tumor Biopsies with Fluorescent Peptide Biosensors

Detection of disease biomarkers constitutes a major challenge for the development of personalized and predictive diagnostics as well as companion assays. Protein kinases (PKs) involved in the coordination of cell cycle progression and proliferation that are hyperactivated in human cancers constitute attractive pharmacological targets and relevant biomarkers. Although it is relatively straightforward to assess the relative abundance of PKs in a biological sample, there is not always a direct correlation with enzymatic activity, which is regulated by several posttranslational mechanisms. Studies of relative abundance therefore convey limited information, and the lack of selective, sensitive, and standardized tools together with the inherent complexity of biological samples makes it difficult to quantify PK activities in physio-pathological tissues. To address this challenge, we have developed a toolbox of fluorescent biosensors that report on CDK activities in a sensitive, selective, dose-dependent, and quantitative fashion, which we have implemented to profile CDK activity signatures in cancer cell lines and biopsies from human tumors. In this study, we report on a standardized and calibrated biosensing approach to quantify CDK1,2,4, and 6 activities simultaneously through a combination of four different biosensors in a panel of 40 lung adenocarcinoma and 40 follicular lymphoma samples. CDK activity profiling highlighted two major patterns which were further correlated with age, sex of patients, tumor size, grade, and genetic and immunohistochemical features of the biopsies. Multiplex CDKACT biosensing technology provides new and complementary information relative to current genetic and immunohistochemical characterization of tumor biopsies, which will be useful for diagnostic purposes, potentially guiding therapeutic decision. These fluorescent peptide biosensors offer promise for personalized diagnostics based on kinase activity profiling.

Targeting Protein Kinases to Protect Beta-Cell Function and Survival in Diabetes

The prevalence of diabetes is increasing worldwide. Massive death of pancreatic beta-cells causes type 1 diabetes. Progressive loss of beta-cell function and mass characterizes type 2 diabetes. To date, none of the available antidiabetic drugs promotes the maintenance of a functional mass of endogenous beta-cells, revealing an unmet medical need. Dysfunction and apoptotic death of beta-cells occur, in particular, through the activation of intracellular protein kinases. In recent years, protein kinases have become highly studied targets of the pharmaceutical industry for drug development. A number of drugs that inhibit protein kinases have been approved for the treatment of cancers. The question of whether safe drugs that inhibit protein kinase activity can be developed and used to protect the function and survival of beta-cells in diabetes is still unresolved. This review presents arguments suggesting that several protein kinases in beta-cells may represent targets of interest for the development of drugs to treat diabetes.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

PYK2, a hub of signaling networks in breast cancer progression

Breast cancer (BC) involves complex signaling networks characterized by extensive cross-communication and feedback loops between and within multiple signaling cascades. Many of these signaling pathways are driven by genetic alterations of oncogene and/or tumor-suppressor genes and are influenced by various environmental cues. We describe unique roles of the non-receptor tyrosine kinase (NRTK) PYK2 in signaling integration and feedback looping in BC. PYK2 functions as a signaling hub in various cascades, and its involvement in positive and negative feedback loops enhances signaling robustness, modulates signaling dynamics, and contributes to BC growth, epithelial-to-mesenchymal transition (EMT), stemness, migration, invasion, and metastasis. We also discuss the potential of PYK2 as a therapeutic target in various BC subtypes.

