The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

Assessing Darkness of the Human Kinome from a Medicinal Chemistry Perspective

In drug discovery, human protein kinases (PKs) represent one of the major target classes due to their central role in cellular signaling, implication in various diseases as a consequence of deregulated signaling, and notable druggability. Individual PKs and their disease biology have been explored to different degrees, giving rise to heterogeneous functional knowledge and disease associations across the human kinome. The U.S. National Institutes of Health previously designated 162 understudied ("dark") human PKs and lipid kinases due to the lack of functional annotations and high-quality molecular probes for functional investigations. Given the large volumes of available PK inhibitors (PKIs) and activity data, we have systematically analyzed the distribution of PKIs and associated data at different confidence levels across the human kinome and distinguished between chemically explored, underexplored, and unexplored PKs. The analysis provides a medicinal chemistry-centric view of PK exploration and further extends prior assessment of the dark kinome.

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.

A web portal for exploring kinase-substrate interactions

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

The expanding landscape of canonical and non-canonical protein phosphorylation

Protein phosphorylation is a crucial regulatory mechanism in cell signaling, acting as a molecular switch that modulates protein function. Catalyzed by protein kinases and reversed by phosphoprotein phosphatases, it is essential in both normal physiological and pathological states. Recent advances have uncovered a vast and intricate landscape of protein phosphorylation that include histidine phosphorylation and more unconventional events, such as pyrophosphorylation and polyphosphorylation. Many questions remain about the true size of the phosphoproteome and, more importantly, its site-specific functional relevance. The involvement of unconventional actors such as pseudokinases and pseudophosphatases adds further complexity to be resolved. This review explores recent discoveries and ongoing challenges, highlighting the need for continued research to fully elucidate the roles and regulation of protein phosphorylation.

Leveraging multiple data types for improved compound-kinase bioactivity prediction

Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning methodology for compound-kinase activity prediction that leverages both single-dose and dose-response data. We demonstrate that our two-stage approach yields accurate activity predictions and significantly improves model performance compared to training solely on dose-response labels. This superior performance is consistent across five diverse machine learning methods. Using the best performing model, we carried out extensive experimental profiling on a total of 347 selected compound-kinase pairs, achieving a high hit rate of 40% and a negative predictive value of 78%. We show that these rates can be improved further by incorporating model uncertainty estimates into the compound selection process. By integrating multiple activity data types, we demonstrate that our approach holds promise for facilitating the development of training activity datasets in a more efficient and cost-effective way.

FYN-mediated phosphorylation of BCKDK at Y151 promotes GBM proliferation by increasing the oncogenic metabolite N-acetyl-L-alanine

Branched chain α-keto acid dehydrogenase kinase (BCKDK) is a key enzyme involved in the metabolism of branched-chain amino acids (BCAAs). Its potential as a therapeutic target and prognostic factor for a variety of cancers has been widely reported. In this study, we investigated the expression of BCKDK in clinical glioma samples and found that BCKDK was significantly overexpressed in glioblastoma (GBM) and was associated with its poor prognosis. We further found that BCKDK is phosphorylated by tyrosine protein kinase Fyn at Y151, which increases its catalytic activity and stability, and demonstrate through in vivo and in vitro experiments that BCKDK phosphorylation promotes GBM cell proliferation. In addition, we found that the levels of the metabolite N-acetyl-L-alanine (NAAL) in GBM cells with high BCKDK were higher than those in the silencing group, and silencing or inhibition of BCKDK promotes the expression of ACY1, an enzyme that catalyzes the hydrolysis of NAAL into acetic acid and alanine. Exogenous addition of NAAL can activate the ERK signaling pathway and promote the proliferation of GBM cells. Taken together, we identified a novel mechanism of BCKDK activation and found NAAL is a novel oncogenic metabolite. Our study confirms the importance of the Fyn-BCKDK-ACY1-NAAL signalling axis in the development of GBM and suggests that p-BCKDK (Y151) and NAAL can serve as potential predictors of GBM progression and prognosis.

