The target landscape of clinical kinase drugs

CellEKT: a robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors

The human genome encodes 518 protein kinases that are pivotal for drug discovery in various therapeutic areas such as cancer and autoimmune disorders. The majority of kinase inhibitors target the conserved ATP-binding pocket, making it difficult to develop selective inhibitors. To characterize and prioritize kinase-inhibiting drug candidates, efficient methods are desired to determine target engagement across the cellular kinome. In this study, we present CellEKT (Cellular Endogenous Kinase Targeting), an optimized and robust chemical proteomics platform for investigating cellular target engagement of endogenously expressed kinases using the sulfonyl fluoride-based probe XO44 and two new probes ALX005 and ALX011. The optimized workflow enabled the determination of the kinome interaction landscape of covalent and non-covalent drugs across over 300 kinases, expressed as half maximum inhibitory concentration (IC50), which were validated using distinct platforms like phosphoproteomics and NanoBRET. With CellEKT, target engagement profiles were linked to their substrate space. CellEKT has the ability to decrypt drug actions and to guide the discovery and development of drugs.

MAM kinases: physiological roles, related diseases, and therapeutic perspectives-a systematic review

Mitochondria-associated membranes (MAMs) are tethering regions amid the membranes of the endoplasmic reticulum (ER) and mitochondria. They are a lipid raft-like structure occupied by various proteins that facilitates signal transduction between the two organelles. The MAM proteome participates in cellular functions such as calcium (Ca2+) homeostasis, lipid synthesis, ER stress, inflammation, autophagy, mitophagy, and apoptosis. The human kinome is a superfamily of homologous proteins consisting of 538 kinases. MAM-associated kinases participate in the aforementioned cellular functions and act as cell fate executors. Studies have proved the dysregulated kinase interactions in MAM as an etiology for various diseases including cancer, diabetes mellitus, neurodegenerative diseases, cardiovascular diseases (CVDs), and obesity. Several small kinase inhibitory molecules have been well explored as promising drug candidates in clinical trials with an accelerating impact in the field of precision medicine. This review narrates the physiological actions, pathophysiology, and therapeutic potential of MAM-associated kinases with recent updates in the field.

Bacterial ubiquitin ligase engineered for small molecule and protein target identification

The Legionella SidE effectors ubiquitinate host proteins independently of the canonical E1-E2 cascade. Here we engineer the SidE ligases to develop a modular proximity ligation approach for the identification of targets of small molecules and proteins, which we call SidBait. We validate the method with known small molecule-protein interactions and use it to identify CaMKII as an off-target interactor of the breast cancer drug ribociclib. Structural analysis and activity assays confirm that ribociclib binds the CaMKII active site and inhibits its activity. We further customize SidBait to identify protein-protein interactions, including substrates for enzymes, and discover the F-actin capping protein (CapZ) as a target of the Legionella effector RavB during infection. Structural and biochemical studies indicate that RavB allosterically binds CapZ and decaps actin, thus functionally mimicking eukaryotic CapZ interacting proteins. Collectively, our results establish SidBait as a reliable tool for identifying targets of small molecules and proteins.

Chemoproteomic Profiling of C. albicans for Characterization of Antifungal Kinase Inhibitors

Candida albicans is a major cause of systemic candidiasis, a severe fungal infection with a ∼40% mortality rate. Yck2, a casein kinase 1 (CK1) in C. albicans, is targeted by antifungal inhibitors YK-I-02 (YK) and MN-I-157 (MN). Using multiplexed inhibitor beads and mass spectrometry (MIB/MS), the selectivity of these inhibitors was determined across the fungal kinome. The MIB matrix captured 89% of C. albicans protein kinases, revealing that YK and MN selectively engage three CK1 homologues (Yck2, Yck22, and Hrr25) and a human p38α homologue (Hog1). Chemoproteomics using a custom MN-kinobead confirmed the remarkable fungal kinome selectivity. To identify new Yck2 inhibitors with selectivity over Hog1, 13 human CK1 inhibitors were screened, leading to the discovery of a new chemotype with antifungal activity. These findings highlight the utility of MIB/MS in profiling nonhuman kinomes and developing selective fungal kinase inhibitors as antimicrobial agents.

Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Computational Development of Allosteric Peptide Inhibitors Targeting LIM Kinases as a Novel Therapeutic Intervention

LIM Kinases (LIMKs) have emerged as critical therapeutic targets in cancer research due to their central role in regulating cytoskeletal dynamics and cell motility via cofilin phosphorylation. Allosteric inhibitors, which bind outside the ATP-binding pocket, offer distinct advantages over ATP-competitive inhibitors, such as increased specificity, reduced off-target effects, and the ability to overcome resistance. This study investigates a series of novel tetrapeptides mimicking the binding mode of TH470, an allosteric LIMK inhibitor, using in silico docking and molecular dynamics simulations to identify potential lead compounds with high specificity, binding affinity, and favorable pharmacokinetic properties. Structural analyses revealed critical interactions between TH470 and LIMKs, particularly with conserved residues such as Thr405 (gatekeeper residue), Ile408 (hinge region), and Asp469 (XDFG motif), which are essential for stabilizing inhibitor binding. Molecular dynamics simulations confirmed the stability of TH470-LIMK1 and TH470-LIMK2 complexes, with lower RMS deviations and robust interaction patterns enhancing binding affinity. From the set of tetrapeptides mimicking TH470 binding mode, only YFYW, WPHW, and YWFP for LIMK1, and PYWG, FYWV, and WFVW for LIMK2 demonstrated high binding affinities, non-toxic profiles, and promising anti-cancer, anti-angiogenic, and anti-inflammatory properties. Among the studied peptides, LIMK1-YFYW and LIMK2-WFVW exhibited the most substantial binding affinities, supported by high hydrogen bond occupancy with key residues such as Ile416 and Thr405. The findings highlight the therapeutic potential of allosteric peptide inhibitors targeting LIMK-mediated pathways in cancer progression. The study underscores the importance of specific interactions with conserved LIMK residues, providing a foundation for further developing selective inhibitors to modulate actin dynamics and combat cancer-related processes.