CSNK2A1 confers gemcitabine resistance to pancreatic ductal adenocarcinoma via inducing autophagy

Gemcitabine, a pivotal chemotherapeutic agent for pancreatic ductal adenocarcinoma (PDAC), frequently encounters drug resistance, posing a significant clinical challenge with implications for PDAC patient prognosis. In this study, employing an integrated approach involving bioinformatic analyses from multiple databases, we unveil CSNK2A1 as a key regulatory factor. The patient-derived xenograft (PDX) model further substantiates the critical role of CSNK2A1 in gemcitabine resistance within the context of PDAC. Additionally, targeted silencing of CSNK2A1 expression significantly enhances sensitivity of PDAC cells to gemcitabine treatment. Mechanistically, CSNK2A1's transcriptional regulation is mediated by H3K27 acetylation in PDAC. Moreover, we identify CSNK2A1 as a pivotal activator of autophagy, and enhanced autophagy drives gemcitabine resistance. Silmitasertib, an established CSNK2A1 inhibitor, can effectively inhibit autophagy. Notably, the combinatorial treatment of Silmitasertib with gemcitabine demonstrates remarkable efficacy in treating PDAC. In summary, our study reveals CSNK2A1 as a potent predictive factor for gemcitabine resistance in PDAC. Moreover, targeted CSNK2A1 inhibition by Silmitasertib represents a promising therapeutic strategy to restore gemcitabine sensitivity in PDAC, offering hope for improved clinical outcomes.

New Function Annotation of PROSER2 in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis due to the absence of diagnostic markers and molecular targets. Here, we took an unconventional approach to identify new molecular targets for pancreatic cancer. We chose uncharacterized protein evidence level 1 without function annotation from extensive proteomic research on pancreatic cancer and focused on proline and serine-rich 2 (PROSER2), which ranked high in the cell membrane and cytoplasm. In our study using cell lines and patient-derived orthotopic xenograft cells, PROSER2 exhibited a higher expression in cells derived from primary tumors than in those from metastatic tissues. PROSER2 was localized in the cell membrane and cytosol by immunocytochemistry. PROSER2 overexpression significantly reduced the metastatic ability of cancer cells, whereas its suppression had the opposite effect. Proteomic analysis revealed that PROSER2 interacts with STK25 and PDCD10, and their binding was confirmed by immunoprecipitation and immunocytochemistry. STK25 knockdown enhanced metastasis by decreasing p-AMPK levels, whereas PROSER2-overexpressing cells increased the level of p-AMPK, indicating that PROSER2 suppresses invasion via the AMPK pathway by interacting with STK25. This is the first demonstration of the novel role of PROSER2 in antagonizing tumor progression via the STK25-AMPK pathway in PDAC. LC-MS/MS data are available at MassIVE (MSV000092953) and ProteomeXchange (PXD045646).

HERC6 regulates STING activity in a sex-biased manner through modulation of LATS2/VGLL3 Hippo signaling

Interferon (IFN) activity exhibits a gender bias in human skin, skewed toward females. We show that HERC6, an IFN-induced E3 ubiquitin ligase, is induced in human keratinocytes through the epidermal type I IFN; IFN-κ. HERC6 knockdown in human keratinocytes results in enhanced induction of interferon-stimulated genes (ISGs) upon treatment with a double-stranded (ds) DNA STING activator cGAMP but not in response to the RNA-sensing TLR3 agonist. Keratinocytes lacking HERC6 exhibit sustained STING-TBK1 signaling following cGAMP stimulation through modulation of LATS2 and TBK1 activity, unmasking more robust ISG responses in female keratinocytes. This enhanced female-biased immune response with loss of HERC6 depends on VGLL3, a regulator of type I IFN signature. These data identify HERC6 as a previously unrecognized negative regulator of ISG expression specific to dsDNA sensing and establish it as a regulator of female-biased immune responses through modulation of STING signaling.

Revealing eEF-2 kinase: recent structural insights into function

The α-kinase eukaryotic elongation factor 2 kinase (eEF-2K) regulates translational elongation by phosphorylating its ribosome-associated substrate, the GTPase eEF-2. eEF-2K is activated by calmodulin (CaM) through a distinctive mechanism unlike that in other CaM-dependent kinases (CAMK). We describe recent structural insights into this unique activation process and examine the effects of specific regulatory signals on this mechanism. We also highlight key unanswered questions to guide future structure-function studies. These include structural mechanisms which enable eEF-2K to interact with upstream/downstream partners and facilitate its integration of diverse inputs, including Ca2+ transients, phosphorylation mediated by energy/nutrient-sensing pathways, pH changes, and metabolites. Answering these questions is key to establishing how eEF-2K harmonizes translation with cellular requirements within the boundaries of its molecular landscape.