Bacterial Production of CDKL5 Catalytic Domain: Insights in Aggregation, Internal Translation and Phosphorylation Patterns

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase involved in human brain development and functioning. Mutations in CDKL5, especially in its catalytic domain, cause a severe developmental condition named CDKL5 deficiency disorder. Nevertheless, molecular studies investigating the structural consequences of such mutations are still missing. The CDKL5 catalytic domain harbors different sites of post-translational modification, such as phosphorylations, but their role in catalytic activity, protein folding, and stability has not been entirely investigated. With this work, we describe the expression pattern of the CDKL5 catalytic domain in Escherichia coli demonstrating that it predominantly aggregates. However, the use of solubility tags, the lowering of the expression temperature, the manual codon optimization to overcome an internal translational start, and the incubation of the protein with K+ and MgATP allow the collection of a soluble catalytically active kinase. Interestingly, the resulting protein exhibits hypophosphorylation compared to its eukaryotic counterpart, proving that bacteria are a useful tool to achieve almost unmodified CDKL5. Posing questions about the CDKL5 autoactivation mechanism and the determinants for its stability, this research provides a valuable platform for comparative biophysical studies between bacterial and eukaryotic-expressed proteins, contributing to our understanding of neurodevelopmental disorders associated with CDKL5 dysfunction.

Mechanisms of acquired resistance to HER2-Positive breast cancer therapies induced by HER3: A comprehensive review

Receptor tyrosine kinases (RTKs) are cell surface receptors with kinase activity that play a crucial role in diverse cellular processes. Among the RTK family members, Human epidermal growth factor receptor 2 (HER2) and HER3 are particularly relevant to breast cancer. The review delves into the complexities of receptor tyrosine kinase interactions, resistance mechanisms, and the potential of anti-HER3 drugs, offering valuable insights into the clinical implications and future directions in this field of study. It assesses the potential of anti-HER3 drugs, such as pertuzumab, in overcoming resistance observed in HER2-positive breast cancer therapies. The review also explores the resistance mechanisms associated with various drugs, including trastuzumab, lapatinib, and PI3K inhibitors, providing insights into the intricate molecular processes underlying resistance development. The review concludes by emphasizing the necessity for further clinical trials to assess the efficacy of HER3 inhibitors and the potential of developing safe and effective anti-HER3 treatments to improve treatment outcomes for patients with HER2-positive breast cancer.

Unlocking the potential: advancements and future horizons in ROR1-targeted cancer therapies

While receptor tyrosine kinase-like orphan receptor 1 (ROR1) is typically expressed at low levels or absent in normal tissues, its expression is notably elevated in various malignant tumors and conditions, including chronic lymphocytic leukemia (CLL), breast cancer, ovarian cancer, melanoma, and lung adenocarcinoma. This distinctive feature positions ROR1 as an attractive target for tumor-specific treatments. Currently, several targeted drugs directed at ROR1 are undergoing clinical development, including monoclonal antibodies, antibody-drug conjugates (ADCs), and chimeric antigen receptor T-cell therapy (CAR-T). Additionally, there are four small molecule inhibitors designed to bind to ROR1, presenting promising avenues for the development of PROTAC degraders targeting ROR1. This review offers updated insights into ROR1's structural and functional characteristics, embryonic development implications, cell survival signaling pathways, and evolutionary targeting strategies, all of which have the potential to advance the treatment of malignant tumors.

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.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

Splice variants of CK1α and CK1α-like: Comparative analysis of subcellular localization, kinase activity, and function in the Wnt signaling pathway

Members of the casein kinase 1 (CK1) family are important regulators of multiple signaling pathways. CK1α is a well-known negative regulator of the Wnt/β-catenin pathway, which promotes the degradation of β-catenin via its phosphorylation of Ser45. In contrast, the closest paralog of CK1α, CK1α-like, is a poorly characterized kinase of unknown function. In this study, we show that the deletion of CK1α, but not CK1α-like, resulted in a strong activation of the Wnt/β-catenin pathway. Wnt-3a treatment further enhanced the activation, which suggests there are at least two modes, a CK1α-dependent and Wnt-dependent, of β-catenin regulation. Rescue experiments showed that only two out of ten naturally occurring splice CK1α/α-like variants were able to rescue the augmented Wnt/β-catenin signaling caused by CK1α deficiency in cells. Importantly, the ability to phosphorylate β-catenin on Ser45 in the in vitro kinase assay was required but not sufficient for such rescue. Our compound CK1α and GSK3α/β KO models suggest that the additional nonredundant function of CK1α in the Wnt pathway beyond Ser45-β-catenin phosphorylation includes Axin phosphorylation. Finally, we established NanoBRET assays for the three most common CK1α splice variants as well as CK1α-like. Target engagement data revealed comparable potency of known CK1α inhibitors for all CK1α variants but not for CK1α-like. In summary, our work brings important novel insights into the biology of CK1α, including evidence for the lack of redundancy with other CK1 kinases in the negative regulation of the Wnt/β-catenin pathway at the level of β-catenin and Axin.