Regulation of gene expression through protein-metabolite interactions

Organisms have to adapt to changes in their environment. Cellular adaptation requires sensing, signalling and ultimately the activation of cellular programs. Metabolites are environmental signals that are sensed by proteins, such as metabolic enzymes, protein kinases and nuclear receptors. Recent studies have discovered novel metabolite sensors that function as gene regulatory proteins such as chromatin associated factors or RNA binding proteins. Due to their function in regulating gene expression, metabolite-induced allosteric control of these proteins facilitates a crosstalk between metabolism and gene expression. Here we discuss the direct control of gene regulatory processes by metabolites and recent progresses that expand our abilities to systematically characterize metabolite-protein interaction networks. Obtaining a profound map of such networks is of great interest for aiding metabolic disease treatment and drug target identification.

Kinase Plasticity in Response to Vandetanib Enhances Sensitivity to Tamoxifen in Estrogen Receptor Positive Breast Cancer

Resistance to endocrine therapy (ET) is common in estrogen receptor (ER) positive breast cancer. Multiple studies have demonstrated that upregulation of MAPK signaling pathways contributes to ET resistance. Herein we show that vandetanib treatment enhances sensitivity to ET in ET-sensitive and -resistant ER+ breast cancer models. Vandetanib treatment alters the gene expression program of ER+ breast cancer cells resulting in a less proliferative and more estrogen responsive Luminal-A like character. Tyrosine kinase network reprogramming was assessed using multiplexed kinase inhibitor beads-mass spectrometry (MIB/MS) assay to identify adaptive resistance mechanisms to vandetanib treatment, including upregulation of HER2 activity. Co-treatment to inhibit HER2 with lapatinib enhanced sensitivity to vandetanib, demonstrating biologic activity of HER2 upregulation. Using a CRISPR knockout model, we demonstrate that vandetanib effects are partially mediated by RET receptor tyrosine kinase. Finally, we use our operating room-to-laboratory assay that measures drug response in individual primary tumor cells in short term cultures to demonstrate conserved gene expression changes, including increased HER2 activity signatures, in vandetanib treated cells, and identify features associated with vandetanib response. These results support future investigation of RET targeting strategies considering reprogrammed networks, such as activated HER2, in patients with ET resistant ER+ breast cancer.

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response

The DNA-damaging agent Gemcitabine (GEM) is a first-line treatment for pancreatic cancer, but chemoresistance is frequently observed. Several clinical trials investigate the efficacy of GEM in combination with targeted drugs, including kinase inhibitors, but the experimental evidence for such rationale is often unclear. Here, we phenotypically screened 13 human pancreatic adenocarcinoma (PDAC) cell lines against GEM in combination with 146 clinical inhibitors and observed strong synergy for the ATR kinase inhibitor Elimusertib in most cell lines. Dose-dependent phosphoproteome profiling of four ATR inhibitors following DNA damage induction by GEM revealed a strong block of the DNA damage response pathway, including phosphorylated pS468 of CHEK1, as the underlying mechanism of drug synergy. The current work provides a strong rationale for why the combination of GEM and ATR inhibition may be useful for the treatment of PDAC patients and constitutes a rich phenotypic and molecular resource for further investigating effective drug combinations.

TClC effectively suppresses the growth and metastasis of NSCLC via polypharmacology

Despite significant advances in targeted therapies and immunotherapies, non-small cell lung cancer (NSCLC) continues to present a global health challenge, with a modest five-year survival rate of 28 %, largely due to the emergence of treatment-resistant and metastatic tumors. In response, we synthesized a novel bioactive compound, ethyl 6-chlorocoumarin-3-carboxylyl L-theanine (TClC), which significantly inhibited NSCLC growth, epithelial mesenchymal transition (EMT), migration, and invasion in vitro and tumor growth and metastasis in vivo without inducing toxicity. TClC disrupts autocrine loops that promote tumor progression, particularly in stem-like CD133-positive NSCLC (CD133+ LC) cells, which are pivotal in tumor metastasis. Through targeted molecular assays, we identified direct binding targets of TClC, including Akt, NF-κB, β-catenin, EZH2, and PD-L1. This interaction not only suppresses the expression of oncogenic factors and cancer stem cell markers but also downregulates the expression of a multidrug resistance transporter, underscoring the compound's polypharmacological potential. These results position TClC as a promising candidate for NSCLC treatment, signaling a new era in the development of cancer therapies that directly target multiple critical cancer pathways.

Design, Synthesis, and SAR of Covalent KIT and PDGFRA Inhibitors─Exploring Their Potential in Targeting GIST

Gastrointestinal stromal tumors (GIST), driven by KIT and PDGFRA mutations, are the most common mesenchymal tumors of the gastrointestinal tract. Although tyrosine kinase inhibitors (TKIs) have advanced treatment, resistance mutations and off-target toxicity limit their efficacy. This study develops covalent TKIs targeting drug-resistant GIST through structure-based design, synthesis, and biological evaluation. SAR studies provided key insights into mutant KIT and PDGFRA interactions, and the first crystal structure of PDGFRA bound to a covalent inhibitor is reported. These findings highlight the promise of covalent inhibitors for overcoming resistance and advancing safer, more effective therapies for advanced GIST.

Automated High-Throughput Affinity Capture-Mass Spectrometry Platform with Data-Independent Acquisition

Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

Proteome selectivity profiling of photoaffinity probes derived from imidazopyrazine-kinase inhibitors

Kinases are attractive drug targets, but the design of highly selective kinase inhibitors remains challenging. Selectivity may be evaluated against a panel of kinases, or - preferred - in a complex proteome. Probes that allow photoaffinity-labeling of their targets can facilitate this process. Here, we report photoaffinity probes based on the imidazopyrazine scaffold, which is found in several kinase inhibitors and drugs or drug candidates. By chemical proteomics experiments, we find a range of off-targets, which vary between the different probes. In silico analysis suggests that differences between probes may be related to the size, spatial arrangement and rigidity of the imidazopyrazine and its substituent at the 1-position.

