The target landscape of clinical kinase drugs

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.

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).

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.

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.

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.

Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas With EWSR1 and CIC::DUX4 Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers

Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.

[Recent advances in protein precipitation-based methods for drug-target screening]

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Insights into the structural and functional activities of forgotten Kinases: PCTAIREs CDKs

In cells, signal transduction heavily relies on the intricate regulation of protein kinases, which provide the fundamental framework for modulating most signaling pathways. Dysregulation of kinase activity has been implicated in numerous pathological conditions, particularly in cancer. The druggable nature of most kinases positions them into a focal point during the process of drug development. However, a significant challenge persists, as the role and biological function of nearly one third of human kinases remains largely unknown.Within this diverse landscape, cyclin-dependent kinases (CDKs) emerge as an intriguing molecular subgroup. In human, this kinase family encompasses 21 members, involved in several key biological processes. Remarkably, 13 of these CDKs belong to the category of understudied kinases, and only 5 having undergone broad investigation to date. This knowledge gap underscores the pressing need to delve into the study of these kinases, starting with a comprehensive review of the less-explored ones.Here, we will focus on the PCTAIRE subfamily of CDKs, which includes CDK16, CDK17, and CDK18, arguably among the most understudied CDKs members. To contextualize PCTAIREs within the spectrum of human pathophysiology, we conducted an exhaustive review of the existing literature and examined available databases. This approach resulted in an articulate depiction of these PCTAIREs, encompassing their expression patterns, 3D configurations, mechanisms of activation, and potential functions in normal tissues and in cancer.We propose that this effort offers the possibility of identifying promising areas of future research that extend from basic research to potential clinical and therapeutic applications.

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have leveraged a microfluidics-based Plate-Seq technology to survey drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Built on this dataset, we implemented an efficient computational framework to define a tissue-specific protein activity landscape of these drugs and reported almost 50 million differential protein activities derived from drug perturbations vs. vehicle controls. These analyses revealed thousands of highly reproducible and novel, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide drug functional network, characterization of MoA-related drug clusters and off-target effects, dramatical expansion of druggable human proteome, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins, most never reported before. The drug perturbation profile resource described here represents the first, large-scale, whole-genome-wide, RNA-Seq based dataset assembled to date, with the proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, facilitates mechanistic exploration of drug functions, supports systematic and quantitative approaches to precision oncology, and serves as a rich data foundation for drug discovery.

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery

The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus providing a therapeutic target for leukemia treatment. In this study, we introduced a sophisticated virtual screening strategy combined with biological evaluations to discover potent LCK inhibitors. Our initial approach involved utilizing the PLANET algorithm to assess and contrast various scoring methodologies suitable for LCK inhibitor screening. After effectively evaluating PLANET, we progressed to devise a virtual screening workflow that synergistically combines the strengths of PLANET with the capabilities of Schrödinger's suite. This integrative strategy led to the efficient identification of four potential LCK inhibitors. Among them, compound 1232030-35-1 stood out as the most promising candidate with an IC50 of 0.43 nM. Further in vitro bioassays revealed that 1232030-35-1 exhibited robust antiproliferative effects on T-ALL cells, which was attributed to its ability to suppress the phosphorylations of key molecules in the LCK signaling pathway. More importantly, 1232030-35-1 treatment demonstrated profound in vivo antileukemia efficacy in a human T-ALL xenograft model. In addition, complementary molecular dynamics simulations provided deeper insight into the binding kinetics between 1232030-35-1 and LCK, highlighting the formation of a hydrogen bond with Met319. Collectively, our study established a robust and effective screening strategy that integrates AI-driven and conventional methodologies for the identification of LCK inhibitors, positioning 1232030-35-1 as a highly promising and novel drug-like candidate for potential applications in treating T-ALL.

