Enrichment of ligands for the serotonin receptor using the Shape Signatures approach

Shape Signatures, a new 3-dimensional molecular comparison method, has been adapted to rank ligands of the serotonin receptors. A set of 825 agonists and 400 antagonists together with approximately 10,000 randomly chosen compounds from the NCI database were used in this study. Both 1D and 2D Shape Signature databases were created, and enrichment studies were carried out. Results from these studies reveal that the 1D Shape Signature approach is highly efficient in separating agonists from a mixture of molecules which includes compounds randomly selected from the NCI database taken as inactives. It is also equally effective at separating agonists and antagonists from a pool of active ligands for the serotonin receptor. Parallel enrichment studies using 2D shape signatures showed high selectivity with more restricted coverage due to the high specificity of 2D signatures. The influence of conformational variation of the shape signature on enrichment was explored by docking a subset of ligands into the crystal structure of serotonin N-acetyltransferase. Enrichment studies on the resulting "docked" conformations produced only slightly improved results compared with the CORINA-generated conformations.     

A virtual screen for diverse ligands: discovery of selective G protein-coupled receptor antagonists

Virtual screening has become a major focus of bioactive small molecule lead identification, and reports of agonists and antagonists discovered via virtual methods are becoming more frequent. G protein-coupled receptors (GPCRs) are the one class of protein targets for which success with this approach has been limited. This is likely due to the paucity of detailed experimental information describing GPCR structure and the intrinsic function-associated structural flexibility of GPCRs which present major challenges in the application of receptor-based virtual screening. Here we describe an in silico methodology that diminishes the effects of structural uncertainty, allowing for more inclusive representation of a potential docking interaction with exogenous ligands. Using this approach, we screened one million compounds from a virtual database, and a diverse subgroup of 100 compounds was selected, leading to experimental identification of five structurally diverse antagonists of the thyrotropin-releasing hormone receptors (TRH-R1 and TRH-R2). The chirality of the most potent chemotype was demonstrated to be important in its binding affinity to TRH receptors; the most potent stereoisomer was noted to have a 13-fold selectivity for TRH-R1 over TRH-R2. A comprehensive mutational analysis of key amino acid residues that form the putative binding pocket of TRH receptors further verified the binding modality of these small molecule antagonists. The described virtual screening approach may prove applicable in the search for novel small molecule agonists and antagonists of other GPCRs.     

Identification and characterization of mGlu3 ligands using a high throughput FLIPR assay for detection of agonists, antagonists, and allosteric modulators

When targeting G-protein coupled receptors (GPCRs) in early stage drug discovery, or for novel targets, the type of ligand most likely to produce the desired therapeutic effect may be unknown. Therefore, it can be desirable to identify potential lead compounds from multiple categories: agonists, antagonists, and allosteric modulators. In this study, we developed a triple addition calcium flux assay using FLIPR Tetra to identify multiple ligand classes for the metabotropic glutamate receptor 3 (mGlu3), using a cell line stably co-expressing the human G-protein-coupled mGlu3 receptor, a promiscuous G-protein (G(α16)), and rat Glast, a glutamate transporter. Compounds were added to the cells followed by stimulation with EC(10) and then EC(80) concentration of glutamate, the physiological agonist for mGlu receptors. This format produced a robust assay, facilitating the identification of agonists, positive allosteric modulators and antagonists/negative allosteric modulators. Follow up experiments were conducted to exclude false positives. Using this approach, we screened a library of approximately 800,000 compounds using FLIPR Tetra and identified viable leads for all three ligand classes. Further characterization revealed the selectivity of individual ligands.     

Improving database enrichment through ensemble docking

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.     

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.     

Virtual screening of organic molecule databases. Design of focused libraries of potential ligands of NMDA and AMPA receptors

A system of virtual screening of organic molecule databases is designed, which permits preprocessing of databases, molecular docking to a three-dimensional model of receptor, and post-processing of the results obtained. Using this screening system, it is possible to reproduce positions of the known ligands in the glutamate sites of the NMDA and AMPA receptors and in the glycine site of the NMDA receptor, to substantially enrich the database with potentially active compounds, and to distinguish between the agonistic and antagonistic character of the action of these compounds in the case of docking to the open and closed forms of the binding sites. Based on the results of screening of a database of low-molecular-weight organic compounds (total of 135,000 structures) using models of the open and closed forms of the glutamate and glycine sites of the NMDA receptor and of the glutamate site of the AMPA receptor, focused libraries of potential agonists and antagonists of these sites were designed.     