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.

The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications

Recent advances in machine learning (ML) have led to newer model architectures including transformers (large language models, LLMs) showing state of the art results in text generation and image analysis as well as few-shot learning (FSLC) models which offer predictive power with extremely small datasets. These new architectures may offer promise, yet the 'no-free lunch' theorem suggests that no single model algorithm can outperform at all possible tasks. Here, we explore the capabilities of classical (SVR), FSLC, and transformer models (MolBART) over a range of dataset tasks and show a 'goldilocks zone' for each model type, in which dataset size and feature distribution (i.e. dataset "diversity") determines the optimal algorithm strategy. When datasets are small ( < 50 molecules), FSLC tend to outperform both classical ML and transformers. When datasets are small-to-medium sized (50-240 molecules) and diverse, transformers outperform both classical models and few-shot learning. Finally, when datasets are of larger and of sufficient size, classical models then perform the best, suggesting that the optimal model to choose likely depends on the dataset available, its size and diversity. These findings may help to answer the perennial question of which ML algorithm is to be used when faced with a new dataset.

Reversible male contraception by targeted inhibition of serine/threonine kinase 33

Men or mice with homozygous serine/threonine kinase 33 (STK33) mutations are sterile owing to defective sperm morphology and motility. To chemically evaluate STK33 for male contraception with STK33-specific inhibitors, we screened our multibillion-compound collection of DNA-encoded chemical libraries, uncovered potent STK33-specific inhibitors, determined the STK33 kinase domain structure bound with a truncated hit CDD-2211, and generated an optimized hit CDD-2807 that demonstrates nanomolar cellular potency (half-maximal inhibitory concentration = 9.2 nanomolar) and favorable metabolic stability. In mice, CDD-2807 exhibited no toxicity, efficiently crossed the blood-testis barrier, did not accumulate in brain, and induced a reversible contraceptive effect that phenocopied genetic STK33 perturbations without altering testis size. Thus, STK33 is a chemically validated, nonhormonal contraceptive target, and CDD-2807 is an effective tool compound.

An emerging target for male contraception

An inhibitor of a nonhormonal target is identified using a DNA-encoded chemical library.

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.

Assessment of Kinome-Wide Activity Remodeling upon Picornavirus Infection

Picornaviridae represent a large family of single-stranded positive RNA viruses of which different members can infect both humans and animals. These include the enteroviruses (e.g., poliovirus, coxsackievirus, and rhinoviruses) as well as the cardioviruses (e.g., encephalomyocarditis virus). Picornaviruses have evolved to interact with, use, and/or evade cellular host systems to create the optimal environment for replication and spreading. It is known that viruses modify kinase activity during infection, but a proteome-wide overview of the (de)regulation of cellular kinases during picornavirus infection is lacking. To study the kinase activity landscape during picornavirus infection, we here applied dedicated targeted mass spectrometry-based assays covering ∼40% of the human kinome. Our data show that upon infection, kinases of the MAPK pathways become activated (e.g., ERK1/2, RSK1/2, JNK1/2/3, and p38), while kinases involved in regulating the cell cycle (e.g., CDK1/2, GWL, and DYRK3) become inactivated. Additionally, we observed the activation of CHK2, an important kinase involved in the DNA damage response. Using pharmacological kinase inhibitors, we demonstrate that several of these activated kinases are essential for the replication of encephalomyocarditis virus. Altogether, the data provide a quantitative understanding of the regulation of kinome activity induced by picornavirus infection, providing a resource important for developing novel antiviral therapeutic interventions.

Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors

The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.

