Progress towards a public chemogenomic set for protein kinases and a call for contributions

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.

Identification of kinase modulators as host-directed therapeutics against intracellular methicillin-resistant Staphylococcus aureus

The increasing prevalence of antimicrobial-resistant Staphylococcus aureus strains, especially methicillin-resistant S. aureus (MRSA), poses a threat to successful antibiotic treatment. Unsuccessful attempts to develop a vaccine and rising resistance to last-resort antibiotics urge the need for alternative treatments. Host-directed therapy (HDT) targeting critical intracellular stages of S. aureus emerges as a promising alternative, potentially acting synergistically with antibiotics and reducing the risk of de novo drug resistance. We assessed 201 ATP-competitive kinase inhibitors from Published Kinase Inhibitor Sets (PKIS1 and PKIS2) against intracellular MRSA. Seventeen hit compounds were identified, of which the two most effective and well-tolerated hit compounds (i.e., GW633459A and GW296115X) were selected for further analysis. The compounds did not affect planktonic bacterial cultures, while they were active in a range of human cell lines of cervical, skin, lung, breast and monocyte origin, confirming their host-directed mechanisms. GW633459A, structurally related to lapatinib, exhibited an HDT effect on intracellular MRSA independently of its known human epidermal growth factor receptor (EGFR)/(HER) kinase family targets. GW296115X activated adenosine monophosphate-activated protein kinase (AMPK), thereby enhancing bacterial degradation via autophagy. Finally, GW296115X not only reduced MRSA growth in human cells but also improved the survival rates of MRSA-infected zebrafish embryos, highlighting its potential as HDT.

Preclinical characterization of CPL304110 as a potent and selective inhibitor of fibroblast growth factor receptors 1, 2, and 3 for gastric, bladder, and squamous cell lung cancer

Fibroblast Growth Factor Receptors (FGFRs) are a family of receptor tyrosine kinases expressed on a plethora of cell membranes. They play crucial roles in both embryonic development and adult tissue functions. There is an increasing amount of evidence that FGFR-mediated oncogenesis is mainly related to gene amplification, activating mutations, or translocation in tumors of various histological types. Dysregulation of FGFRs has been implicated in a wide variety of neoplasms, such as bladder, gastric, and lung cancers. Given their functional significance, FGFRs emerge as promising targets for cancer therapy. Here, we introduce CPL304100, an innovative and highly potent FGFR1-3 kinase inhibitor demonstrating excellent in vitro biological activity. Comprehensive analyses encompassed kinase assays, cell line evaluations, PK/PD studies surface plasmon resonance studies, molecular docking, and in vivo testing in mouse xenografts. CPL304110 exhibited a distinctive binding profile to FGFR1/2/3 kinase domains, accompanied by a good safety profile and favorable ADMET parameters. Selective inhibition of tumor cell lines featuring active FGFR signaling was observed, distinguishing it from cell lines lacking FGFR aberrations (FGFR1, 2, and 3). CPL304110 demonstrated efficacy in both FGFR-dependent cell lines and patient-derived tumor xenograft (PDTX) in vivo models. Comparative analyses with FDA-approved FGFR inhibitors, erdafitinib and pemigatinib, revealed certain advantages of CPL304110 in both in vitro and in vivo assessments. Encouraging preclinical results led the way for the initiation of a Phase I clinical trial (01FGFR2018; NCT04149691) to further evaluate CPL304110 as a novel anticancer therapy.

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 a potent off-target kinase. The oxindole has long been considered a promiscuous inhibitor template, but across these 4 specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different from narrow to broad spectrum coverage. We synthesized a large series of analogues and through quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, small-molecule x-ray structural analysis and kinome profiling, narrow spectrum, sub-family selective, chemical tool compounds were identified to enable elucidation of TLK2 biology.

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

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.

Artificial intelligence methods in kinase target profiling: Advances and challenges

Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.

SGC-CLK-1: A chemical probe for the Cdc2-like kinases CLK1, CLK2, and CLK4

Small molecule modulators are important tools to study both basic biology and the complex signaling of protein kinases. The cdc2-like kinases (CLK) are a family of four kinases that have garnered recent interest for their involvement in a diverse set of diseases such as neurodegeneration, autoimmunity, and many cancers. Targeted medicinal chemistry around a CLK inhibitor hit identified through screening of a kinase inhibitor set against a large panel of kinases allowed us to identify a potent and selective inhibitor of CLK1, 2, and 4. Here, we present the synthesis, selectivity, and preliminary biological characterization of this compound - SGC-CLK-1 (CAF-170). We further show CLK2 has the highest binding affinity, and high CLK2 expression correlates with a lower IC50 in a screen of multiple cancer cell lines. Finally, we show that SGC-CLK-1 not only reduces serine arginine-rich (SR) protein phosphorylation but also alters SR protein and CLK2 subcellular localization in a reversible way. Therefore, we anticipate that this compound will be a valuable tool for increasing our understanding of CLKs and their targets, SR proteins, at the level of phosphorylation and subcellular localization.

Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery

In recent years machine learning has transformed many aspects of the drug discovery process including small molecule design for which the prediction of the bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches, but is fundamentally limited by the accuracy with which protein:ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase:inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures co-crystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the co-crystallized ligand-utilizing shape overlap with or without maximum common substructure matching-are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance to generate a low RMSD docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar co-crystallized ligands according to shape and electrostatics proofed to be the most efficient way to reproduce binding poses achieving a success rate of 66.9 % across all included systems. The studied docking and pose selection strategies-which utilize the OpenEye Toolkit-were implemented into pipelines of the KinoML framework allowing automated and reliable protein:ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe the general findings can also be transferred to other protein families.

High-throughput screening identifies cell cycle-associated signaling cascades that regulate a multienzyme glucosome assembly in human cells

We have previously demonstrated that human liver-type phosphofructokinase 1 (PFK1) recruits other rate-determining enzymes in glucose metabolism to organize multienzyme metabolic assemblies, termed glucosomes, in human cells. However, it has remained largely elusive how glucosomes are reversibly assembled and disassembled to functionally regulate glucose metabolism and thus contribute to human cell biology. We developed a high-content quantitative high-throughput screening (qHTS) assay to identify regulatory mechanisms that control PFK1-mediated glucosome assemblies from stably transfected HeLa Tet-On cells. Initial qHTS with a library of pharmacologically active compounds directed following efforts to kinase-inhibitor enriched collections. Consequently, three compounds that were known to inhibit cyclin-dependent kinase 2, ribosomal protein S6 kinase and Aurora kinase A, respectively, were identified and further validated under high-resolution fluorescence single-cell microscopy. Subsequent knockdown studies using small-hairpin RNAs further confirmed an active role of Aurora kinase A on the formation of PFK1 assemblies in HeLa cells. Importantly, all the identified protein kinases here have been investigated as key signaling nodes of one specific cascade that controls cell cycle progression in human cells. Collectively, our qHTS approaches unravel a cell cycle-associated signaling network that regulates the formation of PFK1-mediated glucosome assembly in human cells.

Inhibitors of Rho kinases (ROCK) induce multiple mitotic defects and synthetic lethality in BRCA2-deficient cells

The trapping of Poly-ADP-ribose polymerase (PARP) on DNA caused by PARP inhibitors (PARPi) triggers acute DNA replication stress and synthetic lethality (SL) in BRCA2-deficient cells. Hence, DNA damage is accepted as a prerequisite for SL in BRCA2-deficient cells. In contrast, here we show that inhibiting ROCK in BRCA2-deficient cells triggers SL independently from acute replication stress. Such SL is preceded by polyploidy and binucleation resulting from cytokinesis failure. Such initial mitosis abnormalities are followed by other M phase defects, including anaphase bridges and abnormal mitotic figures associated with multipolar spindles, supernumerary centrosomes and multinucleation. SL was also triggered by inhibiting Citron Rho-interacting kinase, another enzyme that, similarly to ROCK, regulates cytokinesis. Together, these observations demonstrate that cytokinesis failure triggers mitotic abnormalities and SL in BRCA2-deficient cells. Furthermore, the prevention of mitotic entry by depletion of Early mitotic inhibitor 1 (EMI1) augmented the survival of BRCA2-deficient cells treated with ROCK inhibitors, thus reinforcing the association between M phase and cell death in BRCA2-deficient cells. This novel SL differs from the one triggered by PARPi and uncovers mitosis as an Achilles heel of BRCA2-deficient cells.

Modulation of tau tubulin kinases (TTBK1 and TTBK2) impacts ciliogenesis

Tau tubulin kinase 1 and 2 (TTBK1/2) are highly homologous kinases that are expressed and mediate disease-relevant pathways predominantly in the brain. Distinct roles for TTBK1 and TTBK2 have been delineated. While efforts have been devoted to characterizing the impact of TTBK1 inhibition in diseases like Alzheimer's disease and amyotrophic lateral sclerosis, TTBK2 inhibition has been less explored. TTBK2 serves a critical function during cilia assembly. Given the biological importance of these kinases, we designed a targeted library from which we identified several chemical tools that engage TTBK1 and TTBK2 in cells and inhibit their downstream signaling. Indolyl pyrimidinamine 10 significantly reduced the expression of primary cilia on the surface of human induced pluripotent stem cells (iPSCs). Furthermore, analog 10 phenocopies TTBK2 knockout in iPSCs, confirming a role for TTBK2 in ciliogenesis.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Multiparameter phenotypic screening for endogenous TFEB and TFE3 translocation identifies novel chemical series modulating lysosome function