Mirk/Dyrk1B controls ventral spinal cord development via Shh pathway

Cross-talk between Mirk/Dyrk1B kinase and Sonic hedgehog (Shh)/Gli pathway affects physiology and pathology. Here, we reveal a novel role for Dyrk1B in regulating ventral progenitor and neuron subtypes in the embryonic chick spinal cord (SC) via the Shh pathway. Using in ovo gain-and-loss-of-function approaches at E2, we report that Dyrk1B affects the proliferation and differentiation of neuronal progenitors at E4 and impacts on apoptosis specifically in the motor neuron (MN) domain. Especially, Dyrk1B overexpression decreases the numbers of ventral progenitors, MNs, and V2a interneurons, while the pharmacological inhibition of endogenous Dyrk1B kinase activity by AZ191 administration increases the numbers of ventral progenitors and MNs. Mechanistically, Dyrk1B overexpression suppresses Shh, Gli2 and Gli3 mRNA levels, while conversely, Shh, Gli2 and Gli3 transcription is increased in the presence of Dyrk1B inhibitor AZ191 or Smoothened agonist SAG. Most importantly, in phenotype rescue experiments, SAG restores the Dyrk1B-mediated dysregulation of ventral progenitors. Further at E6, Dyrk1B affects selectively the medial lateral motor neuron column (LMCm), consistent with the expression of Shh in this region. Collectively, these observations reveal a novel regulatory function of Dyrk1B kinase in suppressing the Shh/Gli pathway and thus affecting ventral subtypes in the developing spinal cord. These data render Dyrk1B a possible therapeutic target for motor neuron diseases.

FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of ~5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

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.

In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network

Genetic analyses of Schizophrenia (SCZ) patients have identified thousands of risk factors. In silico protein-protein interaction (PPI) network analysis has provided strong evidence that disrupted PPI networks underlie SCZ pathogenesis. In this study, we performed in vivo PPI analysis of several SCZ risk factors in the rodent brain. Using endogenous antibody immunoprecipitations coupled to mass spectrometry (MS) analysis, we constructed a SCZ network comprising 1612 unique PPI with a 5% FDR. Over 90% of the PPI were novel, reflecting the lack of previous PPI MS studies in brain tissue. Our SCZ PPI network was enriched with known SCZ risk factors, which supports the hypothesis that an accumulation of disturbances in selected PPI networks underlies SCZ. We used Stable Isotope Labeling in Mammals (SILAM) to quantitate phencyclidine (PCP) perturbations in the SCZ network and found that PCP weakened most PPI but also led to some enhanced or new PPI. These findings demonstrate that quantitating PPI in perturbed biological states can reveal alterations to network biology.

Functions of SRPK, CLK and DYRK kinases in stem cells, development, and human developmental disorders

Human developmental disorders encompass a wide range of debilitating physical conditions and intellectual disabilities. Perturbation of protein kinase signalling underlies the development of some of these disorders. For example, disrupted SRPK signalling is associated with intellectual disabilities, and the gene dosage of DYRKs can dictate the pathology of disorders including Down's syndrome. Here, we review the emerging roles of the CMGC kinase families SRPK, CLK, DYRK, and sub-family HIPK during embryonic development and in developmental disorders. In particular, SRPK, CLK, and DYRK kinase families have key roles in developmental signalling and stem cell regulation, and can co-ordinate neuronal development and function. Genetic studies in model organisms reveal critical phenotypes including embryonic lethality, sterility, musculoskeletal errors, and most notably, altered neurological behaviours arising from defects of the neuroectoderm and altered neuronal signalling. Further unpicking the mechanisms of specific kinases using human stem cell models of neuronal differentiation and function will improve our understanding of human developmental disorders and may provide avenues for therapeutic strategies.