Illuminating function of the understudied druggable kinome

The human kinome, with more than 500 proteins, is crucial for cell signaling and disease. Yet, about one-third of kinases lack in-depth study. The Data and Resource Generating Center for Understudied Kinases has developed multiple resources to address this challenge including creation of a heavy amino acid peptide library for parallel reaction monitoring and quantitation of protein kinase expression, use of understudied kinases tagged with a miniTurbo-biotin ligase to determine interaction networks by proximity-dependent protein biotinylation, NanoBRET probe development for screening chemical tool target specificity in live cells, characterization of small molecule chemical tools inhibiting understudied kinases, and computational tools for defining kinome architecture. These resources are available through the Dark Kinase Knowledgebase, supporting further research into these understudied protein kinases.

Illuminating the druggable genome: Pathways to progress

There are ∼4500 genes within the 'druggable genome', the subset of the human genome that expresses proteins able to bind drug-like molecules, yet existing drugs only target a few hundred. A substantial subset of druggable proteins are largely uncharacterized or understudied, with many falling within G protein-coupled receptor (GPCR), ion channel, and kinase protein families. To improve scientific understanding of these three understudied protein families, the US National Institutes of Health launched the Illuminating the Druggable Genome Program. Now, as the program draws to a close, this review will lay out resources developed by the program that are intended to equip the scientific community with the tools necessary to explore previously understudied biology with the potential to rapidly impact human health.

Informatic challenges and advances in illuminating the druggable proteome

The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

High-throughput chemogenetic drug screening reveals PKC-RhoA/PKN as a targetable signaling vulnerability in GNAQ-driven uveal melanoma

Uveal melanoma (UM) is the most prevalent cancer of the eye in adults, driven by activating mutation of GNAQ/GNA11; however, there are limited therapies against UM and metastatic UM (mUM). Here, we perform a high-throughput chemogenetic drug screen in GNAQ-mutant UM contrasted with BRAF-mutant cutaneous melanoma, defining the druggable landscape of these distinct melanoma subtypes. Across all compounds, darovasertib demonstrates the highest preferential activity against UM. Our investigation reveals that darovasertib potently inhibits PKC as well as PKN/PRK, an AGC kinase family that is part of the "dark kinome." We find that downstream of the Gαq-RhoA signaling axis, PKN converges with ROCK to control FAK, a mediator of non-canonical Gαq-driven signaling. Strikingly, darovasertib synergizes with FAK inhibitors to halt UM growth and promote cytotoxic cell death in vitro and in preclinical metastatic mouse models, thus exposing a signaling vulnerability that can be exploited as a multimodal precision therapy against mUM.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

How Ligands Interact with the Kinase Hinge

ATP-competitive kinase inhibitors form hydrogen bond interactions with the kinase hinge region at the adenine binding site. Thus, it is crucial to explore hinge-ligand recognition as part of a rational drug design strategy. Here, harnessing known ligand-bound kinase structures and experimental assay resources, we first created a kinase structure-assay database (KSAD) containing 2705 nM ligand-bound kinase complexes. Then, using KSAD, we systematically investigate hinge-ligand binding patterns using interaction fingerprints, thereby delineating 15 different hydrogen-bond interaction modes. We believe these results will be valuable for de novo drug design and/or scaffold hopping of kinase-targeted drugs.

The Cyclin-dependent kinase 1: more than a cell cycle regulator

The Cyclin-dependent kinase 1, as a serine/threonine protein kinase, is more than a cell cycle regulator as it was originally identified. During the last decade, it has been shown to carry out versatile functions during the last decade. From cell cycle control to gene expression regulation and apoptosis, CDK1 is intimately involved in many cellular events that are vital for cell survival. Here, we provide a comprehensive catalogue of the CDK1 upstream regulators and substrates, describing how this kinase is implicated in the control of key 'cell cycle-unrelated' biological processes. Finally, we describe how deregulation of CDK1 expression and activation has been closely associated with cancer progression and drug resistance.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The doublecortin-like kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations, and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other calcium calmodulin kinases (CAMKs), and a 'Swiss Army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for autoregulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically divergent DCLK1 modulators, stabilizers, or degraders.

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The Doublecortin Like Kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other Calcium Calmodulin Kinases (CAMKs), and a 'Swiss-army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for auto-regulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor-binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically-divergent DCLK1 modulators, stabilizers or degraders.