The accumulation of toxic protein aggregates in multiple neurodegenerative diseases is associated with defects in the macroautophagy/autophagy-lysosome pathway. The amelioration of disease phenotypes across multiple models of neurodegeneration can be achieved through modulating the master regulator of lysosome function, TFEB (transcription factor EB). Using a novel multi-parameter high-throughput screen for cytoplasmic:nuclear translocation of endogenous TFEB and the related transcription factor TFE3, we screened the Published Kinase Inhibitor Set 2 (PKIS2) library as proof of principle and to identify kinase regulators of TFEB and TFE3. Given that TFEB and TFE3 are responsive to cellular stress we have established assays for cellular toxicity and lysosomal function, critical to ensuring the identification of hit compounds with only positive effects on lysosome activity. In addition to AKT inhibitors which regulate TFEB localization, we identified a series of quinazoline-derivative compounds that induced TFEB and TFE3 translocation. A novel series of structurally-related analogs was developed, and several compounds induced TFEB and TFE3 translocation at higher potency than previously screened compounds. KINOMEscan and cell-based KiNativ kinase profiling revealed high binding for the PRKD (protein kinase D) family of kinases, suggesting good selectivity for these compounds. We describe and utilize a cellular target-validation platform using CRISPRi knockdown and orthogonal PRKD inhibitors to demonstrate that the activity of these compounds is independent of PRKD inhibition. The more potent analogs induced subsequent upregulation of the CLEAR gene network and cleared pathological HTT protein in a cellular model of proteinopathy, demonstrating their potential to alleviate neurodegeneration-relevant phenotypes. Abbreviations: AD: Alzheimer disease; AK: adenylate kinase; CLEAR: coordinated lysosomal expression and regulation; CQ: chloroquine; HD: Huntington disease; PD: Parkinson disease; PKIS2: Published Kinase Inhibitor Set 2; PRKD: protein kinase D; TFEB: transcription factor EB.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

Developing a Kinase Chemogenomic Set: Facilitating Investigation into Kinase Biology by Linking Phenotypes to Targets

Advances in increasingly complex phenotypic screening with lower throughput have necessitated the screening of smaller more highly annotated sets. One such collection of compounds which has been recently assembled is the kinase chemogenomic set. This is a set of curated kinase inhibitors built upon previous iterations, PKIS and PKIS2, and donations from our partners. Each compound in the set has been carefully selected based on selectivity, potency, and kinome coverage. These compounds as a set have been made available to the scientific community, enabling phenotypic screens to identify kinases that drive novel biology. Additionally, the associated data deposited in the public domain have also been used to inform new inhibitor design. Further expansion of this set to complete kinome coverage will allow for a greater understanding of kinase biology and its role in disease.

Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).

From targeted therapy to a novel way: Immunogenic cell death in lung cancer

Lung cancer (LC) is one of the most incident malignancies and a leading cause of cancer mortality worldwide. Common tumorigenic drivers of LC mainly include genetic alterations of EGFR, ALK, KRAS, BRAF, ROS1, and MET. Small inhibitory molecules and antibodies selectively targeting these alterations or/and their downstream signaling pathways have been approved for treatment of LC. Unfortunately, following initial positive responses to these targeted therapies, a large number of patients show dismal prognosis due to the occurrence of resistance mechanisms, such as novel mutations of these genes and activation of alternative signaling pathways. Over the past decade, it has become clear that there is no possible cure for LC unless potent antitumor immune responses are induced by therapeutic intervention. Immunogenic cell death (ICD) is a newly emerged concept, a form of regulated cell death that is sufficient to activate adaptive immune responses against tumor cells. It transforms dying cancer cells into a therapeutic vaccine and stimulates long-lasting protective antitumor immunity. In this review, we discuss the key targetable genetic aberrations and the underlying mechanism of ICD in LC. Various agents inducing ICD are summarized and the possibility of harnessing ICD in LC immunotherapy is further explored.

Identification of Inhibitors of the Schistosoma mansoni VKR2 Kinase Domain

Schistosomiasis is a neglected tropical disease caused by parasitic flatworms. Current treatment relies on just one partially effective drug, praziquantel (PZQ). Schistosoma mansoni Venus Kinase Receptors 1 and 2 (SmVKR1 and SmVKR2) are important for parasite growth and egg production, and are potential targets for combating schistosomiasis. VKRs consist of an extracellular Venus Flytrap Module (VFTM) linked via a transmembrane helix to a kinase domain. Here, we initiated a drug discovery effort to inhibit the activity of the SmVKR2 kinase domain (SmVKR2_KD) by screening the GSK published kinase inhibitor set 2 (PKIS2). We identified several inhibitors, of which four were able to inhibit its enzymatic activity and induced phenotypic changes in ex vivo S. mansoni. Our crystal structure of the SmVKR2_KD displays an active-like state that sheds light on the activation process of VKRs. Our data provide a basis for the further exploration of SmVKR2 as a possible drug target.

An interpretable machine learning model for selectivity of small molecules against homologous protein family

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

ERK5 Signalling and Resistance to ERK1/2 Pathway Therapeutics: The Path Less Travelled?