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2's ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameter's ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins.

Significance Statement

Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithm's multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

Community detection in empirical kinase networks identifies new potential members of signalling pathways

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.

Protein kinases on carbon metabolism: potential targets for alternative chemotherapies against toxoplasmosis

The apicomplexan parasite Toxoplasma gondii is the causative agent of toxoplasmosis, a global disease that significantly impacts human health. The clinical manifestations are mainly observed in immunocompromised patients, including ocular damage and neuronal alterations leading to psychiatric disorders. The congenital infection leads to miscarriage or severe alterations in the development of newborns. The conventional treatment is limited to the acute phase of illness, without effects in latent parasites; consequently, a cure is not available yet. Furthermore, considerable toxic effects and long-term therapy contribute to high treatment abandonment rates. The investigation of exclusive parasite pathways would provide new drug targets for more effective therapies, eliminating or reducing the side effects of conventional pharmacological approaches. Protein kinases (PKs) have emerged as promising targets for developing specific inhibitors with high selectivity and efficiency against diseases. Studies in T. gondii have indicated the presence of exclusive PKs without homologs in human cells, which could become important targets for developing new drugs. Knockout of specific kinases linked to energy metabolism have shown to impair the parasite development, reinforcing the essentiality of these enzymes in parasite metabolism. In addition, the specificities found in the PKs that regulate the energy metabolism in this parasite could bring new perspectives for safer and more efficient therapies for treating toxoplasmosis. Therefore, this review provides an overview of the limitations for reaching an efficient treatment and explores the role of PKs in regulating carbon metabolism in Toxoplasma, discussing their potential as targets for more applied and efficient pharmacological approaches.

The dark kinase STK32A regulates hair cell planar polarity opposite of EMX2 in the developing mouse inner ear

The vestibular maculae of the inner ear contain sensory receptor hair cells that detect linear acceleration and contribute to equilibrioception to coordinate posture and ambulatory movements. These hair cells are divided between two groups, separated by a line of polarity reversal (LPR), with oppositely oriented planar-polarized stereociliary bundles that detect motion in opposite directions. The transcription factor EMX2 is known to establish this planar polarized organization in mouse by regulating the distribution of the transmembrane receptor GPR156 at hair cell boundaries in one group of cells. However, the genes regulated by EMX2 in this context were previously not known. Using mouse as a model, we have identified the serine threonine kinase STK32A as a downstream effector negatively regulated by EMX2. Stk32a is expressed in hair cells on one side of the LPR in a pattern complementary to Emx2 expression in hair cells on the opposite side. Stk32a is necessary to align the intrinsic polarity of the bundle with the core planar cell polarity (PCP) proteins in EMX2-negative regions, and is sufficient to reorient bundles when ectopically expressed in neighboring EMX2-positive regions. We demonstrate that STK32A reinforces LPR formation by regulating the apical localization of GPR156. These observations support a model in which bundle orientation is determined through separate mechanisms in hair cells on opposite sides of the maculae, with EMX2-mediated repression of Stk32a determining the final position of the LPR.

CDK6 activity in a recurring convergent kinase network motif

In humans, more than 500 kinases phosphorylate ~15% of all proteins in an emerging phosphorylation network. Convergent local interaction motifs, in which ≥two kinases phosphorylate the same substrate, underlie feedback loops and signal amplification events but have not been systematically analyzed. Here, we first report a network-wide computational analysis of convergent kinase-substrate relationships (cKSRs). In experimentally validated phosphorylation sites, we find that cKSRs are common and involve >80% of all human kinases and >24% of all substrates. We show that cKSRs occur over a wide range of stoichiometries, in many instances harnessing co-expressed kinases from family subgroups. We then experimentally demonstrate for the prototypical convergent CDK4/6 kinase pair how multiple inputs phosphorylate the tumor suppressor retinoblastoma protein (RB) and thereby hamper in situ analysis of the individual kinases. We hypothesize that overexpression of one kinase combined with a CDK4/6 inhibitor can dissect convergence. In breast cancer cells expressing high levels of CDK4, we confirm this hypothesis and develop a high-throughput compatible assay that quantifies genetically modified CDK6 variants and inhibitors. Collectively, our work reveals the occurrence, topology, and experimental dissection of convergent interactions toward a deeper understanding of kinase networks and functions.