Riok1, A Novel Potential Target in MSI-High p53 Mutant Colorectal Cancer Cells

The vulnerabilities of cancer cells constitute a promising strategy for drug therapeutics. This paper integrates proteomics, bioinformatics, and cell genotype together with in vitro cell proliferation assays to identify key biological processes and potential novel kinases that could account, at least in part, for the clinical differences observed in colorectal cancer (CRC) patients. This study started by focusing on CRC cell lines stratified by their microsatellite (MS) state and p53 genotype. It shows that cell-cycle checkpoint, metabolism of proteins and RNA, signal transduction, and WNT signaling processes are significantly more active in MSI-High p53-WT cell lines. Conversely, MSI-High cell lines with a mutant (Mut) p53 gene showed hyperactivation of cell signaling, DNA repair, and immune-system processes. Several kinases were linked to these phenotypes, from which RIOK1 was selected for additional exploration. We also included the KRAS genotype in our analysis. Our results showed that RIOK1's inhibition in CRC MSI-High cell lines was dependent on both the p53 and KRAS genotypes. Explicitly, Nintedanib showed relatively low cytotoxicity in MSI-High with both mutant p53 and KRAS (HCT-15) but no inhibition in p53 and KRAS WT (SW48) MSI-High cells. This trend was flipped in CRC MSI-High bearing opposite p53-KRAS genotypes (e.g., p53-Mut KRAS-WT or p53-WT KRAS-Mut), where observed cytotoxicity was more extensive compared to the p53-KRAS WT-WT or Mut-Mut cells, with HCT 116 (KRAS-Mut and p53-WT) being the most sensitive to RIOK1 inhibition. These results highlight the potential of our in silico computational approach to identify novel kinases in CRC sub-MSI-High populations as well as the importance of clinical genomics in determining drug potency.

Kinase Degraders, Activators, and Inhibitors: Highlights and Synthesis Routes to the Chemical Probes on opnMe.com, Part 1

Kinases are among the most important and successful drug targets. Chemical probe compounds have played a critical role in elucidating the role of kinases in many biological pathways. There are currently twelve well-validated chemical probes that target kinases available free-of-cost via the Molecules to Order (M2O) arm of Boehringer Ingelheim's open innovation platform, opnMe.com. Here we present a summary of the key data for each of these probe compounds and the synthesis routes to all twelve compounds. We hope this will aid researchers who use or plan to use these compounds in their research.

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

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

Discovery of a Potent and Selective CDKL5/GSK3 Chemical Probe That Is Neuroprotective

Despite mediating several essential processes in the brain, including during development, cyclin-dependent kinase-like 5 (CDKL5) remains a poorly characterized human protein kinase. Accordingly, its substrates, functions, and regulatory mechanisms have not been fully described. We realized that availability of a potent and selective small molecule probe targeting CDKL5 could enable illumination of its roles in normal development as well as in diseases where it has become aberrant due to mutation. We prepared analogs of AT-7519, a compound that has advanced to phase II clinical trials and is a known inhibitor of several cyclin-dependent kinases (CDKs) and cyclin-dependent kinase-like kinases (CDKLs). We identified analog 2 as a highly potent and cell-active chemical probe for CDKL5/GSK3 (glycogen synthase kinase 3). Evaluation of its kinome-wide selectivity confirmed that analog 2 demonstrates excellent selectivity and only retains GSK3α/β affinity. We next demonstrated the inhibition of downstream CDKL5 and GSK3α/β signaling and solved a co-crystal structure of analog 2 bound to human CDKL5. A structurally similar analog (4) proved to lack CDKL5 affinity and maintain potent and selective inhibition of GSK3α/β, making it a suitable negative control. Finally, we used our chemical probe pair (2 and 4) to demonstrate that inhibition of CDKL5 and/or GSK3α/β promotes the survival of human motor neurons exposed to endoplasmic reticulum stress. We have demonstrated a neuroprotective phenotype elicited by our chemical probe pair and exemplified the utility of our compounds to characterize the role of CDKL5/GSK3 in neurons and beyond.