Global Profiling Lysine Reactivity and Ligandability with Oxidant-Triggered Bioconjugation Chemistry

Due to the high abundance and diverse functions of lysine residues, both in the interior and on the surface of proteins, the development of new methods to characterize their reactivity and ligandability could significantly expand the pool of druggable targets. To date, only a limited number of aminophilic electrophiles have been assessed for interactions with the lysine proteome, resulting in a substantial fraction remaining inaccessible to current probes. Here, to the best of our knowledge, we report the first oxidant-triggered bioconjugation platform for in-depth profiling of lysines. We quantified over 7000 covalently modifiable lysine residues, which significantly expands the coverage of ligandable lysines in the whole proteome. Chemical proteomics enabled the mapping of more than 100 endogenous kinases, thus providing a comprehensive landscape of ligandable catalytic lysines within the kinome. Moreover, we identified a suite of new ligandable lysines such as K60 of ENO1 and K31 of PPIA, offering insights for exploring new functional and targetable residues. These findings could provide valuable clues for the development of targeted covalent inhibitors (TCIs).

Multi-Omics Analysis Reveals Immune Infiltration and Clinical Significance of Phosphorylation Modification Enzymes in Lung Adenocarcinoma

Protein phosphorylation is a dynamic and reversible modification involved in almost all cellular processes. Numerous investigations have shown that protein phosphorylation modification enzymes (PPMEs) that regulate protein phosphorylation play an important role in the occurrence and treatment of tumors. However, there is still a lack of effective insights into the value of PPMEs in the classification and treatment of patients with lung adenocarcinoma (LUAD). Here, four topological algorithms identified 15 hub PPMEs from a protein-protein interaction (PPI) network. This PPI network was constructed using 124 PPMEs significantly correlated with 35 cancer hallmark-related pathways. Our study illustrates that these hub PPMEs can affect the survival of patients with LUAD in the form of somatic mutation or expression perturbation. Consistency clustering based on hub PPMEs recognized two phosphorylation modification subtypes (namely cluster1 and cluster2) from LUAD. Compared with patients in cluster1, the survival prognosis of patients in cluster2 is worse. This disparity is probably attributed to the higher tumor mutation burden, the higher male proportion, and the more significant expression disturbance in patients in cluster2. Moreover, phosphorylation modification subtypes also have different characteristics in terms of immune activity, immune infiltration level, immunotherapy response, and drug sensitivity. We constructed a PSig scoring system by using a principal component analysis algorithm to estimate the level of phosphorylation modification in individual LUAD patients. Patients in the high and low PSig score groups demonstrated different characteristics in terms of survival rate, tumor mutation burden, somatic gene mutation rate, immune cell abundance, and sensitivity to immunotherapy and drug treatment. This work reveals that phosphorylation plays a non-negligible role in the tumor microenvironment and immunotherapy of LUAD. Evaluating the phosphorylation status of individual LUAD patients by the PSig score can contribute to enhancing our cognition of the tumor microenvironment and guiding the formulation of more effective personalized treatment strategies.

FLT3 is associated with dendritic cell infiltration, tertiary lymphoid structure construction, and predict response to checkpoint inhibitors immunotherapy in solid cancers

The crosstalk between cancers and the immune microenvironment plays a critical role in malignant progression. FMS-like tyrosine kinase 3 (FLT3) is a frequently mutated gene in acute myeloid leukemia (AML). However, its role in solid cancers remains poorly understood. We analyzed the frequency of FLT3 alterations, its mRNA expression levels, and its prognostic implications across multiple cancer types. Additionally, we explored genes co-expressed with FLT3 and performed gene ontology analysis to identify associated biological processes. We also examined the relationship between FLT3 expression and markers of various immune cells, tertiary lymphoid structures (TLSs), and epithelial-mesenchymal transition. Furthermore, we validated these findings in our own cohort of hepatocellular carcinoma (HCC) patients. We found that FLT3 alteration and expression were both significantly upregulated in AML and were associated with poor prognosis, which is opposite to its role in solid cancers. The genes co-expressed with FLT3 in solid cancers were correlated with the regulation of the immune microenvironment. FLT3 was positively correlated with the formation of TLSs in only solid cancers, which was especially relevant to central memory T cells. We also found that FLT3 was positively correlated with the infiltration of NK cells, B cells, and DCs. It also positively correlated with the occurrence of apoptosis in solid cancers, but exhibited opposite roles in AML. The structural factors of the TLSs were positively correlated with FLT3 in solid cancers, but exhibited a negative correlation in AML. Meanwhile, we further validated the above conclusions in our own HCC cohort and demonstrated that FLT3 could serve as a predictive indicator of PD-1 treatment efficacy in HCC. In summary, the role of FLT3 is different in AML and solid cancers. FLT3 is associated with dendritic cell infiltration, tertiary lymphoid structure construction, and predict response to checkpoint inhibitors immunotherapy in HCC.

Aberrant Activation of Wound-Healing Programs within the Metastatic Niche Facilitates Lung Colonization by Osteosarcoma Cells

PURPOSE

Lung metastasis is responsible for nearly all deaths caused by osteosarcoma, the most common pediatric bone tumor. How malignant bone cells coerce the lung microenvironment to support metastatic growth is unclear. The purpose of this study is to identify metastasis-specific therapeutic vulnerabilities by delineating the cellular and molecular mechanisms underlying osteosarcoma lung metastatic niche formation.

