Discovery and Optimization of Selective Brain-Penetrant EBP Inhibitors that Enhance Oligodendrocyte Formation.

The inhibition of emopamil binding protein (EBP), a sterol isomerase within the cholesterol biosynthesis pathway, promotes oligodendrocyte formation, which has been proposed as a potential therapeutic approach for treating multiple sclerosis. Herein, we describe the discovery and optimization of brain-penetrant, orally bioavailable inhibitors of EBP. A structure-based drug design approach from literature compound 1 led to the discovery of a hydantoin-based scaffold, which provided balanced physicochemical properties and potency and an improved in vitro safety profile. The long half-lives of early hydantoin-based EBP inhibitors in rodents prompted an unconventional optimization strategy, focused on increasing metabolic turnover while maintaining potency and a brain-penetrant profile. The resulting EBP inhibitor 11 demonstrated strong in vivo target engagement in the brain, as illustrated by the accumulation of EBP substrate zymostenol after repeated dosing. Furthermore, compound 11 enhanced the formation of oligodendrocytes in human cortical organoids, providing additional support for our therapeutic hypothesis.

Dual targeting of salt inducible kinases and CSF1R uncouples bone formation and bone resorption.

Bone formation and resorption are typically coupled, such that the efficacy of anabolic osteoporosis treatments may be limited by bone destruction. The multi-kinase inhibitor YKL-05-099 potently inhibits salt inducible kinases (SIKs) and may represent a promising new class of bone anabolic agents. Here, we report that YKL-05-099 increases bone formation in hypogonadal female mice without increasing bone resorption. Postnatal mice with inducible, global deletion of SIK2 and SIK3 show increased bone mass, increased bone formation, and, distinct from the effects of YKL-05-099, increased bone resorption. No cell-intrinsic role of SIKs in osteoclasts was noted. In addition to blocking SIKs, YKL-05-099 also binds and inhibits CSF1R, the receptor for the osteoclastogenic cytokine M-CSF. Modeling reveals that YKL-05-099 binds to SIK2 and CSF1R in a similar manner. Dual targeting of SIK2/3 and CSF1R induces bone formation without concomitantly increasing bone resorption and thereby may overcome limitations of most current anabolic osteoporosis therapies.

Exploiting Allosteric Properties of RAF and MEK Inhibitors to Target Therapy-Resistant Tumors Driven by Oncogenic BRAF Signaling.

Current clinical RAF inhibitors (RAFi) inhibit monomeric BRAF (mBRAF) but are less potent against dimeric BRAF (dBRAF). RAFi equipotent for mBRAF and dBRAF have been developed but are predicted to have lower therapeutic index. Here we identify a third class of RAFi that selectively inhibits dBRAF over mBRAF. Molecular dynamic simulations reveal restriction of the movement of the BRAF αC-helix as the basis of inhibitor selectivity. Combination of inhibitors based on their conformation selectivity (mBRAF- plus dBRAF-selective plus the most potent BRAF-MEK disruptor MEK inhibitor) promoted suppression of tumor growth in BRAFV600E therapy-resistant models. Strikingly, the triple combination showed no toxicities, whereas dBRAF-selective plus MEK inhibitor treatment caused weight loss in mice. Finally, the triple combination achieved durable response and improved clinical well-being in a patient with stage IV colorectal cancer. Thus, exploiting allosteric properties of RAF and MEK inhibitors enables the design of effective and well-tolerated therapies for BRAFV600E tumors. SIGNIFICANCE: This work identifies a new class of RAFi that are selective for dBRAF over mBRAF and determines the basis of their selectivity. A rationally designed combination of RAF and MEK inhibitors based on their conformation selectivity achieved increased efficacy and a high therapeutic index when used to target BRAFV600E tumors.See related commentary by Zhang and Bollag, p. 1620.This article is highlighted in the In This Issue feature, p. 1601.

Type II Binders Targeting the "GLR-Out" Conformation of the Pseudokinase STRADα.