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors

Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine learning models have been generated to aid in simplifying and improving the prediction of candidate compounds. Models tuned for predicting kinase inhibitors have been established. However, an effective model can be limited by the size of the chosen training dataset. In this study, we tested several machine learning models to predict potential kinase inhibitors. A dataset was curated from a number of publicly available repositories. This resulted in a comprehensive dataset covering more than half of the human kinome. More than 2,000 kinase models were established using different model approaches. The performances of the models were compared, and the Keras-MLP model was determined to be the best performing model. The model was then used to screen a chemical library for potential inhibitors targeting platelet-derived growth factor receptor-β (PDGFRB). Several PDGFRB candidates were selected, and in vitro assays confirmed four compounds with PDGFRB inhibitory activity and IC₅₀ values in the nanomolar range. These results show the effectiveness of machine learning models trained on the reported dataset. This report would aid in the establishment of machine learning models as well as in the discovery of novel kinase inhibitors.

Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles

There have been more than 70 FDA-approved drugs to target the ATP binding site of kinases, mainly in the field of oncology. These compounds are usually developed to target specific kinases, but in practice, most of these drugs are multi-kinase inhibitors that leverage the conserved nature of the ATP pocket across multiple kinases to increase their clinical efficacy. To utilize kinase inhibitors in targeted therapy and outside of oncology, a narrower kinome profile and an understanding of the toxicity profile is imperative. This is essential when considering treating chronic diseases with kinase targets, including neurodegeneration and inflammation. This will require the exploration of inhibitor chemical space and an in-depth understanding of off-target interactions. We have developed an early pipeline toxicity screening platform that uses supervised machine learning (ML) to classify test compounds’ cell stress phenotypes relative to a training set of on-market and withdrawn drugs. Here, we apply it to better understand the toxophores of some literature kinase inhibitor scaffolds, looking specifically at a series of 4-anilinoquinoline and 4-anilinoquinazoline model libraries.

Discovery of Potent and Exquisitely Selective Inhibitors of Kinase CK1 with Tunable Isoform Selectivity

Casein kinases 1 (CK1) are key signaling molecules that have emerged recently as attractive therapeutic targets in particular for the treatment of hematological malignancies. Herein, we report the identification of a new class of potent and highly selective inhibitors of CK1α, δ and ϵ. Based on their optimal in vitro and in vivo profiles and their exclusive selectivity, MU1250, MU1500 and MU1742 were selected as quality chemical probes for those CK1 isoforms. At proper concentrations, MU1250 and MU1500 allow for specific targeting of CK1δ or dual inhibition of CK1δ/ϵ in cells. The compound MU1742 also efficiently inhibits CK1α and, to our knowledge, represents the first potent and highly selective inhibitor of this enzyme. In addition, we demonstrate that the central 1H-pyrrolo[2,3-b]pyridine-imidazole pharmacophore can be used as the basis of highly selective inhibitors of other therapeutically relevant protein kinases, e.g. p38α, as exemplified by the compound MU1299.

A Potent and Selective CDKL5/GSK3 Chemical Probe is Neuroprotective

Despite mediating several essential processes in the brain, including during development, cyclin-dependent kinase-like 5 (CDKL5) remains a poorly characterized human protein kinase. Accordingly, its substrates, functions, and regulatory mechanisms have not been fully described. We realized that availability of a potent and selective small molecule probe targeting CDKL5 could enable illumination of its roles in normal development as well as in diseases where it has become aberrant due to mutation. We prepared analogs of AT-7519, a known inhibitor of several cyclin dependent and cyclin-dependent kinase-like kinases that has been advanced into Phase II clinical trials. We identified analog 2 as a highly potent and cell-active chemical probe for CDKL5/GSK3 (glycogen synthase kinase 3). Evaluation of its kinome-wide selectivity confirmed that analog 2 demonstrates excellent selectivity and only retains GSK3α/β affinity. As confirmation that our chemical probe is a high-quality tool to use in directed biological studies, we demonstrated inhibition of downstream CDKL5 and GSK3α/β signaling and solved a co-crystal structure of analog 2 bound to CDKL5. A structurally similar analog ( 4 ) proved to lack CDKL5 affinity and maintain potent and selective inhibition of GSK3α/β. Finally, we used our chemical probe pair ( 2 and 4 ) to demonstrate that inhibition of CDKL5 and/or GSK3α/β promotes the survival of human motor neurons exposed to endoplasmic reticulum (ER) stress. We have demonstrated a neuroprotective phenotype elicited by our chemical probe pair and exemplified the utility of our compounds to characterize the role of CDKL5/GSK3 in neurons and beyond.

