Similarity metrics for ligands reflecting the similarity of the target proteins

In this study we evaluate how far the scope of similarity searching can be extended to identify not only ligands binding to the same target as the reference ligand(s) but also ligands of other homologous targets without initially known ligands. This "homology-based similarity searching" requires molecular representations reflecting the ability of a molecule to interact with target proteins. The Similog keys, which are introduced here as a new molecular representation, were designed to fulfill such requirements. They are based only on the molecular constitution and are counts of atom triplets. Each triplet is characterized by the graph distances and the types of its atoms. The atom-typing scheme classifies each atom by its function as H-bond donor or acceptor and by its electronegativity and bulkiness. In this study the Similog keys are investigated in retrospective in silico screening experiments and compared with other conformation independent molecular representations. Studied were molecules of the MDDR database for which the activity data was augmented by standardized target classification information from public protein classification databases. The MDDR molecule set was split randomly into two halves. The first half formed the candidate set. Ligands of four targets (dopamine D2 receptor, opioid delta-receptor, factor Xa serine protease, and progesterone receptor) were taken from the second half to form the respective reference sets. Different similarity calculation methods are used to rank the molecules of the candidate set by their similarity to each of the four reference sets. The accumulated counts of molecules binding to the reference target and groups of targets with decreasing homology to it were examined as a function of the similarity rank for each reference set and similarity method. In summary, similarity searching based on Unity 2D-fingerprints or Similog keys are found to be equally effective in the identification of molecules binding to the same target as the reference set. However, the application of the Similog keys is more effective in comparison with the other investigated methods in the identification of ligands binding to any target belonging to the same family as the reference target. We attribute this superiority to the fact that the Similog keys provide a generalization of the chemical elements and that the keys are counted instead of merely noting their presence or absence in a binary form. The second most effective molecular representation are the occurrence counts of the public ISIS key fragments, which like the Similog method, incorporates key counting as well as a generalization of the chemical elements. The results obtained suggest that ligands for a new target can be identified by the following three-step procedure: 1. Select at least one target with known ligands which is homologous to the new target. 2. Combine the known ligands of the selected target(s) to a reference set. 3. Search candidate ligands for the new targets by their similarity to the reference set using the Similog method. This clearly enlarges the scope of similarity searching from the classical application for a single target to the identification of candidate ligands for whole target families and is expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

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.     

An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement

Gaining insight into the pharmacology of ligand engagement with G-protein coupled receptors (GPCRs) under biologically relevant conditions is vital to both drug discovery and basic research. NanoLuc-based bioluminescence resonance energy transfer (NanoBRET) monitoring competitive binding between fluorescent tracers and unmodified test compounds has emerged as a robust and sensitive method to quantify ligand engagement with specific GPCRs genetically fused to NanoLuc luciferase or the luminogenic HiBiT peptide. However, development of fluorescent tracers is often challenging and remains the principal bottleneck for this approach. One way to alleviate the burden of developing a specific tracer for each receptor is using promiscuous tracers, which is made possible by the intrinsic specificity of BRET. Here, we devised an integrated tracer discovery workflow that couples machine learning-guided in silico screening for scaffolds displaying promiscuous binding to GPCRs with a blend of synthetic strategies to rapidly generate multiple tracer candidates. Subsequently, these candidates were evaluated for binding in a NanoBRET ligand-engagement screen across a library of HiBiT-tagged GPCRs. Employing this workflow, we generated several promiscuous fluorescent tracers that can effectively engage multiple GPCRs, demonstrating the efficiency of this approach. We believe that this workflow has the potential to accelerate discovery of NanoBRET fluorescent tracers for GPCRs and other target classes.     