EXPERIMENTAL DESIGN

Using single-cell RNA sequencing, we characterized genome- and tissue-wide molecular changes induced within lung tissues by disseminated osteosarcoma cells in both immunocompetent murine models of metastasis and patient samples. We confirmed transcriptomic findings at the protein level and determined spatial relationships with multiparameter immunofluorescence and spatial transcriptomics. Based on these findings, we evaluated the ability of nintedanib, a kinase inhibitor used to treat patients with pulmonary fibrosis, to impair metastasis progression in both immunocompetent murine osteosarcoma and immunodeficient human xenograft models. Single-nucleus and spatial transcriptomics were used to perform molecular pharmacodynamic studies that define the effects of nintedanib on tumor and nontumor cells within the metastatic microenvironment.

RESULTS

Osteosarcoma cells induced acute alveolar epithelial injury upon lung dissemination. Single-cell RNA sequencing demonstrated that the surrounding lung stroma adopts a chronic, nonresolving wound-healing phenotype similar to that seen in other models of lung injury. Accordingly, the metastasis-associated lung demonstrated marked fibrosis, likely because of the accumulation of pathogenic, profibrotic, partially differentiated epithelial intermediates and macrophages. Our data demonstrated that nintedanib prevented metastatic progression in multiple murine and human xenograft models by inhibiting osteosarcoma-induced fibrosis.

CONCLUSIONS

Fibrosis represents a targetable vulnerability to block the progression of osteosarcoma lung metastasis. Our data support a model wherein interactions between osteosarcoma cells and epithelial cells create a prometastatic niche by inducing tumor deposition of extracellular matrix proteins such as fibronectin that is disrupted by the antifibrotic tyrosine kinase inhibitor (TKI) nintedanib. Our data shed light on the non-cell-autonomous effects of TKIs on metastasis and provide a roadmap for using single-cell and spatial transcriptomics to define the mechanism of action of TKI on metastases in animal models.

Phosphoproteomics profiling of sorafenib-resistant hepatocellular carcinoma patient-derived xenografts reveals potential therapeutic strategies

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with poor prognosis. Sorafenib, a first-line treatment for advanced HCC, has shown limited clinical benefits due to the onset of drug resistance. Thus, it is imperative to comprehend the mechanisms underlying sorafenib resistance and explore strategies to overcome or delay it. Here, we established HCC patient-derived xenograft (PDX) models with acquired resistance to sorafenib and performed comprehensive proteomic and phosphoproteomic analyses on these models. The active cell cycle pathway along with the active cyclin-dependent kinase CDK1 and DNA-dependent protein kinase PRKDC was identified through KEGG pathway enrichment and kinase substrate enrichment analyses. Upon investigating the potential of combining sorafenib with putative kinase inhibitors, we found that the combination displays synergistic anti-proliferative effects in the sorafenib-resistant liver cancer cell line, thus providing a proof of concept for phosphoproteomic-guided design of precision medicine.

Chemoproteomic Profiling of C. albicans for Characterization of Anti-fungal Kinase Inhibitors

Candida albicans is a growing health concern as the leading causal agent of systemic candidiasis, a life-threatening fungal infection with a mortality rate of ~40% despite best available therapy. Yck2, a fungal casein kinase 1 (CK1) family member, is the cellular target of inhibitors YK-I-02 (YK) and MN-I-157 (MN). Here, multiplexed inhibitor beads paired with mass spectrometry (MIB/MS) employing ATP-competitive kinase inhibitors were used to define the selectivity of these Yck2 inhibitors across the global C. albicans proteome. The MIB matrix captured 89% of the known and predicted C. albicans protein kinases present in cell lysate. In MIB/MS competition assays, YK and MN demonstrated exquisite selectivity across the C. albicans fungal kinome with target engagement of only three CK1 homologs (Yck2, Yck22, and Hrr25) and a homolog of human p38α (Hog1). Additional chemoproteomics using a custom MN-kinobead identified only one additional C. albicans protein, confirming its remarkable fungal proteome-wide selectivity. To identify new Yck2 inhibitors with selectivity over Hog1, thirteen human CK1 kinase inhibitors were profiled for fungal kinase-binding activity using MIB/MS competition assays and in-cell NanoBRET target engagement assays. A new chemotype of family-selective Yck2 inhibitors with antifungal activity was identified. Together, these findings expand the application of MIB/MS proteomic profiling for non-human kinomes and demonstrate its utility in the discovery and development of selective inhibitors of fungal kinases with potential antimicrobial activity.

PTMNavigator: interactive visualization of differentially regulated post-translational modifications in cellular signaling pathways

Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform that offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams. PTMNavigator provides ~3000 canonical pathways from manually curated databases, enabling users to modify and create custom diagrams tailored to their data. Additionally, PTMNavigator automatically runs kinase and pathway enrichment algorithms whose results are directly integrated into the visualization. This offers a comprehensive view of the intricate relationship between PTMs and signaling pathways. We demonstrate the utility of PTMNavigator by applying it to two phosphoproteomics datasets, showing how it can enhance pathway enrichment analysis, visualize how drug treatments result in a discernable flow of PTM-driven signaling, and aid in proposing extensions to existing pathways. By enhancing our understanding of cellular signaling dynamics and facilitating the discovery of PTM-pathway interactions, PTMNavigator advances our knowledge of PTM biology and its implications in health and disease.

Targeting FMS-like tyrosine kinase 3 (FLT3) in acute myeloid leukemia: Novel molecular approaches and therapeutic challenges

Acute myeloid leukemia (AML), a heterogeneous hematologic malignancy, has generally a poor prognosis despite the recent advancements in diagnostics and treatment. Genetic instability, particularly mutations in the FMS-like tyrosine kinase 3 (FLT3) gene, is associated with severe outcomes. Approximately 30 % of AML patients harbor FLT3 mutations, which have been linked to higher relapse and reduced survival rates. Traditional AML treatments employ cytarabine and anthracyclines drugs. Furthermore, the development of FLT3 inhibitors has significantly improved therapy for FLT3-mutated AML patients. For example, the introduction of midostaurin, the first FLT3 inhibitor, improved patient outcomes. However, resistant AML cell clones continue to pose a challenge to the success of AML treatment. This review discusses FLT3 kinase, mutations, and role in AML pathogenesis. It explores the molecular mechanisms of FLT3 activation, signaling pathways, and the structure and function of the FLT3 receptor. Current and emerging therapeutic approaches are presented, while highlighting the latest FLT3 inhibitors in clinical use, and strategies to overcome drug resistance. Future directions, including personalized therapies and novel drug designs, are examined to provide updated insights into FLT3-targeted treatments. This comprehensive review aims to guide clinicians and researchers in the development of innovative therapies to improve AML patient outcomes.