Lestaurtinib's antineoplastic activity converges on JAK/STAT signaling to inhibit advanced forms of therapy resistant ovarian cancer

Ovarian cancer is the deadliest gynecological malignancy, owing to its late-stage diagnosis and high rates of recurrence and resistance following standard-of-care treatment, highlighting the need for novel treatment approaches. Through an unbiased drug screen, we identified the kinase inhibitor, lestaurtinib, as a potent antineoplastic agent for chemotherapy- and PARP-inhibitor (PARPi)-sensitive and -resistant ovarian cancer cells and patient derived xenografts (PDXs). RNA-sequencing revealed that lestaurtinib potently suppressed JAK/STAT signaling and lestaurtinib efficacy was shown to be directly related to JAK/STAT pathway activity in cell lines and PDX models. Most ovarian cancer cells exhibited constitutive JAK/STAT pathway activation and genetic loss of STAT1 and STAT3 resulted in growth inhibition. Lestaurtinib also displayed synergy when combined with cisplatin and olaparib, including in a model of PARPi resistance. In contrast, the most well-known JAK/STAT inhibitor, ruxolitinib, lacked antineoplastic activity against all ovarian cancer cell lines and PDX models tested. This divergent behavior was reflected in the ability of lestaurtinib to block both Y701/705 and S727 phosphorylation of STAT1 and STAT3, whereas ruxolitinib failed to block S727. Consistent with these findings, lestaurtinib additionally inhibited JNK and ERK activity, leading to more complete suppression of STAT phosphorylation. Concordantly, combinatorial treatment with ruxolitinib and a JNK or ERK inhibitor resulted in synergistic antineoplastic effects at dose levels where single agents were ineffective. Taken together, these findings indicate that lestaurtinib, and other treatments that converge on JAK/STAT signaling, are worthy of further pre-clinical and clinical exploration for the treatment of highly aggressive and advanced forms of ovarian cancer.

Statement of significance

Lestaurtinib is a novel inhibitor of ovarian cancer, including chemotherapy- and PARPi-resistant models, that acts through robust inhibition of the JAK/STAT pathway and synergizes with standard-of-care agents at clinically relevant concentrations.

Determining the ERK-regulated phosphoproteome driving KRAS-mutant cancer

To delineate the mechanisms by which the ERK1 and ERK2 mitogen-activated protein kinases support mutant KRAS-driven cancer growth, we determined the ERK-dependent phosphoproteome in KRAS-mutant pancreatic cancer. We determined that ERK1 and ERK2 share near-identical signaling and transforming outputs and that the KRAS-regulated phosphoproteome is driven nearly completely by ERK. We identified 4666 ERK-dependent phosphosites on 2123 proteins, of which 79 and 66%, respectively, were not previously associated with ERK, substantially expanding the depth and breadth of ERK-dependent phosphorylation events and revealing a considerably more complex function for ERK in cancer. We established that ERK controls a highly dynamic and complex phosphoproteome that converges on cyclin-dependent kinase regulation and RAS homolog guanosine triphosphatase function (RHO GTPase). Our findings establish the most comprehensive molecular portrait and mechanisms by which ERK drives KRAS-dependent pancreatic cancer growth.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Exploring affinity chromatography in proteomics: A comprehensive review

Over the past decades, the proteomics field has undergone rapid growth. Progress in mass spectrometry and bioinformatics, together with separation methods, has brought many innovative approaches to the study of the molecular biology of the cell. The potential of affinity chromatography was recognized immediately after its first application in proteomics, and since that time, it has become one of the cornerstones of many proteomic protocols. Indeed, this chromatographic technique exploiting the specific binding between two molecules has been employed for numerous purposes, from selective removal of interfering (over)abundant proteins or enrichment of scarce biomarkers in complex biological samples to mapping the post-translational modifications and protein interactions with other proteins, nucleic acids or biologically active small molecules. This review presents a comprehensive survey of this versatile analytical tool in current proteomics. To navigate the reader, the haphazard space of affinity separations is classified according to the experiment's aims and the separated molecule's nature. Different types of available ligands and experimental strategies are discussed in further detail for each of the mentioned procedures.