Pseudokinases play important roles in signal transduction and cellular processes similar to those of catalytically competent kinases. However, pseudokinase pharmacological tractability and conformational space accessibility are poorly understood. Pseudokinases have only recently been suggested to adopt "inactive" conformations or interact with conformation-specific kinase inhibitors (e.g., type II compounds). In this work, the heavily substituted pseudokinase STRADα, which possesses a DFG → GLR substitution in the catalytic site that permits nucleotide binding while impairing divalent cation coordination, is used as a test case to demonstrate the potential applicability of conformation-specific, type II compounds to pseudokinase pharmacology. Integrated structural modeling is employed to generate a "GLR-out" conformational ensemble. Likely interacting type II compounds are identified through virtual screening against this ensemble model. Biophysical validation of compound binding is demonstrated through protein thermal stabilization and ATP competition. Localization of a top-performing compound through surface methylation strongly suggests that STRADα can adopt the "GLR-out" conformation and interact with compounds that comply with the standard type II pharmacophore. These results suggest that, despite a loss of catalytic function, some pseudokinases, including STRADα, may retain the conformational switching properties of conventional protein kinases.

Identification of a G-Protein-Independent Activator of GIRK Channels.

G-protein-gated inwardly rectifying K+ (GIRK) channels are essential effectors of inhibitory neurotransmission in the brain. GIRK channels have been implicated in diseases with abnormal neuronal excitability, including epilepsy and addiction. GIRK channels are tetramers composed of either the same subunit (e.g., homotetramers) or different subunits (e.g., heterotetramers). Compounds that specifically target subsets of GIRK channels in vivo are lacking. Previous studies have shown that alcohol directly activates GIRK channels through a hydrophobic pocket located in the cytoplasmic domain of the channel. Here, we report the identification and functional characterization of a GIRK1-selective activator, termed GiGA1, that targets the alcohol pocket. GiGA1 activates GIRK1/GIRK2 both in vitro and in vivo and, in turn, mitigates the effects of a convulsant in an acute epilepsy mouse model. These results shed light on the structure-based development of subunit-specific GIRK modulators that could provide potential treatments for brain disorders.

High-Resolution Structure and Inhibition of the Schizophrenia-Linked Pseudokinase ULK4.

The ULK (UNC51-like) enzymes are a family of mammalian kinases that have critical roles in autophagy and development. While ULK1, ULK2, and ULK3 have been characterized, very little is known about ULK4. However, recently, deletions in ULK4 have been genetically linked to increased susceptibility to developing schizophrenia, a devastating neuropsychiatric disease with high heritability but few genes identified. Interestingly, ULK4 is a pseudokinase with some unusual mutations in the kinase catalytic motifs. Here, we report the first structure of the human ULK4 kinase at high resolution and show that although ULK4 has no apparent phosphotransfer activity, it can strongly bind ATP. We find an unusual mechanism for binding ATP in a Mg2+-independent manner, including a rare hydrophobic bridge in the active site. In addition, we develop two assays for ATP binding to ULK4, perform a virtual and experimental screen to identify small-molecule binders of ULK4, and identify several novel scaffolds that bind ULK4 and can lead the way to more selective small molecules that may help shed light on the function of this enigmatic protein.

Structures of ligand-occupied ß-Klotho complexes reveal a molecular mechanism underlying endocrine FGF specificity and activity.

The three members of the endocrine fibroblast growth factor (FGF) family designated FGF19, FGF21, and FGF23 mediate their pleiotropic cellular effects by binding to and activating binary complexes composed of an FGF receptor (FGFR) bound to either α-Klotho or ß-Klotho receptors. Structural analyses of ligand-occupied Klotho extracellular domains have provided important insights concerning mechanisms underlying the binding specificities of FGF21 and FGF23 to ß-Klotho or α-Klotho, respectively. They have also demonstrated that Klotho proteins function as primary high-affinity receptors while FGFRs function as the catalytic subunits that mediate intracellular signaling. Here we describe the crystal structure the C-terminal tail of FGF19 (FGF19CT) bound to sKLB and demonstrate that FGF19CT and FGF21CT bind to the same binding site on sKLB, via a multiturn D-P motif to site 1 and via a S-P-S motif to the pseudoglycoside hydrolase region (site 2). Binding affinities to sKLB and cellular stimulatory activities of FGF19CT, FGF21CT, and a variety of chimeric mutants to cells expressing ß-Klotho together with FGFR1c or FGFR4 were also analyzed. These experiments as well as detailed comparison of the structures of free and ligand-occupied sKLB to the structure of ligand-occupied sKLA reveal a general mechanism for recognition of endocrine FGFs by Klotho proteins and regulatory interactions with FGFRs that control their pleiotropic cellular responses.

Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network.

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRetM955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a 'hybrid' molecule with improved suppression of dRetM955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity.