Selumetinib in adults with NF1 and inoperable plexiform neurofibroma: a phase 2 trial

The MEK inhibitor selumetinib induces objective responses and provides clinical benefit in children with neurofibromatosis type 1 (NF1) and inoperable plexiform neurofibromas (PNs). To evaluate whether similar outcomes were possible in adult patients, in whom PN growth is generally slower than in pediatric patients, we conducted an open-label phase 2 study of selumetinib in adults with NF1 PNs. The study was designed to evaluate objective response rate (primary objective), tumor volumetric responses, patient-reported outcomes and pharmacodynamic effects in PN biopsies. The objective response rate was 63.6% (21/33 participants). Median maximal PN volume decrease was 23.6% (range: -48.1% to 5.5%). No disease progression relative to baseline PN volumes occurred before data cutoff, with a median of 28 cycles completed (range: 1-78, 28 d per cycle). Participants experienced decreased tumor pain intensity and pain interference. Adverse events (AEs) were similar to those of the pediatric trial; acneiform rash was the most prevalent AE. Phosphorylation ratios of ERK1/2 decreased significantly (ERK1 median change: -64.6% (range: -99.5% to 90.7%), ERK2 median change: -57.3% (range: -99.9% to 84.4%)) in paired PN biopsies (P ≤ 0.001 for both isoforms) without compensatory phosphorylation of AKT1/2/3. The sustained PN volume decreases, associated improvement in pain and manageable AE profile indicate that selumetinib provides benefit to adults with NF1 and inoperable PNs. ClinicalTrials.gov identifier: NCT02407405 .

Longitudinal phosphoproteomics reveals the PI3K-PAK1 axis as a potential target for recurrent colorectal liver metastases

The resistance of colorectal cancer liver metastases (CRLMs) to 5-fluorouracil (5-FU) chemotherapy remains a significant global health challenge. We investigated the phosphoproteomic dynamics of serial tissue sections obtained from initial metastases and recurrent tumors collected from 24 patients to address this unmet need for innovative therapeutic strategies for patients with CRLM with a poor prognosis. Our analysis revealed the activation of PAK kinase in patients with CRLM with a poor prognosis. Using an unbiased computational approach, we conducted a correlation analysis between PAK1 kinase activity and 545 drug sensitivity profiles across 35 colorectal cancer cell lines and identified PI3K inhibitors as potential therapeutic candidates. The efficacy of the FDA-approved PI3K inhibitor copanlisib was validated in 5-FU-resistant cell lines with high PAK1 kinase activity both in vitro and in vivo. This study presents an effective strategy for drug target discovery based on kinase activity, and the concept of this approach is widely applicable.

Evolutionary analysis of Quinone Reductases 1 and 2 suggests that NQO2 evolved to function as a pseudoenzyme

Quinone reductases 1 and 2 (NQO1 and NQO2) are paralogous FAD-linked enzymes found in all amniotes. NQO1 and NQO2 have similar structures, and both catalyze the reduction of quinones and other electrophiles; however, the two enzymes differ in their cosubstrate preference. While NQO1 can use both redox couples NADH and NADPH, NQO2 is almost inactive with these cosubstrates and instead must use dihydronicotinamide riboside (NRH) and small synthetic cosubstrates such as N-benzyl-dihydronicotinamide (BNAH) for efficient catalysis. We used ancestral sequence reconstruction to investigate the catalytic properties of a predicted common ancestor and two additional ancestors from each of the evolutionary pathways to extant NQO1 and NQO2. In all cases, the small nicotinamide cosubstrates NRH and BNAH were good cosubstrates for the common ancestor and the enzymes along both the NQO1 and NQO2 lineages. In contrast, with NADH as cosubstrate, extant NQO1 evolved to a catalytic efficiency 100 times higher than the common ancestor, while NQO2 has evolved to a catalytic efficiency 3000 times lower than the common ancestor. The evolutionary analysis combined with site-directed mutagenesis revealed a potential site of interaction for the ADP portion of NAD(P)H in NQO1 that is altered in charge and structure in NQO2. The results indicate that while NQO1 evolved to have greater efficiency with NAD(P)H, befitting an enzymatic function in cells, NQO2 was under selective pressure to acquire extremely low catalytic efficiency with NAD(P)H. These divergent trajectories have implications for the functions of both enzymes.

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.

Multi-layered proteomics identifies insulin-induced upregulation of the EphA2 receptor via the ERK pathway which is dependent on low IGF1R level

Insulin resistance impairs the cellular insulin response, and often precedes metabolic disorders, like type 2 diabetes, impacting an increasing number of people globally. Understanding the molecular mechanisms in hepatic insulin resistance is essential for early preventive treatments. To elucidate changes in insulin signal transduction associated with hepatocellular resistance, we employed a multi-layered mass spectrometry-based proteomics approach focused on insulin receptor (IR) signaling at the interactome, phosphoproteome, and proteome levels in a long-term hyperinsulinemia-induced insulin-resistant HepG2 cell line with a knockout of the insulin-like growth factor 1 receptor (IGF1R KO). The analysis revealed insulin-stimulated recruitment of the PI3K complex in both insulin-sensitive and -resistant cells. Phosphoproteomics showed attenuated signaling via the metabolic PI3K-AKT pathway but sustained extracellular signal-regulated kinase (ERK) activity in insulin-resistant cells. At the proteome level, the ephrin type-A receptor 2 (EphA2) showed an insulin-induced increase in expression, which occurred through the ERK signaling pathway and was concordantly independent of insulin resistance. Induction of EphA2 by insulin was confirmed in additional cell lines and observed uniquely in cells with high IR-to-IGF1R ratio. The multi-layered proteomics dataset provided insights into insulin signaling, serving as a resource to generate and test hypotheses, leading to an improved understanding of insulin resistance.