An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors

More than 500 kinases are implicated in the control of most cellular process in mammals, and deregulation of their activity is linked to cancer and inflammatory disorders. 80 clinical kinase inhibitors (CKIs) have been approved for clinical use and hundreds are in various stages of development. However, CKIs inhibit other kinases in addition to the intended target(s), causing both enhanced clinical effects and undesired side effects that are only partially predictable based on in vitro selectivity profiling. Here, we report an integrative approach grounded on the use of chromatin modifications as unbiased, information-rich readouts of the functional effects of CKIs on macrophage activation. This approach exceeded the performance of transcriptome-based approaches and allowed us to identify similarities and differences among CKIs with identical intended targets, to recognize novel CKI specificities and to pinpoint CKIs that may be repurposed to control inflammation, thus supporting the utility of this strategy to improve selection and use of CKIs in clinical settings.

Neratinib, a pan ERBB/HER inhibitor, restores sensitivity of PTEN-null, BRAFV600E melanoma to BRAF/MEK inhibition

Introduction

Approximately 50% of melanomas harbor an activating BRAFV600E mutation. Standard of care involves a combination of inhibitors targeting mutant BRAF and MEK1/2, the substrate for BRAF in the MAPK pathway. PTEN loss-of-function mutations occur in ~40% of BRAFV600E melanomas, resulting in increased PI3K/AKT activity that enhances resistance to BRAF/MEK combination inhibitor therapy.

Methods

To compare the response of PTEN null to PTEN wild-type cells in an isogenic background, CRISPR/Cas9 was used to knock out PTEN in a melanoma cell line that harbors a BRAFV600E mutation. RNA sequencing, functional kinome analysis, and drug synergy screening were employed in the context of BRAF/MEK inhibition.

Results

RNA sequencing and functional kinome analysis revealed that the loss of PTEN led to an induction of FOXD3 and an increase in expression of the FOXD3 target gene, ERBB3/HER3. Inhibition of BRAF and MEK1/2 in PTEN null, BRAFV600E cells dramatically induced the expression of ERBB3/HER3 relative to wild-type cells. A synergy screen of epigenetic modifiers and kinase inhibitors in combination with BRAFi/MEKi revealed that the pan ERBB/HER inhibitor, neratinib, could reverse the resistance observed in PTEN null, BRAFV600E cells.

Conclusions

The findings indicate that PTEN null BRAFV600E melanoma exhibits increased reliance on ERBB/HER signaling when treated with clinically approved BRAFi/MEKi combinations. Future studies are warranted to test neratinib reversal of BRAFi/MEKi resistance in patient melanomas expressing ERBB3/HER3 in combination with its dimerization partner ERBB2/HER2.

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.

Data-oriented protein kinase drug discovery

The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts.

Discovery and optimization of narrow spectrum inhibitors of Tousled like kinase 2 (TLK2) using quantitative structure activity relationships

The oxindole scaffold has been the center of several kinase drug discovery programs, some of which have led to approved medicines. A series of two oxindole matched pairs from the literature were identified where TLK2 was potently inhibited as an off-target kinase. The oxindole has long been considered a promiscuous kinase inhibitor template, but across these four specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different ranging from narrow to broad spectrum kinome coverage. We synthesized a large series of analogues, utilizing quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, kinome profiling, and small-molecule x-ray structural analysis to optimize TLK2 inhibition and kinome selectivity. This resulted in the identification of several narrow spectrum, sub-family selective, chemical tool compounds including 128 (UNC-CA2-103) that could enable elucidation of TLK2 biology.

Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors

Medicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study kinase biology. Because of polypharmacology, a large part of the human kinome currently lacks selective chemical probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates of cancer cell lines using Kinobeads. The resulting 500,000 compound-target interactions are available in ProteomicsDB and we exemplify how this molecular resource may be used. For instance, the data revealed several hundred reasonably selective compounds for 72 kinases. Cellular assays validated GSK986310C as a candidate SYK (spleen tyrosine kinase) probe and X-ray crystallography uncovered the structural basis for the observed selectivity of the CK2 inhibitor GW869516X. Compounds targeting PKN3 were discovered and phosphoproteomics identified substrates that indicate target engagement in cells. We anticipate that this molecular resource will aid research in drug discovery and chemical biology.