A general technique to rank protein-ligand binding affinities and determine allosteric versus direct binding site competition in compound mixtures

To realize the full potential of combinatorial chemistry-based drug discovery, generic and efficient tools must be developed that apply the strengths of diversity-oriented chemical synthesis to the identification and optimization of lead compounds for disease-associated protein targets. We report an affinity selection-mass spectrometry (AS-MS) method for protein-ligand affinity ranking and the classification of ligands by binding site. The method incorporates the following steps: (1) an affinity selection stage, where protein-binding compounds are selected from pools of ligands in the presence of varying concentrations of a competitor ligand, (2) a first chromatography stage to separate unbound ligands from protein-ligand complexes, and (3) a second chromatography stage to dissociate the ligands from the complexes for identification and quantification by MS. The ability of the competitor ligand to displace a target-bound library member, as measured by MS, reveals the binding site classification and affinity ranking of the mixture components. The technique requires no radiolabel incorporation or direct biochemical assay, no modification or immobilization of the compounds or target protein, and all reaction components, including any buffers or cofactors required for protein stability, are free in solution. We demonstrate the method for several compounds of wide structural variety against representatives of the most important protein classes in contemporary drug discovery, including novel ATP-competitive and allosteric inhibitors of the Akt-1 (PKB) and Zap-70 kinases, and previously undisclosed antagonists of the M(2) muscarinic acetylcholine receptor, a G-protein coupled receptor (GPCR). The theoretical basis of the technique is analyzed mathematically, allowing quantitative estimation of binding affinities and, in the case of allosteric interaction, absolute determination of binding cooperativity. The method is readily applicable to high-throughput screening hit triage, combinatorial library-based affinity optimization, and developing structure-activity relationships among multiple ligands to a given receptor.     

Using silico methods predicting ligands for orphan GPCRs

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.     

Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified na?ve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.     

High-throughput modeling of human G-protein coupled receptors: amino acid sequence alignment, three-dimensional model building, and receptor library screening

The current study describes the development of a computer package (GPCRmod) aimed at the high-throughput modeling of the therapeutically important family of human G-protein coupled receptors (GPCRs). GPCRmod first proposes a reliable alignment of the seven transmembrane domains (7 TMs) of most druggable human GPCRs based on pattern/motif recognition for each of the 7 TMs that are considered independently. It then converts the alignment into knowledge-based three-dimensional (3-D) models starting from a set of 3-D backbone templates and two separate rotamer libraries for side chain positioning. The 7 TMs of 277 human GPCRs have been accurately aligned, unambiguously clustered in three different classes (rhodopsin-like, secretin-like, metabotropic glutamate-like), and converted into high-quality 3-D models at a remarkable throughput (ca. 3s/model). A 3-D GPCR target library of 277 receptors has consequently been setup. Its utility for "in silico" inverse screening purpose has been demonstrated by recovering among top scorers the receptor of a selective GPCR antagonist as well as the receptors of a promiscuous antagonist. The current GPCR target library thus constitutes a 3-D database of choice to address as soon as possible the "virtual selectivity" profile of any GPCR antagonist or inverse agonist in an early hit optimization process.     

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.     

The Role of Lipids in Allosteric Modulation of Dopamine D2 Receptor—In Silico Study

The dopamine D₂ receptor, belonging to the class A G protein-coupled receptors (GPCRs), is an important drug target for several diseases, including schizophrenia and Parkinson’s disease. The D₂ receptor can be activated by the natural neurotransmitter dopamine or by synthetic ligands, which in both cases leads to the receptor coupling with a G protein. In addition to receptor modulation by orthosteric or allosteric ligands, it has been shown that lipids may affect the behaviour of membrane proteins. We constructed a model of a D₂ receptor with a long intracellular loop (ICL3) coupled with G_iα1 or G_iα2 proteins, embedded in a complex asymmetric membrane, and simulated it in complex with positive, negative or neutral allosteric ligands. In this study, we focused on the influence of ligand binding and G protein coupling on the membrane–receptor interactions. We show that there is a noticeable interplay between the cell membrane, G proteins, D₂ receptor and its modulators.     

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Conotoxins and structural biology: a prospective paradigm for drug discovery