Silmitasertib (CX-4945), a Clinically Used CK2-Kinase Inhibitor with Additional Effects on GSK3β and DYRK1A Kinases: A Structural Perspective

A clinical casein kinase 2 inhibitor, CX-4945 (silmitasertib), shows significant affinity toward the DYRK1A and GSK3β kinases, involved in down syndrome phenotypes, Alzheimer's disease, circadian clock regulation, and diabetes. This off-target activity offers an opportunity for studying the effect of the DYRK1A/GSK3β kinase system in disease biology and possible line extension. Motivated by the dual inhibition of these kinases, we solved and analyzed the crystal structures of DYRK1A and GSK3β with CX-4945. We built a quantum-chemistry-based model to rationalize the compound affinity for CK2α, DYRK1A, and GSK3β kinases. Our calculations identified a key element for CK2α's subnanomolar affinity to CX-4945. The methodology is expandable to other kinase selectivity modeling. We show that the inhibitor limits DYRK1A- and GSK3β-mediated cyclin D1 phosphorylation and reduces kinase-mediated NFAT signaling in the cell. Given the CX-4945's clinical and pharmacological profile, this inhibitory activity makes it an interesting candidate with potential for application in additional disease areas.

Structure-Guided Prediction of the Functional Impact of DCLK1 Mutations on Tumorigenesis

Doublecortin-like kinase 1 (DCLK1) is a functional serine/threonine (S/T)-kinase and a member of the doublecortin family of proteins which are characterized by their ability to bind to microtubules (MTs). DCLK1 is a proposed cancer driver gene, and its upregulation is associated with poor overall survival in several solid cancer types. However, how DCLK1 associates with MTs and how its kinase function contributes to pro-tumorigenic processes is poorly understood. This review builds on structural models to propose not only the specific functions of the domains but also attempts to predict the impact of individual somatic missense mutations on DCLK1 functions. Somatic missense mutations in DCLK1 are most frequently located within the N-terminal MT binding region and likely impact on the ability of DCLK1 to bind to αβ-tubulin and to polymerize and stabilize MTs. Moreover, the MT binding affinity of DCLK1 is negatively regulated by its auto-phosphorylation, and therefore mutations that affect kinase activity are predicted to indirectly alter MT dynamics. The emerging picture portrays DCLK1 as an MT-associated protein whose interactions with tubulin heterodimers and MTs are tightly controlled processes which, when disrupted, may confer pro-tumorigenic properties.

Multiplexed kinase interactome profiling quantifies cellular network activity and plasticity

Dynamic changes in protein-protein interaction (PPI) networks underlie all physiological cellular functions and drive devastating human diseases. Profiling PPI networks can, therefore, provide critical insight into disease mechanisms and identify new drug targets. Kinases are regulatory nodes in many PPI networks; yet, facile methods to systematically study kinase interactome dynamics are lacking. We describe kinobead competition and correlation analysis (kiCCA), a quantitative mass spectrometry-based chemoproteomic method for rapid and highly multiplexed profiling of endogenous kinase interactomes. Using kiCCA, we identified 1,154 PPIs of 238 kinases across 18 diverse cancer lines, quantifying context-dependent kinase interactome changes linked to cancer type, plasticity, and signaling states, thereby assembling an extensive knowledgebase for cell signaling research. We discovered drug target candidates, including an endocytic adapter-associated kinase (AAK1) complex that promotes cancer cell epithelial-mesenchymal plasticity and drug resistance. Our data demonstrate the importance of kinase interactome dynamics for cellular signaling in health and disease.