Functional and structural analysis of rare SLC2A2 variants associated with Fanconi-Bickel syndrome and metabolic traits.

Deleterious variants in SLC2A2 cause Fanconi-Bickel Syndrome (FBS), a glycogen storage disorder, whereas less common variants in SLC2A2 associate with numerous metabolic diseases. Phenotypic heterogeneity in FBS has been observed, but its causes remain unknown. Our goal was to functionally characterize rare SLC2A2 variants found in FBS and metabolic disease-associated variants to understand the impact of these variants on GLUT2 activity and expression and establish genotype-phenotype correlations. Complementary RNA-injected Xenopus laevis oocytes were used to study mutant transporter activity and membrane expression. GLUT2 homology models were constructed for mutation analysis using GLUT1, GLUT3, and XylE as templates. Seventeen FBS variants were characterized. Only c.457_462delCTTATA (p.Leu153_Ile154del) exhibited residual glucose uptake. Functional characterization revealed that only half of the variants were expressed on the plasma membrane. Most less common variants (except c.593 C>A (p.Thr198Lys) and c.1087 G>T (p.Ala363Ser)) exhibited similar GLUT2 transport activity as the wild type. Structural analysis of GLUT2 revealed that variants affect substrate-binding, steric hindrance, or overall transporter structure. The mutant transporter that is associated with a milder FBS phenotype, p.Leu153_Ile154del, retained transport activity. These results improve our overall understanding of the underlying causes of FBS and impact of GLUT2 function on various clinical phenotypes ranging from rare to common disease.

An IRAK1-PIN1 signalling axis drives intrinsic tumour resistance to radiation therapy.

Drug-based strategies to overcome tumour resistance to radiotherapy (R-RT) remain limited by the single-agent toxicity of traditional radiosensitizers (for example, platinums) and a lack of targeted alternatives. In a screen for compounds that restore radiosensitivity in p53 mutant zebrafish while tolerated in non-irradiated wild-type animals, we identified the benzimidazole anthelmintic oxfendazole. Surprisingly, oxfendazole acts via the inhibition of IRAK1, a kinase thus far implicated in interleukin-1 receptor (IL-1R) and Toll-like receptor (TLR) immune responses. IRAK1 drives R-RT in a pathway involving IRAK4 and TRAF6 but not the IL-1R/TLR-IRAK adaptor MyD88. Rather than stimulating nuclear factor-κB, radiation-activated IRAK1 prevented apoptosis mediated by the PIDDosome complex (comprising PIDD, RAIDD and caspase-2). Countering this pathway with IRAK1 inhibitors suppressed R-RT in tumour models derived from cancers in which TP53 mutations predict R-RT. Moreover, IRAK1 inhibitors synergized with inhibitors of PIN1, a prolyl isomerase essential for IRAK1 activation in response to pathogens and, as shown here, in response to ionizing radiation. These data identify an IRAK1 radiation-response pathway as a rational chemoradiation therapy target.

Encounter and React: Computer-Guided Design of Covalent Inhibitors.

Covalent inhibitors can obtain optimal selectivity and extended residence time. In this issue of Cell Chemical Biology, Shraga et al. (2019) take a comprehensive computational and experimental approach to modulate the JNK-Jun pathway through design of MKK7 covalent inhibitors. This study highlights a promising and emerging strategy for therapeutic discovery.

KinaMetrix: a web resource to investigate kinase conformations and inhibitor space.

Protein kinases are among the most explored protein drug targets. Visualization of kinase conformations is critical for understanding structure-function relationship in this family and for developing chemically unique, conformation-specific small molecule drugs. We have developed Kinformation, a random forest classifier that annotates the conformation of over 3500 protein kinase structures in the Protein Data Bank. Kinformation was trained on structural descriptors derived from functionally important motifs to automatically categorize kinases into five major conformations with pharmacological relevance. Here we present KinaMetrix (http://KinaMetrix.com), a web resource enabling researchers to investigate the protein kinase conformational space as well as a subset of kinase inhibitors that exhibit conformational specificity. KinaMetrix allows users to classify uploaded kinase structures, as well as to derive structural descriptors of protein kinases. Uploaded structures can then be compared to atomic structures of other kinases, enabling users to identify kinases that occupy a similar conformational space to their uploaded structure. Finally, KinaMetrix also serves as a repository for both small molecule substructures that are significantly associated with each conformation type, and for homology models of kinases in inactive conformations. We expect KinaMetrix to serve as a resource for researchers studying kinase structural biology or developing conformation-specific kinase inhibitors.