The RAS-regulated RAF-MEK1/2-ERK1/2 signalling pathway is frequently de-regulated in human cancer. Melanoma in particular exhibits a high incidence of activating BRAF^V600E/K and NRAS^Q61L/K mutations and such cells are addicted to the activity of these mutant oncoproteins. As a result three different BRAF inhibitors (BRAFi) have now been approved for BRAFV600E/K- mutant melanoma and have transformed the treatment of this disease. Despite this, clinical responses are typically transient as tumour cells develop resistance. These resistance mechanisms frequently involve reinstatement of ERK1/2 signalling and BRAFi are now deployed in combination with one of three approved MEK1/2 inhibitors (MEKi) to provide more durable, but still transient, clinical responses. Furthermore, inhibitors to ERK1/2 (ERK1/2i) have also been developed to counteract ERK1/2 signalling. However, recent studies have suggested that BRAFi/MEKi and ERK1/2i resistance can arise through activation of a parallel signalling pathway leading to activation of ERK5, an unusual protein kinase that contains both a kinase domain and a transcriptional transactivation domain. Here we review the evidence supporting ERK5 as a mediator of BRAFi/MEKi and ERK1/2i resistance. We also review the challenges in targeting ERK5 signalling with small molecules, including paradoxical activation of the transcriptional transactivation domain, and discuss new therapeutic modalities that could be employed to target ERK5.

AD Informer Set: Chemical tools to facilitate Alzheimer's disease drug discovery

Introduction

The portfolio of novel targets to treat Alzheimer's disease (AD) has been enriched by the Accelerating Medicines Partnership Program for Alzheimer's Disease (AMP AD) program.

Methods

Publicly available resources, such as literature and databases, enabled a data-driven effort to identify existing small molecule modulators for many protein products expressed by the genes nominated by AMP AD and suitable positive control compounds to be included in the set. Compounds contained within the set were manually selected and annotated with associated published, predicted, and/or experimental data.

Results

We built an annotated set of 171 small molecule modulators targeting 98 unique proteins that have been nominated by AMP AD consortium members as novel targets for the treatment of AD. The majority of compounds included in the set are inhibitors. These small molecules vary in their quality and should be considered chemical tools that can be used in efforts to validate therapeutic hypotheses, but which will require further optimization. A physical copy of the AD Informer Set can be requested on the Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) website.

Discussion

Small molecules that enable target validation are important tools for the translation of novel hypotheses into viable therapeutic strategies for AD.

Identification of 4-anilino-quin(az)oline as a cell active Protein Kinase Novel 3 (PKN3) inhibitor chemotype

Deep annotation of a library of 4-anilinoquinolines led to the identification of 7-iodo- N -(3,4,5-trimethoxyphenyl)quinolin-4-amine 16 as a potent inhibitor (IC 50 = 14 nM) of Protein Kinase Novel 3 (PKN3) with micromolar activity in cells. Compound 16 is a potential tool compound to study the cell biology of PKN3 and its role in pancreatic and prostate cancer and T-cell acute lymphoblastic leukemia. These 4-anilinoquinolines may also be useful tools to uncover the therapeutic potential of PKN3 inhibition in a broad range of diseases.

Target 2035 - update on the quest for a probe for every protein

Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. The challenge of translating the wealth of new knowledge from genomics into new medicines is that proteins, and not genes, are the primary executers of biological function. Therefore, much of how biology works in health and disease must be understood through the lens of protein function. Accordingly, a subset of human proteins has been at the heart of research interests of scientists over the centuries, and we have accumulated varying degrees of knowledge about approximately 65% of the human proteome. Nevertheless, a large proportion of proteins in the human proteome (∼35%) remains uncharacterized, and less than 5% of the human proteome has been successfully targeted for drug discovery. This highlights the profound disconnect between our abilities to obtain genetic information and subsequent development of effective medicines. Target 2035 is an international federation of biomedical scientists from the public and private sectors, which aims to address this gap by developing and applying new technologies to create by year 2035 chemogenomic libraries, chemical probes, and/or biological probes for the entire human proteome.

Use of AD Informer Set compounds to explore validity of novel targets in Alzheimer's disease pathology

Introduction

A chemogenomic set of small molecules with annotated activities and implicated roles in Alzheimer's disease (AD) called the AD Informer Set was recently developed and made available to the AD research community: https://treatad.org/data‐tools/ad‐informer‐set/.

Methods

Small subsets of AD Informer Set compounds were selected for AD‐relevant profiling. Nine compounds targeting proteins expressed by six AD‐implicated genes prioritized for study by Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT‐AD) teams were selected for G‐protein coupled receptor (GPCR), amyloid beta (Aβ) and tau, and pharmacokinetic (PK) studies. Four non‐overlapping compounds were analyzed in microglial cytotoxicity and phagocytosis assays.

