Structural characterization of human Vaccinia-Related Kinases (VRK) bound to small-molecule inhibitors identifies different P-loop conformations

The human genome encodes two active Vaccinia-related protein kinases (VRK), VRK1 and VRK2. These proteins have been implicated in a number of cellular processes and linked to a variety of tumors. However, understanding the cellular role of VRKs and establishing their potential use as targets for therapeutic intervention has been limited by the lack of tool compounds that can specifically modulate the activity of these kinases in cells. Here we identified BI-D1870, a dihydropteridine inhibitor of RSK kinases, as a promising starting point for the development of chemical probes targeting the active VRKs. We solved co-crystal structures of both VRK1 and VRK2 bound to BI-D1870 and of VRK1 bound to two broad-spectrum inhibitors. These structures revealed that both VRKs can adopt a P-loop folded conformation, which is stabilized by different mechanisms on each protein. Based on these structures, we suggest modifications to the dihydropteridine scaffold that can be explored to produce potent and specific inhibitors towards VRK1 and VRK2

Discovery and characterization of a specific inhibitor of serine-threonine kinase cyclin dependent kinase-like 5 (CDKL5) demonstrates role in hippocampal CA1 physiology

Pathological loss-of-function mutations in cyclin-dependent kinase-like 5 (CDKL5) cause CDKL5 deficiency disorder (CDD), a rare and severe neurodevelopmental disorder associated with severe and medically refractory early-life epilepsy, motor, cognitive, visual, and autonomic disturbances in the absence of any structural brain pathology. Analysis of genetic variants in CDD has indicated that CDKL5 kinase function is central to disease pathology. CDKL5 encodes a serine-threonine kinase with significant homology to GSK3β, which has also been linked to synaptic function. Further, Cdkl5 knock-out rodents have increased GSK3β activity and often increased long-term potentiation (LTP). Thus, development of a specific CDKL5 inhibitor must be careful to exclude cross-talk with GSK3β activity. We synthesized and characterized specific, high-affinity inhibitors of CDKL5 that do not have detectable activity for GSK3β. These compounds are very soluble in water but blood–brain barrier penetration is low. In rat hippocampal brain slices, acute inhibition of CDKL5 selectively reduces postsynaptic function of AMPA-type glutamate receptors in a dose-dependent manner. Acute inhibition of CDKL5 reduces hippocampal LTP. These studies provide new tools and insights into the role of CDKL5 as a newly appreciated key kinase necessary for synaptic plasticity. Comparisons to rodent knock-out studies suggest that compensatory changes have limited the understanding of the roles of CDKL5 in synaptic physiology, plasticity, and human neuropathology

WNT activates the AAK1 kinase to promote clathrin-mediated endocytosis of LRP6 and establish a negative feedback loop