Spatiotemporal control of kinases and the biomolecular tools to trace activity

The delicate balance of cell physiology is implicitly tied to the expression and activation of proteins. Post-translational modifications offer a tool to dynamically switch protein activity on and off to orchestrate a wide range of protein-protein interactions to tune signal transduction during cellular homeostasis and pathological responses. There is a growing acknowledgment that subcellular locations of kinases define the spatial network of potential scaffolds, adaptors, and substrates. These highly ordered and localized biomolecular microdomains confer a spatially distinct bias in the outcomes of kinase activity. Furthermore, they may hold essential clues to the underlying mechanisms that promote disease. Developing tools to dissect the spatiotemporal activation of kinases is critical to reveal these mechanisms and promote the development of spatially targeted kinase inhibitors. Here, we discuss the spatial regulation of kinases, the tools used to detect their activity, and their potential impact on human health.

Dynamic Interplay of Loop Motions Governs the Molecular Level Regulatory Dynamics in Spleen Tyrosine Kinase: Insights from Molecular Dynamics Simulations

The spleen tyrosine kinase (Syk) is a key regulator in immune cell signaling and is linked to various mechanisms in cancer and neurodegenerative diseases. Although most computational research on Syk focuses on novel drug design, the molecular-level regulatory dynamics remain unexplored. In this study, we utilized 5 × 1 μs all-atom molecular dynamics simulations of the Syk kinase domain, examining it in combinations of activation segment phosphorylated/unphosphorylated (at Tyr525, Tyr526) and the "DFG"-Asp protonated/deprotonated (at Asp512) states to investigate conformational variations and regulatory dynamics of various loops and motifs within the kinase domain. Our findings revealed that the formation and disruption of several electrostatic interactions among residues within and near the activation segment likely influenced its dynamics. The protein structure network analysis indicated that the N-terminal and C-terminal anchors were stabilized by connections with the nearby stable helical regions. The P-loop showed conformational variation characterized by movements toward and away from the conserved "HRD"-motif. Additionally, there was a significant correlation between the movement of the β3-αC loop and the P-loop, which controls the dimensions of the adenine-binding cavity of the C-spine region. Overall, understanding these significant motions of the Syk kinase domain enhances our knowledge of its functional regulatory mechanism and can guide future research.

Stimulation of insulin secretion induced by low 4-cresol dose involves the RPS6KA3 signalling pathway

4-cresol (4-methylphenol, p-cresol) is a xenobiotic substance negatively correlated with type 2 diabetes and associated with health improvement in preclinical models of diabetes. We aimed at refining our understanding of the physiological role of this metabolite and identifying potential signalling mechanisms. Functional studies revealed that 4-cresol does not deteriorate insulin sensitivity in human primary adipocytes and exhibits an additive effect to that of insulin on insulin sensitivity in mouse C2C12 myoblasts. Experiments in mouse isolated islets showed that 4-cresol potentiates glucose induced insulin secretion. We demonstrated the absence of off target effects of 4-cresol on a panel of 44 pharmacological compounds. Screening large panels of 241 G protein-coupled receptors (GPCRs) and 468 kinases identified binding of 4-cresol only to TNK1, EIF2AK4 (GCN2) and RPS6KA3 (RSK2), a kinase strongly expressed in human and rat pancreatic islets. Islet expression of RPS6KA3 is reduced in spontaneously diabetic rats chronically treated with 4-cresol and Rps6ka3 deficient mice exhibit reduction in both body weight and fasting glycemia, modest improvement in glycemic control and enhanced insulin release in vivo. Similar to low doses of 4-cresol, incubation of isolated rat islets with low concentrations of the RPS6KA3 inhibitor BIX 02565 stimulates both glucose induced insulin secretion and β-cell proliferation. These results provide further information on the role of low 4-cresol doses in the regulation of insulin secretion.

Streamlined analysis of drug targets by proteome integral solubility alteration indicates organ-specific engagement

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

Discovery of BAY-405: An Azaindole-Based MAP4K1 Inhibitor for the Enhancement of T-Cell Immunity against Cancer

Mitogen-activated protein kinase kinase kinase kinase 1 (MAP4K1) is a serine/threonine kinase that acts as an immune checkpoint downstream of T-cell receptor stimulation. MAP4K1 activity is enhanced by prostaglandin E2 (PGE2) and transforming growth factor beta (TGFβ), immune modulators commonly present in the tumor microenvironment. Therefore, its pharmacological inhibition is an attractive immuno-oncology concept for inducing therapeutic T-cell responses in cancer patients. Here, we describe the systematic optimization of azaindole-based lead compound 1, resulting in the discovery of potent and selective MAP4K1 inhibitor 38 (BAY-405) that displays nanomolar potency in biochemical and cellular assays as well as in vivo exposure after oral dosing. BAY-405 enhances T-cell immunity and overcomes the suppressive effect of PGE2 and TGFβ. Treatment of tumor-bearing mice shows T-cell-dependent antitumor efficacy. MAP4K1 inhibition in conjunction with PD-L1 blockade results in a superior antitumor impact, illustrating the complementarity of the single agent treatments.