Results

The nine compounds targeting CAPN2, EPHX2, MDK, MerTK/FLT3, or SYK proteins were profiled in 46 to 47 primary GPCR binding assays. Human induced pluripotent stem cell (iPSC)‐derived neurons were treated with the same nine compounds and secretion of Aβ peptides (Aβ40 and Aβ42) as well as levels of phosphophorylated tau (p‐tau, Thr231) and total tau (t‐tau) peptides measured at two concentrations and two timepoints. Finally, CD1 mice were dosed intravenously to determine preliminary PK and/or brain‐specific penetrance values for these compounds. As a final cell‐based study, a non‐overlapping subset of four compounds was selected based on single‐concentration screening for analysis of both cytotoxicity and phagocytosis in murine and human microglia cells.

Discussion

We have demonstrated the utility of the AD Informer Set in the validation of novel AD hypotheses using biochemical, cellular (primary and immortalized), and in vivo studies. The selectivity for their primary targets versus essential GPCRs in the brain was established for our compounds. Statistical changes in tau, p‐tau, Aβ40, and/or Aβ42 and blood–brain barrier penetrance were observed, solidifying the utility of specific compounds for AD. Single‐concentration phagocytosis results were validated as predictive of dose–response findings. These studies established workflows, validated assays, and illuminated next steps for protein targets and compounds.

PRM-LIVE with Trapped Ion Mobility Spectrometry and Its Application in Selectivity Profiling of Kinase Inhibitors

Parallel reaction monitoring (PRM) has emerged as a popular approach for targeted protein quantification. With high ion utilization efficiency and first-in-class acquisition speed, the timsTOF Pro provides a powerful platform for PRM analysis. However, sporadic chromatographic drift in peptide retention time represents a fundamental limitation for the reproducible multiplexing of targets across PRM acquisitions. Here, we present PRM-LIVE, an extensible, Python-based acquisition engine for the timsTOF Pro, which dynamically adjusts detection windows for reproducible target scheduling. In this initial implementation, we used iRT peptides as retention time standards and demonstrated reproducible detection and quantification of 1857 tryptic peptides from the cell lysate in a 60 min PRM-LIVE acquisition. As an application in functional proteomics, we use PRM-LIVE in an activity-based protein profiling platform to assess binding selectivity of small-molecule inhibitors against 220 endogenous human kinases.

Design and Development of a Chemical Probe for Pseudokinase Ca2+/calmodulin-Dependent Ser/Thr Kinase

CASK (Ca²⁺/calmodulin-dependent Ser/Thr kinase) is a member of the MAGUK (membrane-associated guanylate kinase) family that functions as neurexin kinases with roles implicated in neuronal synapses and trafficking. The lack of a canonical DFG motif, which is altered to GFG in CASK, led to the classification as a pseudokinase. However, functional studies revealed that CASK can still phosphorylate substrates in the absence of divalent metals. CASK dysfunction has been linked to many diseases, including colorectal cancer, Parkinson's disease, and X-linked mental retardation, suggesting CASK as a potential drug target. Here, we exploited structure-based design for the development of highly potent and selective CASK inhibitors based on 2,4-diaminopyrimidine-5-carboxamides targeting an unusual pocket created by the GFG motif. The presented inhibitor design offers a more general strategy for the development of pseudokinase ligands that harbor unusual sequence motifs. It also provides a first chemical probe for studying the biological roles of CASK.

A cell-based drug discovery assay identifies inhibition of cell stress responses as a new approach to treatment of epidermolysis bullosa simplex

In the skin fragility disorder epidermolysis bullosa simplex (EBS), mutations in keratin 14 (K14, also known as KRT14) or keratin 5 (K5, also known as KRT5) lead to keratinocyte rupture and skin blistering. Severe forms of EBS are associated with cytoplasmic protein aggregates, with elevated kinase activation of ERK1 and ERK2 (ERK1/2; also known as MAPK3 and MAPK1, respectively), suggesting intrinsic stress caused by misfolded keratin protein. Human keratinocyte EBS reporter cells stably expressing GFP-tagged EBS-mimetic mutant K14 were used to optimize a semi-automated system to quantify the effects of test compounds on keratin aggregates. Screening of a protein kinase inhibitor library identified several candidates that reduced aggregates and impacted on epidermal growth factor receptor (EGFR) signalling. EGF ligand exposure induced keratin aggregates in EBS reporter keratinocytes, which was reversible by EGFR inhibition. EBS keratinocytes treated with a known EGFR inhibitor, afatinib, were driven out of activation and towards quiescence with minimal cell death. Aggregate reduction was accompanied by denser keratin filament networks with enhanced intercellular cohesion and resilience, which when extrapolated to a whole tissue context would predict reduced epidermal fragility in EBS patients. This assay system provides a powerful tool for discovery and development of new pathway intervention therapeutic avenues for EBS.

Synthesis and Evaluation of Novel 1,2,6-Thiadiazinone Kinase Inhibitors as Potent Inhibitors of Solid Tumors

A focused series of substituted 4H-1,2,6-thiadiazin-4-ones was designed and synthesized to probe the anti-cancer properties of this scaffold. Insights from previous kinase inhibitor programs were used to carefully select several different substitution patterns. Compounds were tested on bladder, prostate, pancreatic, breast, chordoma, and lung cancer cell lines with an additional skin fibroblast cell line as a toxicity control. This resulted in the identification of several low single digit micro molar compounds with promising therapeutic windows, particularly for bladder and prostate cancer. A number of key structural features of the 4H-1,2,6-thiadiazin-4-one scaffold are discussed that show promising scope for future improvement.