Prognostic and therapeutic potential of STAT3: Opportunities and challenges in targeting HPV-mediated cervical carcinogenesis

Cervical cancer (CaCx) ranks as the fourth most prevalent cancer among women globally. Persistent infection of high-risk human papillomaviruses (HR-HPVs) is major etiological factor associated with CaCx. Signal Transducer and Activator of Transcription 3 (STAT3), a prominent member of the STAT family, has emerged as independent oncogenic driver. It is a target of many oncogenic viruses including HPV. How STAT3 influences HPV viral gene expression or gets affected by HPV is an area of active investigation. A better understanding of host-virus interaction will provide a prognostic and therapeutic window for CaCx control and management. In this comprehensive review, we delve into carcinogenic role of STAT3 in development of HPV-induced CaCx. With an emphasis on fascinating interplay between STAT3 and HPV genome, the review explores the diverse array of opportunities and challenges associated with this field to harness the prognostic and therapeutic potential of STAT3 in CaCx.

Inhibiting Hippo pathway kinases releases WWC1 to promote AMPAR-dependent synaptic plasticity and long-term memory in mice

The localization, number, and function of postsynaptic AMPA-type glutamate receptors (AMPARs) are crucial for synaptic plasticity, a cellular correlate for learning and memory. The Hippo pathway member WWC1 is an important component of AMPAR-containing protein complexes. However, the availability of WWC1 is constrained by its interaction with the Hippo pathway kinases LATS1 and LATS2 (LATS1/2). Here, we explored the biochemical regulation of this interaction and found that it is pharmacologically targetable in vivo. In primary hippocampal neurons, phosphorylation of LATS1/2 by the upstream kinases MST1 and MST2 (MST1/2) enhanced the interaction between WWC1 and LATS1/2, which sequestered WWC1. Pharmacologically inhibiting MST1/2 in male mice and in human brain-derived organoids promoted the dissociation of WWC1 from LATS1/2, leading to an increase in WWC1 in AMPAR-containing complexes. MST1/2 inhibition enhanced synaptic transmission in mouse hippocampal brain slices and improved cognition in healthy male mice and in male mouse models of Alzheimer's disease and aging. Thus, compounds that disrupt the interaction between WWC1 and LATS1/2 might be explored for development as cognitive enhancers.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

A review on potential heterocycles for the treatment of glioblastoma targeting receptor tyrosine kinases

Glioblastoma, the most aggressive form of brain tumor, poses significant challenges in terms of treatment success and patient survival. Current treatment modalities for glioblastoma include radiation therapy, surgical intervention, and chemotherapy. Unfortunately, the median survival rate remains dishearteningly low at 12-15 months. One of the major obstacles in treating glioblastoma is the recurrence of tumors, making chemotherapy the primary approach for secondary glioma patients. However, the efficacy of drugs is hampered by the presence of the blood-brain barrier and multidrug resistance mechanisms. Consequently, considerable research efforts have been directed toward understanding the underlying signaling pathways involved in glioma and developing targeted drugs. To tackle glioma, numerous studies have examined kinase-downstream signaling pathways such as RAS-RAF-MEK-ERK-MPAK. By targeting specific signaling pathways, heterocyclic compounds have demonstrated efficacy in glioma therapeutics. Additionally, key kinases including phosphatidylinositol 3-kinase (PI3K), serine/threonine kinase, cytoplasmic tyrosine kinase (CTK), receptor tyrosine kinase (RTK) and lipid kinase (LK) have been considered for investigation. These pathways play crucial roles in drug effectiveness in glioma treatment. Heterocyclic compounds, encompassing pyrimidine, thiazole, quinazoline, imidazole, indole, acridone, triazine, and other derivatives, have shown promising results in targeting these pathways. As part of this review, we propose exploring novel structures with low toxicity and high potency for glioma treatment. The development of these compounds should strive to overcome multidrug resistance mechanisms and efficiently penetrate the blood-brain barrier. By optimizing the chemical properties and designing compounds with enhanced drug-like characteristics, we can maximize their therapeutic value and minimize adverse effects. Considering the complex nature of glioblastoma, these novel structures should be rigorously tested and evaluated for their efficacy and safety profiles.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.

A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.