Novel selective thiadiazine DYRK1A inhibitor lead scaffold with human pancreatic ß-cell proliferation activity.

The Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 1A (DYRK1A) is an enzyme that has been implicated as an important drug target in various therapeutic areas, including neurological disorders (Down syndrome, Alzheimer's disease), oncology, and diabetes (pancreatic ß-cell expansion). Current small molecule DYRK1A inhibitors are ATP-competitive inhibitors that bind to the kinase in an active conformation. As a result, these inhibitors are promiscuous, resulting in pharmacological side effects that limit their therapeutic applications. None are in clinical trials at this time. In order to identify a new DYRK1A inhibitor scaffold, we constructed a homology model of DYRK1A in an inactive, DFG-out conformation. Virtual screening of 2.2 million lead-like compounds from the ZINC database, followed by in vitro testing of selected 68 compounds revealed 8 hits representing 5 different chemical classes. We chose to focus on one of the hits from the computational screen, thiadiazine 1 which was found to inhibit DYRK1A with IC50 of 9.41 µM (Kd = 7.3 µM). Optimization of the hit compound 1, using structure-activity relationship (SAR) analysis and in vitro testing led to the identification of potent thiadiazine analogs with significantly improved binding as compared to the initial hit (Kd = 71-185 nM). Compound 3-5 induced human ß-cell proliferation at 5 µM while showing selectivity for DYRK1A over DYRK1B and DYRK2 at 10 µM. This newly developed DYRK1A inhibitor scaffold with unique kinase selectivity profiles has potential to be further optimized as novel therapeutics for diabetes.

Redefining the Protein Kinase Conformational Space with Machine Learning.

Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the catalytic domain of protein kinases. We then construct Kinformation, a random forest classifier, to annotate the conformation of 3,708 kinase structures in the PDB. Our classification scheme captures known active and inactive kinase conformations and defines an additional conformational state, thereby refining the current understanding of the kinase conformational space. Furthermore, network analysis of the small molecules recognized by each conformation captures chemical substructures that are associated with each conformation type. Our description of the kinase conformational space is expected to improve modeling of protein kinase structures, as well as guide the development of conformation-specific kinase inhibitors with optimal pharmacological profiles.

A whole-animal platform to advance a clinical kinase inhibitor into new disease space.

Synthetic tailoring of approved drugs for new indications is often difficult, as the most appropriate targets may not be readily apparent, and therefore few roadmaps exist to guide chemistry. Here, we report a multidisciplinary approach for accessing novel target and chemical space starting from an FDA-approved kinase inhibitor. By combining chemical and genetic modifier screening with computational modeling, we identify distinct kinases that strongly enhance ('pro-targets') or limit ('anti-targets') whole-animal activity of the clinical kinase inhibitor sorafenib in a Drosophila medullary thyroid carcinoma (MTC) model. We demonstrate that RAF-the original intended sorafenib target-and MKNK kinases function as pharmacological liabilities because of inhibitor-induced transactivation and negative feedback, respectively. Through progressive synthetic refinement, we report a new class of 'tumor calibrated inhibitors' with unique polypharmacology and strongly improved therapeutic index in fly and human MTC xenograft models. This platform provides a rational approach to creating new high-efficacy and low-toxicity drugs.

GEN3VA: aggregation and analysis of gene expression signatures from related studies.

BACKGROUND: Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies. RESULTS: Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers. CONCLUSIONS: GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va .

SLC Transporters: Structure, Function, and Drug Discovery.

The human Solute Carrier (SLC) transporters are important targets for drug development. Structure-based drug discovery for SLC transporters requires the description of their structure, dynamics, and mechanism of interaction with small molecule ligands and ions. The recent determination of atomic structures of human SLC transporters and their homologs, combined with improved computational power and prediction methods have led to an increased applicability of structure-based drug design methods for human SLC members. In this review, we provide an overview of the SLC transporters' structures and transport mechanisms. We then describe computational techniques, such as homology modeling and virtual screening that are emerging as key tools to discover chemical probes for human SLC members. We illustrate the utility of these methods by presenting case studies in which rational integration of computation and experiment was used to characterize SLC members that transport key nutrients and metabolites, including the amino acid transporters LAT-1 and ASCT2, the SLC13 family of citric acid cycle intermediate transporters, and the glucose transporter GLUT1. We conclude with a brief discussion about future directions in structure-based drug discovery for the human SLC superfamily, one of the most structurally and functionally diverse protein families in human.