Discovery of a Potent Dual SLK/STK10 Inhibitor Based on a Maleimide Scaffold

SLK (STE20-like kinase) and STK10 (serine/threonine kinase 10) are closely related kinases whose enzymatic activity is linked to the regulation of ezrin, radixin, and moesin function and to the regulation of lymphocyte migration and the cell cycle. We identified a series of 3-anilino-4-arylmaleimides as dual inhibitors of SLK and STK10 with good kinome-wide selectivity. Optimization of this series led to multiple SLK/STK10 inhibitors with nanomolar potency. Crystal structures of exemplar inhibitors bound to SLK and STK10 demonstrated the binding mode of the inhibitors and rationalized their selectivity. Cellular target engagement assays demonstrated the binding of the inhibitors to SLK and STK10 in cells. Further selectivity analyses, including analysis of activity of the reported inhibitors against off-targets in cells, identified compound 31 as the most potent and selective inhibitor of SLK and STK10 yet reported.

Chemical Probes for Understudied Kinases: Challenges and Opportunities

Over 20 years after the approval of the first-in-class protein kinase inhibitor imatinib, the biological function of a significant fraction of the human kinome remains poorly understood while most research continues to be focused on few well-validated targets. Given the strong genetic evidence for involvement of many kinases in health and disease, the understudied fraction of the kinome holds a large and unexplored potential for future therapies. Specific chemical probes are indispensable tools to interrogate biology enabling proper preclinical validation of novel kinase targets. In this Perspective, we highlight recent case studies illustrating the development of high-quality chemical probes for less-studied kinases and their application in target validation. We spotlight emerging techniques and approaches employed in the generation of chemical probes for protein kinases and beyond and discuss the associated challenges and opportunities.

Identification of Pyrimidine-Based Lead Compounds for Understudied Kinases Implicated in Driving Neurodegeneration

The pyrimidine core has been utilized extensively to construct kinase inhibitors, including eight FDA-approved drugs. Because the pyrimidine hinge-binding motif is accommodated by many human kinases, kinome-wide selectivity of resultant molecules can be poor. This liability was seen as an advantage since it is well tolerated by many understudied kinases. We hypothesized that nonexemplified aminopyrimidines bearing side chains from well-annotated pyrimidine-based inhibitors with off-target activity on understudied kinases would provide us with useful inhibitors of these lesser studied kinases. Our strategy paired mixing and matching the side chains from the 2- and 4-positions of the parent compounds with modifications at the 5-position of the pyrimidine core, which is situated near the gatekeeper residue of the binding pocket. Utilizing this approach, we imparted improved kinome-wide selectivity to most members of the resultant library. Importantly, we also identified potent biochemical and cell-active lead compounds for understudied kinases like DRAK1, BMP2K, and MARK3/4.

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Reducing False Positives through the Application of Fluorescence Lifetime Technology: A Comparative Study Using TYK2 Kinase as a Model System

The predominant assay detection methodologies used for enzyme inhibitor identification during early-stage drug discovery are fluorescence-based. Each fluorophore has a characteristic fluorescence decay, known as the fluorescence lifetime, that occurs throughout a nanosecond-to-millisecond timescale. The measurement of fluorescence lifetime as a reporter for biological activity is less common than fluorescence intensity, even though the latter has numerous issues that can lead to false-positive readouts. The confirmation of hit compounds as true inhibitors requires additional assays, cost, and time to progress from hit identification to lead drug-candidate optimization. To explore whether the use of fluorescence lifetime technology (FLT) can offer comparable benefits to label-free-based approaches such as RapidFire mass spectroscopy (RF-MS) and a superior readout compared to time-resolved fluorescence resonance energy transfer (TR-FRET), three equivalent assays were developed against the clinically validated tyrosine kinase 2 (TYK2) and screened against annotated compound sets. FLT provided a marked decrease in the number of false-positive hits when compared to TR-FRET. Further cellular screening confirmed that a number of potential inhibitors directly interacted with TYK2 and inhibited the downstream phosphorylation of the signal transducer and activator of transcription 4 protein (STAT4).

Developments in the Discovery and Design of Protein Kinase D Inhibitors

Protein kinase D (PKD) is a serine/threonine kinase family belonging to the Ca2+/calmodulin-dependent protein kinase group. Since its discovery two decades ago, many efforts have been put in elucidating PKD's structure, cellular role and functioning. The PKD family consists of three highly homologous isoforms: PKD1, PKD2 and PKD3. Accumulating cell-signaling research has evidenced that dysregulated PKD plays a crucial role in the pathogenesis of cardiac hypertrophy and several cancer types. These findings led to a broad interest in the design of small-molecule protein kinase D inhibitors. In this review, we present an extensive overview on the past and recent advances in the discovery and development of PKD inhibitors. The focus extends from broad-spectrum kinase inhibitors used in PKD signaling experiments to intentionally developed, bioactive PKD inhibitors. Finally, attention is paid to PKD inhibitors that have been identified as an off-target through large kinome screening panels.