Evolutionary and cellular analysis of the 'dark' pseudokinase PSKH2

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

An atlas of substrate specificities for the human serine/threonine kinome

Protein phosphorylation is one of the most widespread post-translational modifications in biology1,2. With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes3,4. For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible3. Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

Phosphoproteomics data-driven signalling network inference: Does it work?

The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.

The Power of Biocatalysts for Highly Selective and Efficient Phosphorylation Reactions

Reactions involving the transfer of phosphorus-containing groups are of key importance for maintaining life, from biological cells, tissues and organs to plants, animals, humans, ecosystems and the whole planet earth. The sustainable utilization of the nonrenewable element phosphorus is of key importance for a balanced phosphorus cycle. Significant advances have been achieved in highly selective and efficient biocatalytic phosphorylation reactions, fundamental and applied aspects of phosphorylation biocatalysts, novel phosphorylation biocatalysts, discovery methodologies and tools, analytical and synthetic applications, useful phosphoryl donors and systems for their regeneration, reaction engineering, product recovery and purification. Biocatalytic phosphorylation reactions with complete conversion therefore provide an excellent reaction platform for valuable analytical and synthetic applications.

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Evaluation of a Selective Chemical Probe Validates That CK2 Mediates Neuroinflammation in a Human Induced Pluripotent Stem Cell-Derived Microglial Model

Novel treatments for neurodegenerative disorders are in high demand. It is imperative that new protein targets be identified to address this need. Characterization and validation of nascent targets can be accomplished very effectively using highly specific and potent chemical probes. Human induced pluripotent stem cells (hiPSCs) provide a relevant platform for testing new compounds in disease relevant cell types. However, many recent studies utilizing this platform have focused on neuronal cells. In this study, we used hiPSC-derived microglia-like cells (MGLs) to perform side-by-side testing of a selective chemical probe, SGC-CK2-1, compared with an advanced clinical candidate, CX-4945, both targeting casein kinase 2 (CK2), one of the first kinases shown to be dysregulated in Alzheimer's disease (AD). CK2 can mediate neuroinflammation in AD, however, its role in microglia, the innate immune cells of the central nervous system (CNS), has not been defined. We analyzed available RNA-seq data to determine the microglial expression of kinases inhibited by SGC-CK2-1 and CX-4945 with a reported role in mediating inflammation in glial cells. As proof-of-concept for using hiPSC-MGLs as a potential screening platform, we used both wild-type (WT) MGLs and MGLs harboring a mutation in presenilin-1 (PSEN1), which is causative for early-onset, familial AD (FAD). We stimulated these MGLs with pro-inflammatory lipopolysaccharides (LPS) derived from E. coli and observed strong inhibition of the expression and secretion of proinflammatory cytokines by simultaneous treatment with SGC-CK2-1. A direct comparison shows that SGC-CK2-1 was more effective at suppression of proinflammatory cytokines than CX-4945. Together, these results validate a selective chemical probe, SGC-CK2-1, in human microglia as a tool to reduce neuroinflammation.

Protein proximity networks and functional evaluation of the casein kinase 1 gamma family reveal unique roles for CK1γ3 in WNT signaling

