Crystal structure of an ephrin ectodomain.

Eph receptor tyrosine kinases and their membrane-associated ligands, the ephrins, are essential regulators of axon guidance, cell migration, segmentation, and angiogenesis. There are two classes of vertebrate ephrin ligands which have distinct binding specificities for their cognate receptors. Multimerization of the ligands is required for receptor activation, and ephrin ligands themselves signal intracellularly upon binding Eph receptors. We have determined the structure of the extracellular domain of mouse ephrin-B2. The ephrin ectodomain is an eight-stranded beta barrel with topological similarity to plant nodulins and phytocyanins. Based on the structure, we have identified potential surface determinants of Eph/ephrin binding specificity and a ligand dimerization region. The high sequence similarity among ephrin ectodomains indicates that all ephrins may be modeled upon the ephrin-B2 structure presented here.     

Comparative Computational Modeling of Agonist Binding to the Leukotriene Receptors BLT 1 and BLT 2

leukotriene B4 receptors BLT1 and BLT2 are promising targets for the treatment of allergic and inflammatory diseases. However, no working model of ligand binding to either of these receptors has been developed so far. Under the assumption that homologous receptors bind their ligands in a similar way, computational modeling of agonist binding to BLT1 and BLT2 was performed using fully flexible docking in Galaxy7TM. For both receptors, the carboxyl group of the ligand forms a salt bridge with an arginine residue, while the tail hydroxyl groups form hydrogen bonds with three amino acid residues. The differential specificity of ligands to BLT1 and BLT2 is explained by the replacement of histidine with tyrosine. In BLT1, the histidine residue binds the 5-OH group of the ligand, while the tyrosine residue in BLT2 repels it. The presented models are in agreement with experimental data and may be useful for developing new BLT1- and BLT2-targeted drugs.     

Chemical,Target, and Bioactive Properties of Allosteric Modulation

Allosteric modulators are ligands for proteins that exert their effects via a different binding site than the natural (orthosteric) ligand site and hence form a conceptually distinct class of ligands for a target of interest. Here, the physicochemical and structural features of a large set of allosteric and non-allosteric ligands from the ChEMBL database of bioactive molecules are analyzed. In general allosteric modulators are relatively smaller, more lipophilic and more rigid compounds, though large differences exist between different targets and target classes. Furthermore, there are differences in the distribution of targets that bind these allosteric modulators. Allosteric modulators are over-represented in membrane receptors, ligand-gated ion channels and nuclear receptor targets, but are underrepresented in enzymes (primarily proteases and kinases). Moreover, allosteric modulators tend to bind to their targets with a slightly lower potency (5.96 log units versus 6.66 log units, p<0.01). However, this lower absolute affinity is compensated by their lower molecular weight and more lipophilic nature, leading to similar binding efficiency and surface efficiency indices. Subsequently a series of classifier models are trained, initially target class independent models followed by finer-grained target (architecture/functional class) based models using the target hierarchy of the ChEMBL database. Applications of these insights include the selection of likely allosteric modulators from existing compound collections, the design of novel chemical libraries biased towards allosteric regulators and the selection of targets potentially likely to yield allosteric modulators on screening. All data sets used in the paper are available for download.     

Structure-activity relationships of neuropeptide FF and related peptidic and non-peptidic derivatives

Neuropeptide FF, a member of the RFamide family of peptides, has demonstrated an interesting array of pharmacological effects. To date however, little information has been obtained as to the exact pharmacological roles of the individual NPFF1 and NPFF2 receptors. Through peptide analogs of NPFF and related peptides, the essential pharmacophore has emerged somewhat. Yet, the field is lacking small molecule ligands selective for each receptor. This review of the structure-activity relationships of the reported NPFF peptide analogs and some non-selective small molecule ligands highlights the current understanding of the pharmacophoric elements required for affinity and activity at the NPFF receptors. The lack of mutagenesis data on the receptor as well as a crystal structure has also hindered the understanding of ligand recognition at the receptor level. If the targets can be further investigated as to their requirements for ligand recognition, the successful development of highly selective ligands should follow.     

Evidence for different pharmacological targets for imidazoline compounds inhibiting settlement of the barnacle Balanus improvisus

We describe the effect of eight different imidazoline/guanidinium compounds on the settlement and metamorphosis of larvae of the barnacle Balanus improvisus. These agents were chosen on the basis of their similar pharmacological classification in vertebrates and their chemical similarity to medetomidine and clonidine, previously described as highly potent settlement inhibitors (nanomolar range). Seven of the tested compounds were found to inhibit settlement in a dose-dependent manner in concentrations ranging from 100 nM to 10 microM without any significant lethal effects. In vertebrate systems these substances have overlapping functions and interact with both alpha-adrenoceptors as well as imidazoline binding sites. Antagonizing experiments using the highly specific alpha(2)-antagonist methoxy-idazoxan or agmatine (the putative endogenous ligand at imidazoline receptors) were performed to discriminate between putative pharmacological mechanisms involved in the inhibition of cyprid settlement. Agmatine was not able to reverse the effect of any of the tested compounds. However, methoxy-idazoxan almost completely abolished the settlement inhibition mediated by guanabenz (alpha(2)-agonist, I(2) ligand), moxonidine (alpha(2)-agonist, I(1) ligand) and tetrahydrozoline (alpha-agonist, I(2) ligand). The actions of cirazoline (alpha(1)-agonist, I(2) ligand) BU 224 (I(2) ligand) and metrazoline (I(2) ligand) were not reversed by treatment with methoxy-idazoxan. These results suggest that the settlement inhibition evoked by the I(2) ligands and alpha(2)-agonists used in this study of the neurologically simple but well-organized barnacle larva is mediated through different physiological targets important in the overall settlement process.     

