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.

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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.

DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors.

Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel's performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design.