Aberrant activation or suppression of WNT/β-catenin signaling contributes to cancer initiation and progression, neurodegeneration, and bone disease. However, despite great need and more than 40 years of research, targeted therapies for the WNT pathway have yet to be fully realized. Kinases are considered exceptionally druggable and occupy key nodes within the WNT signaling network, but several pathway-relevant kinases remain understudied and "dark." Here, we studied the function of the casein kinase 1γ (CSNK1γ) subfamily of human kinases and their roles in WNT signaling. miniTurbo-based proximity biotinylation and mass spectrometry analysis of CSNK1γ1, CSNK1γ2, and CSNK1γ3 revealed numerous components of the β-catenin-dependent and β-catenin-independent WNT pathways. In gain-of-function experiments, we found that CSNK1γ3 but not CSNK1γ1 or CSNK1γ2 activated β-catenin-dependent WNT signaling, with minimal effect on other signaling pathways. We also show that within the family, CSNK1γ3 expression uniquely induced low-density lipoprotein receptor-related protein 6 phosphorylation, which mediates downstream WNT signaling transduction. Conversely, siRNA-mediated silencing of CSNK1γ3 alone had no impact on WNT signaling, though cosilencing of all three family members decreased WNT pathway activity. Finally, we characterized two moderately selective and potent small-molecule inhibitors of the CSNK1γ family. We show that these inhibitors and a CSNK1γ3 kinase-dead mutant suppressed but did not eliminate WNT-driven low-density lipoprotein receptor-related protein 6 phosphorylation and β-catenin stabilization. Our data suggest that while CSNK1γ3 expression uniquely drives pathway activity, potential functional redundancy within the family necessitates loss of all three family members to suppress the WNT signaling pathway.

Illuminating the Dark: Highly Selective Inhibition of Serine/Threonine Kinase 17A with Pyrazolo[1,5-a]pyrimidine-Based Macrocycles

Serine/threonine kinase 17A (death-associated protein kinase-related apoptosis-inducing protein kinase 1─DRAK1) is a part of the death-associated protein kinase (DAPK) family and belongs to the so-called dark kinome. Thus, the current state of knowledge of the cellular function of DRAK1 and its involvement in pathophysiological processes is very limited. Recently, DRAK1 has been implicated in tumorigenesis of glioblastoma multiforme (GBM) and other cancers, but no selective inhibitors of DRAK1 are available yet. To this end, we optimized a pyrazolo[1,5-a]pyrimidine-based macrocyclic scaffold. Structure-guided optimization of this macrocyclic scaffold led to the development of CK156 (34), which displayed high in vitro potency (K_D = 21 nM) and selectivity in kinomewide screens. Crystal structures demonstrated that CK156 (34) acts as a type I inhibitor. However, contrary to studies using genetic knockdown of DRAK1, we have seen the inhibition of cell growth of glioma cells in 2D and 3D culture only at low micromolar concentrations.

Bioinformatic Analysis of the Perilymph Proteome to Generate a Human Protein Atlas

The high complexity of the cellular architecture of the human inner ear and the inaccessibility for tissue biopsy hampers cellular and molecular analysis of inner ear disease. Sampling and analysis of perilymph may present an opportunity for improved diagnostics and understanding of human inner ear pathology. Analysis of the perilymph proteome from patients undergoing cochlear implantation was carried out revealing a multitude of proteins and patterns of protein composition that may enable characterisation of patients into subgroups. Based on existing data and databases, single proteins that are not present in the blood circulation were related to cells within the cochlea to allow prediction of which cells contribute to the individual perilymph proteome of the patients. Based on the results, we propose a human atlas of the cochlea. Finally, druggable targets within the perilymph proteome were identified. Understanding and modulating the human perilymph proteome will enable novel avenues to improve diagnosis and treatment of inner ear diseases.

Analysis of human Tribbles 2 (TRIB2) pseudokinase

Human Tribbles 2 (TRIB2) is a cancer-associated pseudokinase with a broad human protein interactome, including the well-studied AKT, C/EBPα and MAPK modules. Several lines of evidence indicate that human TRIB2 promotes cell survival and drug-resistance in solid tumors and blood cancers and is therefore of interest as a potential therapeutic target, although its physiological functions remain relatively poorly understood. The unique TRIB2 pseudokinase domain lacks the canonical 'DFG' motif, and subsequently possesses very low affinity for ATP in both the presence and absence of metal ions. However, TRIB2 also contains a unique cysteine-rich αC-helix, which interacts with a conserved peptide motif in its own carboxyl-terminal tail. This regulatory flanking region drives regulated interactions with distinct E3 ubiquitin ligases that serve to control the stability and turnover of TRIB2 client proteins. TRIB2 is also a low-affinity target of several known small-molecule protein kinase inhibitors, which were originally identified using purified recombinant TRIB2 proteins and a thermal shift assay. In this chapter, we discuss laboratory-based procedures for purification, stabilization and analysis of human TRIB2, including screening procedures that can be used for the identification of both reversible and covalent small molecule ligands.