Virtual screening and library enumeration of new hydroxycinnamates based antioxidant compounds: A complete framework

Designing of molecules for drugs is important topic from many decades. The search of new drugs is very hard, and it is expensive process. Computer assisted framework can provide the fastest way to design and screen drug-like compounds. In present work, a multidimensional approach is introduced for the designing and screening of antioxidant compounds. Antioxidants play a crucial role in ensuring that the body's oxidizing and reducing species are kept in the proper balance, minimizing oxidative stress. Machine learning models are used to predict antioxidant activity. Three hydroxycinnamates are selected as standard antioxidants. Similar compounds are searched from ChEMBL database using chemical structural similarity method. The libraries of new compounds are generated using evolutionary method. New compounds are also designed using automatic decomposition and construction building blocks. The antioxidant activity of all designed and searched compounds is predicted using machine learning models. The chemical space of searched and generated compounds is envisioned using t-distributed stochastic neighbor embedding (t-SNE) method. Best compounds are shortlisted, and their synthetic accessibility is predicted to further facilitate the experimental chemists. The chemical similarity between standard and selected compounds is also studied using fingerprints and heatmap.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global ( E HOMO ) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.     

Drug-like index: a new approach to measure drug-like compounds and their diversity

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.     

Molecular mechanism-based network-like similarity graphs reveal relationships between different types of receptor ligands and structural changes that determine agonistic, inverse-agonistic, and antagonistic effects

Receptor ligands might act as agonists, partial agonists, inverse agonists, or antagonists and it is often difficult to understand structural modifications that alter the mechanism of action. In order to compare ligands that are active against a given receptor but have different mechanisms of action, we have designed molecular networks that mirror similarity relationships and incorporate both mechanism of action information and mechanism-specific SAR features. These network representations make it possible to systematically evaluate relationships between different types of receptor ligands and identify communities of structurally very similar ligands with different mechanisms. From a series of such ligands, structural modifications can often be deduced that lead to "mechanism hops".     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

Homology modelling of the human adenosine A2B receptor based on X-ray structures of bovine rhodopsin, the β2-adrenergic receptor and the human adenosine A2A receptor

A three-dimensional model of the human adenosine A_2B receptor was generated by means of homology modelling, using the crystal structures of bovine rhodopsin, the β₂-adrenergic receptor, and the human adenosine A_2A receptor as templates. In order to compare the three resulting models, the binding modes of the adenosine A_2B receptor antagonists theophylline, ZM241385, MRS1706, and PSB601 were investigated. The A_2A-based model was much better able to stabilize the ligands in the binding site than the other models reflecting the high degree of similarity between A_2A and A_2B receptors: while the A_2B receptor shares about 21% of the residues with rhodopsin, and 31% with the β₂-adrenergic receptor, it is 56% identical to the adenosine A_2A receptor. The A_2A-based model was used for further studies. The model included the transmembrane domains, the extracellular and the intracellular hydrophilic loops as well as the terminal domains. In order to validate the usefulness of this model, a docking analysis of several selective and nonselective agonists and antagonists was carried out including a study of binding affinities and selectivities of these ligands with respect to the adenosine A_2A and A_2B receptors. A common binding site is proposed for antagonists and agonists based on homology modelling combined with site-directed mutagenesis and a comparison between experimental and calculated affinity data. The new, validated A_2B receptor model may serve as a basis for developing more potent and selective drugs.     

3-D QSAR for intrinsic activity of 5-HT1A receptor ligands by the method of comparative molecular field analysis