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Reversible covalent c-Jun N-terminal kinase inhibitors targeting a specific cysteine by precision-guided Michael-acceptor warheads

There has been a surge of interest in covalent inhibitors for protein kinases in recent years. Despite success in oncology, the off-target reactivity of these molecules is still hampering the use of covalent warhead-based strategies. Herein, we disclose the development of precision-guided warheads to mitigate the off-target challenge. These reversible warheads have a complex and cyclic structure with optional chirality center and tailored steric and electronic properties. To validate our proof-of-concept, we modified acrylamide-based covalent inhibitors of c-Jun N-terminal kinases (JNKs). We show that the cyclic warheads have high resilience against off-target thiols. Additionally, the binding affinity, residence time, and even JNK isoform specificity can be fine-tuned by adjusting the substitution pattern or using divergent and orthogonal synthetic elaboration of the warhead. Taken together, the cyclic warheads presented in this study will be a useful tool for medicinal chemists for the deliberate design of safer and functionally fine-tuned covalent inhibitors.

Endothelium as a Source of Cardiovascular Toxicity From Antitumor Kinase Inhibitors

Kinase inhibitors (KIs) targeting oncogenic molecular pathways have revolutionized cancer therapy. By directly targeting specific tumor-driving kinases, targeted therapies have fewer side effects compared with chemotherapy. Despite the enhanced specificity, cardiovascular side effects have emerged with many targeted cancer therapies that limit long-term outcomes in patients with cancer. Endothelial cells lining all blood vessels are critical to cardiovascular health and are also exposed to circulating levels of systemic anticancer therapies. Both on- and off-target perturbation of signaling pathways from KIs can cause endothelial dysfunction, resulting in cardiovascular toxicity. As such, the endothelium is a potential source, and also a therapeutic target for prevention, of cardiovascular toxicity. In this review, we examine the evidence for KI-induced endothelial cell dysfunction as a mechanism for the cardiovascular toxicities of vascular endothelial growth factor inhibitors, BCR-Abl (breakpoint cluster region-Abelson proto-oncogene) KIs, Bruton tyrosine inhibitors, and emerging information regarding endothelial toxicity of newer classes of KIs.

Impact of Optimized Ku-DNA Binding Inhibitors on the Cellular and In Vivo DNA Damage Response

Background: DNA-dependent protein kinase (DNA-PK) is a validated cancer therapeutic target involved in DNA damage response (DDR) and non-homologous end-joining (NHEJ) repair of DNA double-strand breaks (DSBs). Ku serves as a sensor of DSBs by binding to DNA ends and activating DNA-PK. Inhibition of DNA-PK is a common strategy to block DSB repair and improve efficacy of ionizing radiation (IR) therapy and radiomimetic drug therapies. We have previously developed Ku-DNA binding inhibitors (Ku-DBis) that block in vitro and cellular NHEJ activity, abrogate DNA-PK autophosphorylation, and potentiate cellular sensitivity to IR. Results and Conclusions: Here we report the discovery of oxindole Ku-DBis with improved cellular uptake and retained potent Ku-inhibitory activity. Variable monotherapy activity was observed in a panel of non-small cell lung cancer (NSCLC) cell lines, with ATM-null cells being the most sensitive and showing synergy with IR. BRCA1-deficient cells were resistant to single-agent treatment and antagonistic when combined with DSB-generating therapies. In vivo studies in an NSCLC xenograft model demonstrated that the Ku-DBi treatment blocked IR-dependent DNA-PKcs autophosphorylation, modulated DDR, and reduced tumor cell proliferation. This represents the first in vivo demonstration of a Ku-targeted DNA-binding inhibitor impacting IR response and highlights the potential therapeutic utility of Ku-DBis for cancer treatment.

Spatiotemporal regulation of MELK during mitosis

Maternal Embryonic Leucine Zipper Kinase (MELK) has been studied intensively in recent years due to its overexpression in multiple cancers. However, the cell biology of MELK remains less characterized despite its well-documented association with mitosis. Here we report a distinctive pattern of human MELK that translocates from the cytoplasm to cell cortex within 3 min of anaphase onset. The cortex association lasts about 30 min till telophase. The spatiotemporal specific localization of MELK depends on the interaction between its Threonine-Proline (TP) rich domain and kinase associated 1 (KA1) domain, which is regulated by CDK1 kinase and PP4 protein phosphatase. KA1 domains are known to regulate kinase activities through various intramolecular interactions. Our results revealed a new role for KA1 domain to control subcellular localization of a protein kinase.

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Targeting cyclin-dependent kinases: From pocket specificity to drug selectivity

The development of selective modulators of cyclin-dependent kinases (CDKs), a kinase family with numerous members and functional variations, is a significant preclinical challenge. Recent advancements in crystallography have revealed subtle differences in the highly conserved CDK pockets. Exploiting these differences has proven to be an effective strategy for achieving excellent drug selectivity. While previous reports briefly discussed the structural features that lead to selectivity in individual CDK members, attaining inhibitor selectivity requires consideration of not only the specific structures of the target CDK but also the features of off-target members. In this review, we summarize the structure-activity relationships (SARs) that influence selectivity in CDK drug development and analyze the pocket features that lead to selectivity using molecular-protein binding models. In addition, in recent years, novel CDK modulators have been developed, providing more avenues for achieving selectivity. These cases were also included. We hope that these efforts will assist in the development of novel CDK drugs.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines

Numerous aspects of cellular signaling are regulated by the kinome-the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC.

The effect of replication protein A inhibition and post-translational modification on ATR kinase signaling

The ATR kinase responds to elevated levels of single-stranded DNA (ssDNA) to activate the G2/M checkpoint, regulate origin utilization, preserve fork stability, and allow DNA repair to ensure genome integrity. The intrinsic replication stress in cancer cells makes this pathway an attractive therapeutic target. The ssDNA that drives ATR signaling is sensed by the ssDNA-binding protein replication protein A (RPA), which acts as a platform for ATRIP recruitment and subsequent ATR activation by TopBP1. We have developed chemical RPA inhibitors (RPAi) that block RPA-ssDNA interactions (RPA-DBi) and RPA protein-protein interactions (RPA-PPIi); both activities are required for ATR activation. Here, we biochemically reconstitute the ATR kinase signaling pathway and demonstrate that RPA-DBi and RPA-PPIi abrogate ATR-dependent phosphorylation of target proteins with selectivity advantages over active site ATR inhibitors. We demonstrate that RPA post-translational modifications (PTMs) impact ATR kinase activation but do not alter sensitivity to RPAi. Specifically, phosphorylation of RPA32 and TopBP1 stimulate, while RPA70 acetylation does not affect ATR phosphorylation of target proteins. Collectively, this work reveals the RPAi mechanism of action to inhibit ATR signaling that can be regulated by RPA PTMs and offers insight into the anti-cancer activity of ATR pathway-targeted cancer therapeutics.