Molecular Basis for Redox Activation of Epidermal Growth Factor Receptor Kinase.

Epidermal growth factor receptor (EGFR) is a target of signal-derived H2O2, and oxidation of active-site cysteine 797 to sulfenic acid enhances kinase activity. Although a major class of covalent drugs targets C797, nothing is known about its catalytic importance or how S-sulfenylation leads to activation. Here, we report the first detailed functional analysis of C797. In contrast to prior assumptions, mutation of C797 diminishes catalytic efficiency in vitro and cells. The experimentally determined pKa and reactivity of C797 toward H2O2 correspondingly distinguish this residue from the bulk of the cysteinome. Molecular dynamics simulation of reduced versus oxidized EGFR, reinforced by experimental testing, indicates that sulfenylation of C797 allows new electrostatic interactions to be formed with the catalytic loop. Finally, we show that chronic oxidative stress yields an EGFR subpopulation that is refractory to the FDA-approved drug afatinib. Collectively, our data highlight the significance of redox biology to understanding kinase regulation and drug pharmacology.

Inhibitor Discovery for the Human GLUT1 from Homology Modeling and Virtual Screening.

The human Glucose Transporter 1 (hGLUT1 or SLC2A1) is a facilitative membrane transporter found in the liver, intestines, kidney, and brain, where it transports sugars such as d-glucose and d-galactose. Genetic variations in hGLUT1 are associated with a broad range of diseases and metabolic disorders. For example, hGLUT1 is upregulated in various cancer types (e.g., breast carcinoma) to support the increased anaerobic glycolysis and the Warburg effect. Thus, hGLUT1 is an emerging therapeutic target, which also transports commonly used cancer biomarkers (e.g., (18)F-DG). In this study, we use computational prediction followed by experimental testing, to characterize hGLUT1. We construct homology models of hGLUT1 in a partially occluded outward open ("occluded") conformation based on the X-ray structure of the E. coli xylose transporter, XylE. Comparison of the binding site of the occluded models to experimentally determined hGLUT structures revealed a hydrophobic pocket adjacent to the sugar-binding site, which was tested experimentally via site-directed mutagenesis. Virtual screening of various libraries of purchasable compounds against the occluded models, followed by experimental testing with cellular assays revealed seven previously unknown hGLUT1 ligands with IC50 values ranging from 0.45 µM to 59 µM. These ligands represent three unique chemotypes that are chemically different from any other known hGLUT1 ligands. The newly characterized hydrophobic pocket can potentially be utilized by the new ligands for increased affinity. Furthermore, the previously unknown hGLUT1 ligands can serve as chemical tools to further characterize hGLUT1 function or lead molecules for future drug development.

Identifying binding hot spots on protein surfaces by mixed-solvent molecular dynamics: HIV-1 protease as a test case.

Mixed-solvent molecular dynamics (MixMD) simulations use full protein flexibility and competition between water and small organic probes to achieve accurate hot-spot mapping on protein surfaces. In this study, we improved MixMD using human immunodeficiency virus type-1 protease (HIVp) as the test case. We used three probe-water solutions (acetonitrile-water, isopropanol-water, and pyrimidine-water), first at 50% w/w concentration and later at 5% v/v. Paradoxically, better mapping was achieved by using fewer probes; 5% simulations gave a superior signal-to-noise ratio and far fewer spurious hot spots than 50% MixMD. Furthermore, very intense and well-defined probe occupancies were observed in the catalytic site and potential allosteric sites that have been confirmed experimentally. The Eye site, an allosteric site underneath the flap of HIVp, has been confirmed by the presence of a 5-nitroindole fragment in a crystal structure. MixMD also mapped two additional hot spots: the Exo site (between the Gly16-Gly17 and Cys67-Gly68 loops) and the Face site (between Glu21-Ala22 and Val84-Ile85 loops). The Exo site was observed to overlap with crystallographic additives such as acetate and dimethyl sulfoxide that are present in different crystal forms of the protein. Analysis of crystal structures of HIVp in different symmetry groups has shown that some surface sites are common interfaces for crystal contacts, which means that they are surfaces that are relatively easy to desolvate and complement with organic molecules. MixMD should identify these sites; in fact, their occupancy values help establish a solid cut-off where "druggable" sites are required to have higher occupancies than the crystal-packing faces.