The role of CaMKK2 in Golgi-associated vesicle trafficking

Calcium/calmodulin-dependent protein kinase kinase 2 (CaMKK2) is a serine/threonine-protein kinase, that is involved in maintaining various physiological and cellular processes within the cell that regulate energy homeostasis and cell growth. CaMKK2 regulates glucose metabolism by the activation of downstream kinases, AMP-activated protein kinase (AMPK) and other calcium/calmodulin-dependent protein kinases. Consequently, its deregulation has a role in multiple human metabolic diseases including obesity and cancer. Despite the importance of CaMKK2, its signalling pathways and pathological mechanisms are not completely understood. Recent work has been aimed at broadening our understanding of the biological functions of CaMKK2. These studies have uncovered new interaction partners that have led to the description of new functions that include lipogenesis and Golgi vesicle trafficking. Here, we review recent insights into the role of CaMKK2 in membrane trafficking mechanisms and discuss the functional implications in a cellular context and for disease.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Non-enzymatic acetylation inhibits glycolytic enzymes in Escherichia coli

Advanced mass spectrometry methods have detected thousands of post-translational phosphorylation and acetylation sites in bacteria, but their functional role and the enzymes catalyzing these modifications remain largely unknown. In addition to enzymatic acetylation, lysine residues can also be chemically acetylated by the metabolite acetyl phosphate. In Escherichia coli, acetylation at over 3,000 sites has been linked to acetyl phosphate, but the functionality of this widespread non-enzymatic acetylation is even less clear than the enzyme-catalyzed one. Here, we investigate the role of acetyl-phosphate-mediated acetylation in E. coli central metabolism. Out of 19 enzymes investigated, only GapA and GpmA are acetylated at high stoichiometry, which inhibits their activity by interfering with substrate binding, effectively reducing glycolysis when flux to or from acetate is high. Extrapolating our results to the whole proteome, maximally 10% of the reported non-enzymatically acetylated proteins are expected to reach a stoichiometry that could inhibit their activity.

Evolutionary and cellular analysis of the 'dark' pseudokinase PSKH2

Pseudokinases, so named because they lack one or more conserved canonical amino acids that define their catalytically active relatives, have evolved a variety of biological functions in both prokaryotic and eukaryotic organisms. Human PSKH2 is closely related to the canonical kinase PSKH1, which maps to the CAMK family of protein kinases. Primates encode PSKH2 in the form of a pseudokinase, which is predicted to be catalytically inactive due to loss of the invariant catalytic Asp residue. Although the biological role(s) of vertebrate PSKH2 proteins remains unclear, we previously identified species-level adaptions in PSKH2 that have led to the appearance of kinase or pseudokinase variants in vertebrate genomes alongside a canonical PSKH1 paralog. In this paper we confirm that, as predicted, PSKH2 lacks detectable protein phosphotransferase activity, and exploit structural informatics, biochemistry and cellular proteomics to begin to characterise vertebrate PSKH2 orthologues. AlphaFold 2-based structural analysis predicts functional roles for both the PSKH2 N- and C-regions that flank the pseudokinase domain core, and cellular truncation analysis confirms that the N-terminal domain, which contains a conserved myristoylation site, is required for both stable human PSKH2 expression and localisation to a membrane-rich subcellular fraction containing mitochondrial proteins. Using mass spectrometry-based proteomics, we confirm that human PSKH2 is part of a cellular mitochondrial protein network, and that its expression is regulated through client-status within the HSP90/Cdc37 molecular chaperone system. HSP90 interactions are mediated through binding to the PSKH2 C-terminal tail, leading us to predict that this region might act as both a cis and trans regulatory element, driving outputs linked to the PSKH2 pseudokinase domain that are important for functional signalling.

