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.

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.

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.

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.

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.

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.

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 .

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.

Identification of key hinge residues important for nucleotide-dependent allostery in E. coli Hsp70/DnaK.

DnaK is a molecular chaperone that has important roles in protein folding. The hydrolysis of ATP is essential to this activity, and the effects of nucleotides on the structure and function of DnaK have been extensively studied. However, the key residues that govern the conformational motions that define the apo, ATP-bound, and ADP-bound states are not entirely clear. Here, we used molecular dynamics simulations, mutagenesis, and enzymatic assays to explore the molecular basis of this process. Simulations of DnaK's nucleotide-binding domain (NBD) in the apo, ATP-bound, and ADP/Pi-bound states suggested that each state has a distinct conformation, consistent with available biochemical and structural information. The simulations further suggested that large shearing motions between subdomains I-A and II-A dominated the conversion between these conformations. We found that several evolutionally conserved residues, especially G228 and G229, appeared to function as a hinge for these motions, because they predominantly populated two distinct states depending on whether ATP or ADP/Pi was bound. Consistent with the importance of these "hinge" residues, alanine point mutations caused DnaK to have reduced chaperone activities in vitro and in vivo. Together, these results clarify how sub-domain motions communicate allostery in DnaK.

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.

Mutagenesis reveals the complex relationships between ATPase rate and the chaperone activities of Escherichia coli heat shock protein 70 (Hsp70/DnaK).

The Escherichia coli 70-kDa heat shock protein, DnaK, is a molecular chaperone that engages in a variety of cellular activities, including the folding of proteins. During this process, DnaK binds its substrates in coordination with a catalytic ATPase cycle. Both the ATPase and protein folding activities of DnaK are stimulated by its co-chaperones, DnaJ and GrpE. However, it is not yet clear how changes in the stimulated ATPase rate of DnaK impact the folding process. In this study, we performed mutagenesis throughout the nucleotide-binding domain of DnaK to generate a collection of mutants in which the stimulated ATPase rates varied from 0.7 to 13.6 pmol/microg/min(-1). We found that this range was largely established by differences in the ability of the mutants to be stimulated by one or both of the co-chaperones. Next, we explored how changes in ATPase rate might impact refolding of denatured luciferase in vitro and found that the two activities were poorly correlated. Unexpectedly, we found several mutants that refold luciferase normally in the absence of significant ATP turnover, presumably by increasing the flexibility of DnaK. Finally, we tested whether DnaK mutants could complement growth of DeltadnaK E. coli cells under heat shock and found that the ability to refold luciferase was more predictive of in vivo activity than ATPase rate. This study provides insights into how flexibility and co-chaperone interactions affect DnaK-mediated ATP turnover and protein folding.

CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions.

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein-ligand crystal structures were assembled with experimentally determined K(d) or K(i) information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring. A total of 19 different methods were submitted with numerous parameter variations for a total of 64 sets of scores from 16 participating groups. Linear regression and nonparametric tests were used to correlate scores to the experimental values. Correlation to experiment for the various methods ranged R(2) = 0.58-0.12, Spearman ρ = 0.74-0.37, Kendall τ = 0.55-0.25, and median unsigned error = 1.00-1.68 pK(d) units. All types of scoring functions-force field based, knowledge based, and empirical-had examples with high and low correlation, showing no bias/advantage for any particular approach. The data across all the participants were combined to identify 63 complexes that were poorly scored across the majority of the scoring methods and 123 complexes that were scored well across the majority. The two sets were compared using a Wilcoxon rank-sum test to assess any significant difference in the distributions of >400 physicochemical properties of the ligands and the proteins. Poorly scored complexes were found to have ligands that were the same size as those in well-scored complexes, but hydrogen bonding and torsional strain were significantly different. These comparisons point to a need for CSAR to develop data sets of congeneric series with a range of hydrogen-bonding and hydrophobic characteristics and a range of rotatable bonds.

CSAR benchmark exercise of 2010: selection of the protein-ligand complexes.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose ( www.csardock.org ). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein-ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (K(d) or K(i)) in Binding MOAD, augmented with entries from PDB bind. The final data set contains 343 diverse protein-ligand complexes and spans 14 pK(d). Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; Pearson R is limited to ~ 0.91 (Pearson R2 0.83) when fitting to the data set without over parameterizing. Pearson R is limited to ~ 0.83(Pearson R2 ~ 0.70) when scoring the data set with a method trained on outside data [corrected]. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.

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.

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.

Chemical probes that selectively recognize the earliest Aß oligomers in complex mixtures.

Alzheimer's disease (AD) is characterized by the self-assembly of amyloid beta (Aß) peptides. Recent models implicate some of the earliest Aß oligomers, such as trimers and tetramers, in disease. However, the roles of these structures remain uncertain, in part, because selective probes of their formation are not available. Toward that goal, we generated bivalent versions of the known Aß ligand, the pentapeptide KLVFF. We found that compounds containing sufficiently long linkers (∼19 to 24 Å) recognized primarily Aß trimers and tetramers, with little binding to either monomer or higher order structures. These compounds might be useful probes for early Aß oligomers.

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.

Automated genome mining for natural products.

BACKGROUND: Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit. RESULTS: We have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases. CONCLUSION: Using this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.

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.