Understanding the interactions between activating or antagonizing ligands and their cognate receptors at a molecular level offers promise for the development of pharmacological therapeutics for CNS disorders. The discovery of novel molecules that are capable of discriminating between the varied molecular subunits or isoforms of ion channels should provide a more detailed understanding of the pathophysiology of many CNS disorders. Abundant natural sources of pharmacologically active agents that demonstrate this refined selectivity and specificity are found in the animal toxins of venomous species including: snakes, spiders and the marine snail of the genus Conus. The uniquely fascinating combinatorial ability of the marine snail, genus Conus to modify the pharmacological properties of these neurotoxins or conopeptides within its venom is depicted throughout this review. The myriad of posttranslational modifications and disulfide bonded architectures that have been identified in the conopeptides, are described with an emphasis on the unique pharmacological properties and receptor target specificities that have been ascribed to each of these modifications. The ability of NMR spectroscopy to provide three-dimensional structural information within the interaction interface for both the ligand and target protein following complex formation and its application to conopeptide drug discovery are discussed. Similarly, the strength of merging NMR spectroscopy data with ab initio "restrained soft-docking" for rational pharmacophore design and the identification of lead compounds from in silico library screens will also be discussed. The initial phases of this stratagem are illustrated using two toxin antagonists and the recently determined structure of the KcsA potassium channel. These data exemplify the utility of this approach in elucidating important molecular interfaces of specific toxin-receptor/ion channel complexes, which can be further exploited in drug discovery initiatives.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors

G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor’s three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of ∼300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

Application of affinity selection-mass spectrometry assays to purification and affinity-based screening of the chemokine receptor CXCR4

Affinity selection-mass spectrometry (AS-MS) is a sensitive technology for identifying small molecules that bind to target proteins, and assays enabled by AS-MS can be used to delineate relative binding affinities of ligands for proteins. 'Indirect' AS-MS assays employ size-exclusion techniques to separate target-ligand complexes from unbound ligands, and target-associated ligands are then specifically detected by liquid chromatography mass spectrometry. We report how indirect AS-MS binding assays with known reference control compounds were used as guideposts for development of an optimized purification method for CXCR4, a G-protein coupled chemokine receptor, for which we sought novel antagonists. The CXCR4 purification method that was developed was amenable to scale-up and enabled the screening of purified recombinant human CXCR4 against a large combinatorial library of small molecules by high throughput indirect AS-MS. The screen resulted in the discovery of new ligands that competed off binding of reference compounds to CXCR4 in AS-MS binding assays and that antagonized SDF1α-triggered responses and CXCR4-mediated HIV1 viral uptake in cell-based assays. This report provides a methodological paradigm whereby indirect AS-MS-based ligand binding assays may be used to guide optimal integral membrane protein purification methods that enable downstream affinity selection-based applications such as high throughput AS-MS screens.     

Amplification of bifunctional ligands for calmodulin from a dynamic combinatorial library

A well known strategy to prepare high affinity ligands for a biological receptor is to link together low affinity ligands. DCC (dynamic combinatorial chemistry) was used to select bifunctional protein ligands with high affinity relative to the corresponding monofunctional ligands. Thiol to disulfide linkage generated a small dynamic library of bifunctional ligands in the presence of calmodulin, a protein with two independently mobile domains. The binding constant of the bifunctional ligand (disulfide) most amplified by the presence of calmodulin is nearly two orders of magnitude higher than that of the corresponding monofunctional ligand (thiol).     

Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on‐ and off‐target binding. The approach translates the nature‐inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure–activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype‐selective and multitarget‐modulating dopamine D₄ antagonists, as well as ligands selective for the sigma‐1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand‐based computer‐aided molecular design method may guide target‐focused combinatorial chemistry.     

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.     

In Search of Synergistic Insect Repellents: Modeling of Muscarinic GPCR Interactions with Classical and Bitopic Photoactive Ligands

Insect vector-borne diseases pose serious health problems, so there is a high demand for efficient molecules that could reduce transmission. Using molecular docking and molecular dynamics (MD) simulation, we studied a series of compounds acting on human and insect muscarinic acetylcholine receptors (mAChRs), a novel target of synergistic agents in pest control. We characterized early conformational changes of human M1 and fruit fly type-A mAChR G protein-coupled receptors (GPCRs) in response to DEET, IR3535, and muscarine binding based on the MD analysis of the activation microswitches known to form the signal transduction pathway in class A GPCRs. We indicated groups of microswitches that are the most affected by the presence of a ligand. Moreover, to increase selectivity towards insects, we proposed a new, bitopic, photoswitchable mAChR ligand—BQCA-azo-IR353 and studied its interactions with both receptors. Modeling data showed that using a bitopic ligand may be a promising strategy in the search for better insect control.