Comparative functional analysis of macrophage phagocytosis in Dagu chickens and Wenchang chickens

Phagocytosis of macrophages constitutes a powerful barrier to innate immunity. Differences in the phagocytic function of macrophages among chicken breeds have rarely been reported, and the molecular mechanisms underlying phagocytosis remain poorly understood. This study compared functional difference of macrophages in Dagu chickens, originated in Zhuanghe, Liaoning Province, China, and Wenchang chickens, originated from Hainan Island in the South China Sea, and explored the potential molecular mechanisms by integrated analysis of mRNA expression profiles of macrophages and whole genome sequencing. Immunological parameters in peripheral blood indicated that Dagu chickens were more resistant to Salmonella challenge at 28 days old. Phagocytosis index and phagocytosis rate of macrophages displayed Dagu chickens performed a significantly higher phagocytic ability of macrophages at 14 and 28 days old. Furthermore, comparative analysis of mRNA expression profiles of macrophages of two breeds at 28 days old revealed that 1136 differentially expressed genes (DEGs), and 22 DEGs (e.g., H2AFZ, SNRPA1, CUEDC2, S100A12) were found to be hub genes regulating phagocytosis by participating in different immunological biological signaling pathways. In addition, many DEGs and hub genes were under strong differentiation in genome between two breeds, the H2AFZ gene was an intersection of DEGs and hub genes. These results provided a comprehensive functional comparison and transcriptomic profiles of macrophages in Chinese native chicken breeds, and deepened our understanding of the genetic mechanism of innate immunity.

Regulation of secretory pathway kinase or kinase-like proteins in human cancers

Secretory pathway kinase or kinase-like proteins (SPKKPs) are effective in the lumen of the endoplasmic reticulum (ER), Golgi apparatus (GA), and extracellular space. These proteins are involved in secretory signaling pathways and are distinctive from typical protein kinases. Various reports have shown that SPKKPs regulate the tumorigenesis and progression of human cancer via the phosphorylation of various substrates, which is essential in physiological and pathological processes. Emerging evidence has revealed that the expression of SPKKPs in human cancers is regulated by multiple factors. This review summarizes the current understanding of the contribution of SPKKPs in tumorigenesis and the progression of immunity. With the epidemic trend of immunotherapy, targeting SPKKPs may be a novel approach to anticancer therapy. This study briefly discusses the recent advances regarding SPKKPs.

Identification of Novel Kinases of Tau Using Fluorescence Complementation Mass Spectrometry (FCMS)

Hyperphosphorylation of the microtubule-associated protein Tau is a major hallmark of Alzheimer's disease and other tauopathies. Understanding the protein kinases that phosphorylate Tau is critical for the development of new drugs that target Tau phosphorylation. At present, the repertoire of the Tau kinases remains incomplete, and methods to uncover novel upstream protein kinases are still limited. Here, we apply our newly developed proteomic strategy, fluorescence complementation mass spectrometry, to identify novel kinase candidates of Tau. By constructing Tau- and kinase-fluorescent fragment library, we detected 59 Tau-associated kinases, including 23 known kinases of Tau and 36 novel candidate kinases. In the validation phase using in vitro phosphorylation, among 15 candidate kinases we attempted to purify and test, four candidate kinases, OXSR1 (oxidative-stress responsive gene 1), DAPK2 (death-associated protein kinase 2), CSK (C-terminal SRC kinase), and ZAP70 (zeta chain of T-cell receptor-associated protein kinase 70), displayed the ability to phosphorylate Tau in time-course experiments. Furthermore, coexpression of these four kinases along with Tau increased the phosphorylation of Tau in human neuroglioma H4 cells. We demonstrate that fluorescence complementation mass spectrometry is a powerful proteomic strategy to systematically identify potential kinases that can phosphorylate Tau in cells. Our discovery of new candidate kinases of Tau can present new opportunities for developing Alzheimer's disease therapeutic strategies.

Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics

Protein-protein interactions (PPIs) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components but mostly lack the context of cellular states. However, the generation of context-dependent PPI networks is essential for structural and systems-level modeling of biological processes-a goal that remains an unsolved challenge. Here we describe an experimental/computational strategy to achieve a modeling of PPIs that considers contextual information. This strategy defines the composition, stoichiometry, temporal organization, and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

The Inflammasome Activity of NLRP3 Is Independent of NEK7 in HEK293 Cells Co-Expressing ASC

The cytosolic immune receptor NLRP3 (nucleotide-binding domain, leucine-rich repeat (LRR), and pyrin domain (PYD)-containing protein 3) oligomerizes into the core of a supramolecular complex termed inflammasome in response to microbes and danger signals. It is thought that NLRP3 has to bind NEK7 (NIMA (never in mitosis gene a)-related kinase 7) to form a functional inflammasome core that induces the polymerization of the adaptor protein ASC (Apoptosis-associated speck-like protein containing a CARD (caspase recruitment domain)), which is a hallmark for NLRP3 activity. We reconstituted the NLRP3 inflammasome activity in modified HEK293 (human embryonic kidney 293) cells and showed that the ASC speck polymerization is independent of NEK7 in the context of this cell system. Probing the interfaces observed in the different, existing structural models of NLRP3 oligomers, we present evidence that the NEK7-independent, constitutively active NLRP3 inflammasome core in HEK293 cells may resemble a stacked-torus-like hexamer seen for NLRP3 lacking its PYD (pyrin domain).

EXPLICIT-Kinase: A gene expression predictor for dissecting the functions of the Arabidopsis kinome

Protein kinases regulate virtually all cellular processes, but it remains challenging to determine the functions of all protein kinases, collectively called the "kinome", in any species. We developed a computational approach called EXPLICIT-Kinase to predict the functions of the Arabidopsis kinome. Because the activities of many kinases can be regulated transcriptionally, their gene expression patterns provide clues to their functions. A universal gene expression predictor for Arabidopsis was constructed to predict the expression of 30,172 non-kinase genes based on the expression of 994 kinases. The model reconstituted highly accurate transcriptomes for diverse Arabidopsis samples. It identified the significant kinases as predictor kinases for predicting the expression of Arabidopsis genes and pathways. Strikingly, these predictor kinases were often regulators of related pathways, as exemplified by those involved in cytokinesis, tissue development, and stress responses. Comparative analyses revealed that portions of these predictor kinases are shared and conserved between Arabidopsis and maize. As an example, we identified a conserved predictor kinase, RAF6, from a stomatal movement module. We verified that RAF6 regulates stomatal closure. It can directly interact with SLAC1, a key anion channel for stomatal closure, and modulate its channel activity. Our approach enables a systematic dissection of the functions of the Arabidopsis kinome.

Physical and functional interactome atlas of human receptor tyrosine kinases

Much cell-to-cell communication is facilitated by cell surface receptor tyrosine kinases (RTKs). These proteins phosphorylate their downstream cytoplasmic substrates in response to stimuli such as growth factors. Despite their central roles, the functions of many RTKs are still poorly understood. To resolve the lack of systematic knowledge, we apply three complementary methods to map the molecular context and substrate profiles of RTKs. We use affinity purification coupled to mass spectrometry (AP-MS) to characterize stable binding partners and RTK-protein complexes, proximity-dependent biotin identification (BioID) to identify transient and proximal interactions, and an in vitro kinase assay to identify RTK substrates. To identify how kinase interactions depend on kinase activity, we also use kinase-deficient mutants. Our data represent a comprehensive, systemic mapping of RTK interactions and substrates. This resource adds information regarding well-studied RTKs, offers insights into the functions of less well-studied RTKs, and highlights RTK-RTK interactions and shared signaling pathways.