Biased signaling: potential agonist and antagonist of PAR2

Protease activated receptor 2 (PAR2) has emerged as one of the promising therapeutic targets to inhibit rapidly metastasizing breast cancer cells. However, its elusive molecular mechanism of activation and signaling has made it a difficult target for drug development. In this study, in silico methods were used to unfold PAR2 molecular mechanism of signaling based on the concept of GPCR receptor plasticity. Although, there are no conclusive evidences of the presence of specific endogenous ligands for PAR2, the efficacy of synthetic agonist and antagonist in PAR2 signaling has opened up the possibilities of ligand-mediated signaling. Furthermore, it has been proved that ligands specific for one GPCR can induce signaling in GPCRs belonging to other subfamilies. Therefore, the aim of this study was to identify potential agonists and antagonists from the GPCR ligand library (GLL), which may induce biased signaling in PAR2 using the concept of existence of multiple ligand-stabilized receptor conformations. The results of our in silico study suggest that PAR2 may show biased signaling mainly with agonists of serotonin type 1, β-adrenergic type 1,3 and antagonists of substance K (NK1), serotonin type 2, dopamine type 4, and thromboxane receptors. Further, this study also throws light on the putative ligand-specific conformations of PAR2. Thus, the results of this study provide structural insights to putative conformations of PAR2 and also gives initial clues to medicinal chemists for rational drug design targeting this challenging receptor.     

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.     

Merging chemical and biological space: Structural mapping of enzyme binding pocket space

Structure‐based drug design tries to mutually map pharmacological space populated by putative target proteins onto chemical space comprising possible small molecule drug candidates. Both spaces are connected where proteins and ligands recognize each other: in the binding pockets. Therefore, it is highly relevant to study the properties of the space composed by all possible binding cavities. In the present contribution, a global mapping of protein cavity space is presented by extracting consensus cavities from individual members of protein families and clustering them in terms of their shape and exposed physicochemical properties. Discovered similarities indicate common binding epitopes in binding pockets independent of any possibly given similarity in sequence and fold space. Unexpected links between remote targets indicate possible cross‐reactivity of ligands and suggest putative side effects. The global clustering of cavity space is compared to a similar clustering of sequence and fold space and compared to chemical ligand space spanned by the chemical properties of small molecules found in binding pockets of crystalline complexes. The overall similarity architecture of sequence, fold, and cavity space differs significantly. Similarities in cavity space can be mapped best to similarities in ligand binding space indicating possible cross‐reactivities. Most cross‐reactivities affect co‐factor and other endogenous ligand binding sites. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Vicinity analysis: a methodology for the identification of similar protein active sites

Vicinity analysis (VA) is a new methodology developed to identify similarities between protein binding sites based on their three-dimensional structure and the chemical similarity of matching residues. The major objective is to enable searching of the Protein Data Bank (PDB) for similar sub-pockets, especially in proteins from different structural and biochemical series. Inspection of the ligands bound in these pockets should allow ligand functionality to be identified, thus suggesting novel monomers for use in library synthesis. VA has been developed initially using the ATP binding site in kinases, an important class of protein targets involved in cell signalling and growth regulation. This paper defines the VA procedure and describes matches to the phosphate binding sub-pocket of cyclin-dependent protein kinase 2 that were found by searching a small test database that has also been used to parameterise the methodology.     

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.     

Photoaffinity scanning in the mapping of the peptide receptor interface of class II G protein-coupled receptors.

The family of G protein-coupled receptors constitutes about 50% of the therapeutic drug targets used in clinical medicine today, although the mechanisms of ligand binding, activation and signal transduction for G protein-coupled receptors are not yet well defined. This review discusses ongoing research using the photoaffinity scanning method to map the bimolecular interface between class II G protein-coupled receptors and their ligands. Furthermore the available computer model of class II peptide ligand docking into the receptor, based on the positional constraints imposed by the photoaffinity scanning analyses, will be discussed briefly. The ultimate goal of these efforts is to understand the molecular basis of receptor binding and therefore to generate a template for rational drug design.     

Autocrine ligand binding to cell receptors. Mathematical analysis of competition by solution "decoys".