The affinity of a ligand for a receptor is usually expressed in terms of the dissociation constant (K_i) of the drug-receptor complex, conveniently measured by the inhibition of radioligand binding. However, a ligand can be an antagonist, a partial agonist, or a full agonist, a property largely independent of its receptor affinity. This property can be quantitated as intrinsic activity (1A), which can range from 0 for a full antagonist to 1 for a full agonist. Although quantitative structure–activity relationship (QSAR) methods have been applied to the prediction of receptor affinity with considerable success, the prediction of IA, even qualitatively, has rarely been attempted. Because most traditional QSAR methods are limited to congeneric series, and there are often major structural differences between agonists and antagonists, this lack of success in predicting IA is understandable. To overcome this limitation, we used the method of comparative molecular field analysis (CoMFA), which, unlike traditional Hansch analysis, permits the inclusion of structurally dissimilar compounds in a single QSAR model. A structurally diverse set of 5-hydroxytryptamine_1A (5-HT_1A) receptor ligands, with literature IA data (determined by the inhibition of 5-HT sensitive forskolin-stimulated adenylate cyclase), was used to develop a 3-D QSAR model correlating intrinsic activity with molecular structure properties of 5HT_1A receptor ligands. This CoMFA model had a crossvalidated r² of 0.481, five components and final conventional r² of 0.943. The receptor model suggests that agonist and antagonist ligands can share parts of a common binding site on the receptor, with a primary agonist binding region that is also occupied by antagonists and a secondary binding site accommodating the excess bulk present in the sidechains of many antagonists and partial agonists. The CoMFA steric field graph clearly shows that agonists tend to be “flatter” (more coplanar) than antagonists, consistent with the difference between the 5-HT_1A agonist and antagonist pharmacophores proposed by Hibert and coworkers. The CoMFA electrostatic field graph suggests that, in the region surrounding the essential protonated aliphatic amino group, the positive molecular electrostatic potential may be weaker in antagonists as compared to agonists. Together, the steric and electrostatic maps suggest that in the secondary binding site region increased hydrophobic binding may enhance antagonist activity. These results demonstrate that CoMFA is capable of generating a statistically crossvalidated 3-D QSAR model that can successfully distinguish between agonist and antagonist 5-HT_1A ligands. To the best of our knowledge, this is the first time this or any other QSAR method has been successfully applied to the correlation of structure with IA rather than potency or affinity. The analysis has suggested various structural features associated with agonist and antagonist behaviors of 5-HT_1A ligands and thus should assist in the future design of drugs that act via 5-HT_1A receptors. © 1993 John Wiley & Sons, Inc.     

Identification of Novel Cannabinoid CB2 Receptor Agonists from Botanical Compounds and Preliminary Evaluation of Their Anti-Osteoporotic Effects

As cannabinoid CB2 receptors (CB2R) possess various pharmacological effects—including anti-epilepsy, analgesia, anti-inflammation, anti-fibrosis, and regulation of bone metabolism—without the psychoactive side effects induced by cannabinoid CB1R activation, they have become the focus of research and development of new target drugs in recent years. The present study was intended to (1) establish a double luciferase screening system for a CB2R modulator; (2) validate the agonistic activities of the screened compounds on CB2R by determining cAMP accumulation using HEK293 cells that are stably expressing CB2R; (3) predict the binding affinity between ligands and CB2 receptors and characterize the binding modes using molecular docking; (4) analyze the CB2 receptors–ligand complex stability, conformational behavior, and interaction using molecular dynamics; and (5) evaluate the regulatory effects of the screened compounds on bone metabolism in osteoblasts and osteoclasts. The results demonstrated that the screening system had good stability and was able to screen cannabinoid CB2R modulators from botanical compounds. Altogether, nine CB2R agonists were identified by screening from 69 botanical compounds, and these CB2R agonists exhibited remarkable inhibitory effects on cAMP accumulation and good affinity to CB2R, as evidenced by the molecular docking and molecular dynamics. Five of the nine CB2R agonists could stimulate osteoblastic bone formation and inhibit osteoclastic bone resorption. All these findings may provide useful clues for the development of novel anti-osteoporotic drugs and help elucidate the mechanism underlying the biological activities of CB2R agonists identified from the botanical materials.     

Structure-Functions Relationships of the Benzodiazepine and Serotonine Receptors Ligands

Abstract

States of anxiety and fear are controlled in organism by GABA-ergic and serotonine- ergic systems. Concepts about the structure and functions of GABA_A receptor channel, benzodiazepine and serotonine receptors have been considered. Structural and conformational peculiarities of ligands of these receptors determine their role (agonists, antagonists, inverse agonists, partial agonists, partial inverse agonists) at the formation of supramolecular complexes ?ligand-receptor’ and, as a result, pharmacological effects of ligands.     

Design of Drug Efficacy Guided by Free Energy Simulations of the β2-Adrenoceptor

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the β₂ adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.     

Estrogen receptor microarrays: subtype-selective ligand binding

We present the first example of a nuclear hormone receptor microarray, using for illustration the ligand-binding domains of the two estrogen receptors, ERalpha-LBD and ERbeta-LBD. The proteins are printed and allowed to attach to aldehyde slides; the efficiency of attachment depends on whether the LBD is liganded with agonists (low attachment) versus liganded with antagonists or unliganded (high attachment). This suggests that attachment is orientation specific and involves principally a single lysine residue. The attached ERs retain good ligand-binding activity that can be assessed using an estradiol-fluorophore conjugate, and specific and ER subtype-selective binding of ligands can be determined conveniently in competitive binding assays. This powerful new, high-throughput technique to study ligand binding to ER-LBDs can be extended to other nuclear hormone receptors and adapted to assay the recruitment of coregulator proteins.