A survey of the kinome pharmacopeia reveals multiple scaffolds and targets for the development of novel anthelmintics

Over one billion people are currently infected with a parasitic nematode. Symptoms can include anemia, malnutrition, developmental delay, and in severe cases, death. Resistance is emerging to the anthelmintics currently used to treat nematode infection, prompting the need to develop new anthelmintics. Towards this end, we identified a set of kinases that may be targeted in a nematode-selective manner. We first screened 2040 inhibitors of vertebrate kinases for those that impair the model nematode Caenorhabditis elegans. By determining whether the terminal phenotype induced by each kinase inhibitor matched that of the predicted target mutant in C. elegans, we identified 17 druggable nematode kinase targets. Of these, we found that nematode EGFR, MEK1, and PLK1 kinases have diverged from vertebrates within their drug-binding pocket. For each of these targets, we identified small molecule scaffolds that may be further modified to develop nematode-selective inhibitors. Nematode EGFR, MEK1, and PLK1 therefore represent key targets for the development of new anthelmintic medicines.

Application of a small molecule inhibitor screen approach to identify CXCR4 downstream signaling pathways that promote a mesenchymal and fulvestrant-resistant phenotype in breast cancer cells

Chemokine receptor 4 (CXCR4) and its ligand stromal-derived factor 1 (SDF-1) have well-characterized functions in cancer metastasis; however, the specific mechanisms through which CXCR4 promotes a metastatic and drug-resistant phenotype remain widely unknown. The aim of the present study was to demonstrate the application of a phenotypic screening approach using a small molecule inhibitor library to identify potential CXCR4-mediated signaling pathways. The present study demonstrated a new application of the Published Kinase Inhibitor Set (PKIS), a library of small molecule inhibitors from diverse chemotype series with varying levels of selectivity, in a phenotypic medium-throughput screen to identify potential mechanisms to pursue. Crystal violet staining and brightfield microscopy were employed to evaluate relative cell survival and changes to cell morphology in the screens. ‘Hits’ or lead active compounds in the first screen were PKIS inhibitors that reversed mesenchymal morphologies in CXCR4-activated breast cancer cells without the COOH-terminal domain (MCF-7-CXCR4-ΔCTD) and in the phenotypically mesenchymal triple-negative breast cancer cells (MDA-MB-231, BT-549 and MDA-MB-157), used as positive controls. In a following screen, the phenotypic and cell viability screen was used with a positive control that was both morphologically mesenchymal and had acquired fulvestrant resistance. Compounds within the same chemotype series were identified that exhibited biological activity in the screens, the ‘active’ inhibitors, were compared with inactive compounds. Relative kinase activity was obtained using published datasets to discover candidate kinase targets responsible for CXCR4 activity. MAP4K4 and MINK reversed both the mesenchymal and drug-resistant phenotypes, NEK9 and DYRK2 only reversed the mesenchymal morphology, and kinases, including ROS, LCK, HCK and LTK, altered the fulvestrant-resistant phenotype. Oligoarray experiments revealed pathways affected in CXCR4-activated cells, and these pathways were compared with the present screening approach to validate our screening tool. The oligoarray approach identified the integrin-mediated, ephrin B-related, RhoA, RAC1 and ErbB signaling pathways to be upregulated in MCF-7-CXCR4-ΔCTD cells, with ephrin B signaling also identified in the PKIS phenotypic screen. The present screening tool may be used to discover potential mechanisms of targeted signaling pathways in solid cancers.

The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

Kinome Profiling of Primary Endometrial Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies SRPK1 as Candidate Therapeutic Target

Endometrial carcinoma (EC) is the most common gynecologic malignancy in the United States, with limited effective targeted therapies. Endometrial tumors exhibit frequent alterations in protein kinases, yet only a small fraction of the kinome has been therapeutically explored. To identify kinase therapeutic avenues for EC, we profiled the kinome of endometrial tumors and normal endometrial tissues using Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). Our proteomics analysis identified a network of kinases overexpressed in tumors, including Serine/Arginine-Rich Splicing Factor Kinase 1 (SRPK1). Immunohistochemical (IHC) analysis of endometrial tumors confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Moreover, querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Loss-of-function studies targeting SRPK1 in an established USC cell line demonstrated SRPK1 was integral for RNA splicing, as well as cell cycle progression and survival under nutrient deficient conditions. Profiling of USC cells identified a compensatory response to SRPK1 inhibition that involved EGFR and the up-regulation of IGF1R and downstream AKT signaling. Co-targeting SRPK1 and EGFR or IGF1R synergistically enhanced growth inhibition in serous and endometrioid cell lines, representing a promising combination therapy for EC.