The prospect of substrate-based kinase inhibitors to improve target selectivity and overcome drug resistance

Human kinases are recognized as one of the most important drug targets associated with cancer. There are >80 FDA-approved kinase inhibitors to date, most of which work by inhibiting ATP binding to the kinase. However, the frequent development of single-point mutations within the kinase domain has made overcoming drug resistance a major challenge in drug discovery today. Targeting the substrate site of kinases can offer a more selective and resistance-resilient solution compared to ATP inhibition but has traditionally been challenging. However, emerging technologies for the discovery of drug leads using recombinant display and stabilization of lead compounds have increased interest in targeting the substrate site of kinases. This review discusses recent advances in the substrate-based inhibition of protein kinases and the potential of such approaches for overcoming the emergence of resistance.

Off-targets of BRAF inhibitors disrupt endothelial signaling and vascular barrier function

Targeted therapies against mutant BRAF are effectively used in combination with MEK inhibitors (MEKi) to treat advanced melanoma. However, treatment success is affected by resistance and adverse events (AEs). Approved BRAF inhibitors (BRAFi) show high levels of target promiscuity, which can contribute to these effects. The blood vessel lining is in direct contact with high plasma concentrations of BRAFi, but effects of the inhibitors in this cell type are unknown. Hence, we aimed to characterize responses to approved BRAFi for melanoma in the vascular endothelium. We showed that clinically approved BRAFi induced a paradoxical activation of endothelial MAPK signaling. Moreover, phosphoproteomics revealed distinct sets of off-targets per inhibitor. Endothelial barrier function and junction integrity were impaired upon treatment with vemurafenib and the next-generation dimerization inhibitor PLX8394, but not with dabrafenib or encorafenib. Together, these findings provide insights into the surprisingly distinct side effects of BRAFi on endothelial signaling and functionality. Better understanding of off-target effects could help to identify molecular mechanisms behind AEs and guide the continued development of therapies for BRAF-mutant melanoma.

Impact of protein and small molecule interactions on kinase conformations

Protein kinases act as central molecular switches in the control of cellular functions. Alterations in the regulation and function of protein kinases may provoke diseases including cancer. In this study we investigate the conformational states of such disease-associated kinases using the high sensitivity of the kinase conformation (KinCon) reporter system. We first track BRAF kinase activity conformational changes upon melanoma drug binding. Second, we also use the KinCon reporter technology to examine the impact of regulatory protein interactions on LKB1 kinase tumor suppressor functions. Third, we explore the conformational dynamics of RIP kinases in response to TNF pathway activation and small molecule interactions. Finally, we show that CDK4/6 interactions with regulatory proteins alter conformations which remain unaffected in the presence of clinically applied inhibitors. Apart from its predictive value, the KinCon technology helps to identify cellular factors that impact drug efficacies. The understanding of the structural dynamics of full-length protein kinases when interacting with small molecule inhibitors or regulatory proteins is crucial for designing more effective therapeutic strategies.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

The Effect of Replication Protein A Inhibition and Post-Translational Modification on ATR Kinase Signaling

The ATR kinase responds to elevated levels of single-stranded DNA (ssDNA) to activate the G2/M checkpoint, regulate origin utilization, preserve fork stability, and allow DNA repair towards ensuring genome integrity. The intrinsic replication stress in cancer cells makes this pathway an attractive therapeutic target. The ssDNA that drives ATR signaling is sensed by the ssDNA-binding protein replication protein A (RPA), which acts as a platform for ATRIP recruitment and subsequent ATR activation by TopBP1. We have developed chemical RPA inhibitors (RPAi) that block RPA-ssDNA interactions, termed RPA-DBi, and RPA protein-protein interactions, termed RPA-PPIi; both activities are required for ATR activation. Here, we employ a biochemically reconstituted ATR kinase signaling pathway and demonstrate that both RPA-DBi and RPA-PPIi abrogate ATR-dependent phosphorylation of downstream target proteins. We demonstrate that RPA post-translational modifications (PTMs) impact ATR kinase activation but do not alter sensitivity to RPAi. Specifically, phosphorylation of RPA32 and TopBP1 stimulate, while RPA70 acetylation has no effect on ATR phosphorylation of target proteins. Collectively, this work reveals the RPAi mechanism of action to inhibit ATR signaling that can be regulated by RPA PTMs and offers insight into the anti-cancer activity of ATR pathway targeted cancer therapeutics.

Occurrence of "Natural Selection" in Successful Small Molecule Drug Discovery

Published compounds from ChEMBL version 32 are used to seek evidence for the occurrence of "natural selection" in drug discovery. Three measures of natural product (NP) character were applied, to compare time- and target-matched compounds reaching the clinic (clinical compounds in phase 1-3 development and approved drugs) with background compounds (reference compounds). Pseudo-NPs (PNPs), containing NP fragments combined in ways inaccessible by nature, are increasing over time, reaching 67% of clinical compounds first disclosed since 2010. PNPs are 54% more likely to be found in post-2008 clinical versus reference compounds. The majority of target classes show increased clinical compound NP character versus their reference compounds. Only 176 NP fragments appear in >1000 clinical compounds published since 2008, yet these make up on average 63% of the clinical compound's core scaffolds. There is untapped potential awaiting exploitation, by applying nature's building blocks─"natural intelligence"─to drug design.