beta-Catenin-dependent WNT signal transduction governs development, tissue homeostasis, and a vast array of human diseases. Signal propagation through a WNT-Frizzled/LRP receptor complex requires proteins necessary for clathrin-mediated endocytosis (CME). Paradoxically, CME also negatively regulates WNT signaling through internalization and degradation of the receptor complex. Here, using a gain-of-function screen of the human kinome, we report that the AP2 associated kinase 1 (AAK1), a known CME enhancer, inhibits WNT signaling. Reciprocally, AAK1 genetic silencing or its pharmacological inhibition using a potent and selective inhibitor activates WNT signaling. Mechanistically, we show that AAK1 promotes clearance of LRP6 from the plasma membrane to suppress the WNT pathway. Time-course experiments support a transcription-uncoupled, WNT-driven negative feedback loop; prolonged WNT treatment drives AAK1-dependent phosphorylation of AP2M1, clathrin-coated pit maturation, and endocytosis of LRP6. We propose that, following WNT receptor activation, increased AAK1 function and CME limits WNT signaling longevity2617993FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50724-5; 2016/17469-0M.B.M. acknowledges support from the NIH (RO1-CA187799 and U24-DK116204-01). M.J.A. received financial support from NIH T32 Predoctoral Training Grants in Pharmacology (T32-GM007040-43 and T32-GM007040-42), an Initiative for Maximizing Student Diversity Grant (R25-GM055336-16), and the NIH National Cancer Institute (NCI) NRSA Predoctoral Fellowship to Promote Diversity in Health-Related Research (F31CA228289). M.P.W. received support from the Lymphoma Research Foundation (337444) and the NIH (T32-CA009156-35). Y.N. was supported by grants-in-aid from the Japan Society for the Promotion of Science (JSPS) (15KK0356 and 16K11493). T.T. was supported by the Howard Hughes Medical Institute Gilliam Fellowship for Advanced Study. M.V.G. was supported by Cancer Research UK (grants C7379/A15291 and C7379/A24639 to Mariann Bienz). The UNC Flow Cytometry Core Facility is supported in part by Cancer Center Core Support Grant P30 CA016086 to the UNC Lineberger Comprehensive Cancer Center, and research reported in this publication was supported by the Center for AIDS Research (award number 5P30AI050410), and the content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The Structural Genomics Consortium (SGC) is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, the Canada Foundation for Innovation, the Eshelman Institute for Innovation, Genome Canada, the Innovative Medicines Initiative (European Union [EU]/European Federation of Pharmaceutical Industries and Associations [EFPIA]) (ULTRA-DD grant no. 115766), Janssen, Merck & Company, Merck KGaA, Novartis Pharma AG, the Ontario Ministry of Economic Development and Innovation, Pfizer, the São Paulo Research Foundation (FAPESP) (2013/50724-5), Takeda, and the Wellcome Trust (106169/ZZ14/Z). R.R.R. received financial support from FAPESP (2016/17469-0). We would also like to thank Claire Strain-Damerell and Pavel Savitsky for cloning various mutants of AAK1 and BMP2K proteins that were used in the crystallization trials. Additionally, we thank Dr. Sean Conner for providing the AAK1 plasmids, Dr. Stephane Angers for kindly providing the HEK293T DVL TKO cells, and Dr. Mariann Bienz for providing comments and feedback. We would like to thank members of the Major laboratory for their feedback and expertise regarding experimental design and project directio

Host Kinase CSNK2 is a Target for Inhibition of Pathogenic SARS-like β-Coronaviruses

Inhibition of the protein kinase CSNK2 with any of 30 specific and selective inhibitors representing different chemotypes, blocked replication of pathogenic human, bat, and murine β-coronaviruses. The potency of in-cell CSNK2A target engagement across the set of inhibitors correlated with antiviral activity and genetic knockdown confirmed the essential role of the CSNK2 holoenzyme in β-coronavirus replication. Spike protein endocytosis was blocked by CSNK2A inhibition, indicating that antiviral activity was due in part to a suppression of viral entry. CSNK2A inhibition may be a viable target for the development of anti-SARS-like β-coronavirus drugs

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. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.</p

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

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. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

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

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases

New insights into 4-anilinoquinazolines as inhibitors of cardiac troponin I-interacting kinase (TNNI3K)

We report the synthesis of several related 4-anilinoquinazolines as inhibitors of cardiac troponin I-interacting kinase (TNNi3K). These close structural analogs of 3-((6,7-dimethoxyquinazolin-4-yl)amino)-4-(dimethylamino)-N-methylbenzenesulfonamide (GSK114) provide new understanding of structure-activity relationships between the 4-anilinoquinazoline scaffold and TNNi3K inhibition. Through a small focused library of inhibitors, we observed that the N-methylbenzenesulfonamide was driving the potency in addition to the more traditional quinazoline hinge-binding motif. We also identified a compound devoid of TNNi3K kinase activity due to the addition of a methyl group in the hinge binding region. This compound could serve as a negative control in the study of TNNi3K biology. Small molecule crystal structures of several quinazolines have been solved, supporting observations made about overall conformation and TNNi3K inhibition.</p

Design and analysis of the 4-anilinoquin(az)oline kinase inhibition profiles of GAK/SLK/STK10 using quantitative structure-activity relationships

The 4-anilinoquinoline and 4-anilinoquinazoline ring systems have been the focus of significant efforts in prior kinase drug discovery programs, which have led to approved medicines. Broad kinome profiles of these compounds have now been assessed with the advent of advanced screening technologies. These ring systems, while originally designed for specific targets including epidermal growth factor receptor (EGFR), but actually display a number of potent collateral kinase targets, some of which have been associated with negative clinical outcomes. We have designed and synthesized a series of 4-anilinoquin(az)olines in order to better understand the structure-activity relationships of three main collateral kinase targets of quin(az)oline-based kinase inhibitors: cyclin G associated kinase (GAK), STE20-like serine/threonine-protein kinase (SLK) and serine/threonine-protein kinase 10 (STK10). This was achieved through a series of quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites and extensive small-molecule X-ray structural analysis.</p