Recent Advances in the Discovery of Multitargeted Tyrosine Kinase Inhibitors as Anticancer Agents

The treatment of cancer has been one of the most significant challenges for the medical field. Further research on the signal transduction pathway of tumor cells is driving the rapid development of antitumor agents targeting tyrosine kinases. However, most of the currently approved tyrosine kinase inhibitors based on the "single target/single drug" design are becoming less and less effective in the treatment of complex, heterogeneous, and multigenic cancers; this also results in resistance to chemotherapy. In contrast, multitargeted tyrosine kinase inhibitors (MT-TKIs) can effectively block multiple pathways of intracellular signal transduction. Therefore, they have therapeutic advantages over single-targeted inhibitors and have become a hotspot in antitumor drug research in recent years. This minireview summarizes recent advances in the discovery of MT-TKIs based on their chemical structures. In particular, we describe the kinase inhibitory and antitumor activity of promising compounds, as well as their structure - activity relationships (SARs).

A Chemical Probe for Dark Kinase STK17B Derives Its Potency and High Selectivity through a Unique P-Loop Conformation

STK17B is a member of the death-associated protein kinase family and has been genetically linked to the development of diverse diseases. However, the role of STK17B in normal and disease pathology is poorly defined. Here, we present the discovery of thieno[3,2-d] pyrimidine SGC-STK17B-1 (11s), a high-quality chemical probe for this understudied "dark" kinase. 11s is an ATP-competitive inhibitor that showed remarkable selectivity over other kinases including the closely related STK17A. X-ray crystallography of 11s and related thieno[3,2-d]pyrimidines bound to STK17B revealed a unique P-loop conformation characterized by a salt bridge between R41 and the carboxylic acid of the inhibitor. Molecular dynamic simulations of STK17B revealed the flexibility of the P-loop and a wide range of R41 conformations available to the apo-protein. The isomeric thieno[2,3-d]pyrimidine SGC-STK17B-1N (19g) was identified as a negative control compound. The >100-fold lower activity of 19g on STK17B was attributed to the reduced basicity of its pyrimidine N1.

Crystal Structure and Inhibitor Identifications Reveal Targeting Opportunity for the Atypical MAPK Kinase ERK3

Extracellular signal-regulated kinase 3 (ERK3), known also as mitogen-activated protein kinase 6 (MAPK6), is an atypical member of MAPK kinase family, which has been poorly studied. Little is known regarding its function in biological processes, yet this atypical kinase has been suggested to play important roles in the migration and invasiveness of certain cancers. The lack of tools, such as a selective inhibitor, hampers the study of ERK3 biology. Here, we report the crystal structure of the kinase domain of this atypical MAPK kinase, providing molecular insights into its distinct ATP binding pocket compared to the classical MAPK ERK2, explaining differences in their inhibitor binding properties. Medium-scale small molecule screening identified a number of inhibitors, several of which unexpectedly exhibited remarkably high inhibitory potencies. The crystal structure of CLK1 in complex with CAF052, one of the most potent inhibitors identified for ERK3, revealed typical type-I binding mode of the inhibitor, which by structural comparison could likely be maintained in ERK3. Together with the presented structural insights, these diverse chemical scaffolds displaying both reversible and irreversible modes of action, will serve as a starting point for the development of selective inhibitors for ERK3, which will be beneficial for elucidating the important functions of this understudied kinase.

6-Bromo-N-(3-(difluoromethyl)phenyl)quinolin-4-amine

A routine synthesis was performed to furnish the title compound which incorporates a versatile difluoromethyl group on the aniline substitution of a 4-anilinoquinoline kinase inhibitor motif. In addition, the small molecule crystal structure (of the HCl salt) was solved, which uncovered that the difluoromethyl group was disordered within the packing arrangement and also a 126.08(7)° out of plane character between the respective ring systems within the molecule. The compound was fully characterized with 1H/13C-NMR and high-resolution mass spectra (HRMS), with the procedures described.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Selectivity is a key requirement of high-quality chemical probes and lead medicines; however, methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade the target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.

Mining Public Domain Data to Develop Selective DYRK1A Inhibitors

Kinases represent one of the most intensively pursued groups of targets in modern-day drug discovery. Often it is desirable to achieve selective inhibition of the kinase of interest over the remaining ∼500 kinases in the human kinome. This is especially true when inhibitors are intended to be used to study the biology of the target of interest. We present a pipeline of open-source software that analyzes public domain data to repurpose compounds that have been used in previous kinase inhibitor development projects. We define the dual-specificity tyrosine-regulated kinase 1A (DYRK1A) as the kinase of interest, and by addition of a single methyl group to the chosen starting point we remove glycogen synthase kinase β (GSK3β) and cyclin-dependent kinase (CDK) inhibition. Thus, in an efficient manner we repurpose a GSK3β/CDK chemotype to deliver 8b, a highly selective DYRK1A inhibitor.