Autocrine ligands have been demonstrated to regulate cell proliferation, cell adhesion, and cell migration in a number of different systems and are believed to be one of the underlying causes of malignant cell transformation. Binding of these ligands to their cellular receptors can be compromised by diffusive transport of ligand away from the secreting cell. Exogenous addition of antibodies or solution receptors capable of competing with cellular receptors for these autocrine ligands has been proposed as a means of inhibiting autocrine-stimulated cell behavioral responses. Such "decoys" complicate cellular binding by offering alternative binding targets, which may also be capable of aiding or abating transport of the ligand away from the cell surface. We present a mathematical model incorporating autocrine ligand production and the presence of competing cellular and solution receptors. We elucidate effects of key system parameters including ligand diffusion rate, binding rate constants, cell density, and secretion rate on the ability of solution receptors to inhibit cellular receptor binding. Both plated and suspension cell systems are considered. An approximate analytical expression relating the key parameters to the critical concentration of solution "decoys" required for inhibition is derived and compared to the numerical calculations. We find that in order to achieve essentially complete inhibition of surface receptor binding, the concentration of decoys may need to be as much as four to eight orders of magnitude greater than the equilibrium disociation constant for ligand binding to surface receptors.     

Development of a thyroid hormone receptor targeting conjugate

Molecular conjugates of hormone receptor-ligands with molecular probes or functional domains are finding diverse applications in chemical biology. Whereas many examples of hormone conjugates that target steroid hormone receptors have been reported, practical ligand conjugates that target the nuclear thyroid hormone receptor (TRbeta) are lacking. TR-targeting conjugate scaffolds based on the ligands GC-1 and NH-2 and the natural ligand triiodothyronine (T3) were synthesized and evaluated in vitro and in cellular assays. Whereas the T3 or GC-1 based conjugates did not bind TRbeta with high affinity, the NH-2 inspired fluorescein-conjugate JZ01 showed low nanomolar affinity for TRbeta and could be used as a nonradiometric probe for ligand binding. A related analogue JZ07 was a potent TR antagonist that is 13-fold selective for TRbeta over TRalpha. JZ01 localizes in the nuclei of TRbeta expressing cells and may serve as a prototype for other TR-targeting conjugates.     

Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification

Background

Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.

Methodology/Principal Findings

Based on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.

Conclusions/Significance

Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.     

The effect of ligand heterogeneity on the Scatchard plot. Particular relevance to lipoprotein binding analysis

Computer simulations of equilibrium binding studies of a mixture of two labeled ligands binding competitively to a single class of identical and independent sites (receptors) were performed to investigate how ligand heterogeneity affects the observed data in such studies. The simulated data are presented in Scatchard plots. Ligand heterogeneity was generally found to be indistinguishable from the case of a homogeneous ligand when usual experimental conditions applied (that is, Scatchard plots of the data were straight lines). Some factors that increased the probability of recognizing heterogeneity in the system were identified, however. These are 1) a large difference between the dissociation constants of the two ligands, 2) a high concentration of receptors relative to the dissociation constant of the higher-affinity ligand, 3) a high concentration of the lower-affinity ligand relative to that of the higher-affinity ligand, 4) a high specific activity of the lower-affinity ligand relative to that of the higher-affinity ligand, and 5) lack of experimental error. When ligand heterogeneity (under certain conditions) did cause curvilinearity in the Scatchard plot, the curve formed was always concave-downwards. Thus, ligand heterogeneity may occasionally mimic positive cooperativity, but never mimics negative cooperativity or multiple classes of binding sites. Implications of these findings for equilibrium binding studies involving lipoproteins (which are generally isolated as heterogeneous mixtures of particles) are discussed in detail. These findings are also relevant to equilibrium binding studies using ligands which are mixtures of stereoisomers or which contain chemical or radiochemical impurities.     

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization

Background  

G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors.     

Ligand Binding Site Similarity Identification Based on Chemical and Geometric Similarity

The similarity comparison of binding sites based on amino acid between different proteins can facilitate protein function identification. However, Binding site usually consists of several crucial amino acids which are frequently dispersed among different regions of a protein and consequently make the comparison of binding sites difficult. In this study, we introduce a new method, named as chemical and geometric similarity of binding site (CGS-BSite), to compute the ligand binding site similarity based on discrete amino acids with maximum-weight bipartite matching algorithm. The principle of computing the similarity is to find a Euclidean Transformation which makes the similar amino acids approximate to each other in a geometry space, and vice versa. CGS-BSite permits site and ligand flexibilities, provides a stable prediction performance on the flexible ligand binding sites. Binding site prediction on three test datasets with CGS-BSite method has similar performance to Patch-Surfer method but outperforms other five tested methods, reaching to 0.80, 0.71 and 0.85 based on the area under the receiver operating characteristic curve, respectively. It performs a marginally better than Patch-Surfer on the binding sites with small volume and higher hydrophobicity, and presents good robustness to the variance of the volume and hydrophobicity of ligand binding sites. Overall, our method provides an alternative approach to compute the ligand binding site similarity and predict potential special ligand binding sites from the existing ligand targets based on the target similarity.