A Virtual Screen for Diverse Ligands: Discovery of Selective G Protein-Coupled Receptor Antagonists

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

1,4-Benzodioxane, an evergreen, versatile scaffold in medicinal chemistry: A review of its recent applications in drug design

1,4-Benzodioxane has long been a versatile template widely employed to design molecules endowed with diverse bioactivities. Its use spans the last decades of medicinal chemistry until today concerning many strategies of drug discovery, not excluding the most advanced ones. Here, more than fifty benzodioxane-related lead compounds, selected from recent literature, are presented showing the different approaches with which they have been developed. Agonists and antagonists at neuronal nicotinic, α₁ adrenergic and serotoninergic receptor subtypes and antitumor and antibacterial agents form the most representative classes, but a variety of other biological targets are addressed by benzodioxane-containing compounds.

Evaluating Chemicals for Thyroid Disruption: Opportunities and Challenges with in Vitro Testing and Adverse Outcome Pathway Approaches

BACKGROUND

Extensive clinical and experimental research documents the potential for chemical disruption of thyroid hormone (TH) signaling through multiple molecular targets. Perturbation of TH signaling can lead to abnormal brain development, cognitive impairments, and other adverse outcomes in humans and wildlife. To increase chemical safety screening efficiency and reduce vertebrate animal testing, in vitro assays that identify chemical interactions with molecular targets of the thyroid system have been developed and implemented.

OBJECTIVES

We present an adverse outcome pathway (AOP) network to link data derived from in vitro assays that measure chemical interactions with thyroid molecular targets to downstream events and adverse outcomes traditionally derived from in vivo testing. We examine the role of new in vitro technologies, in the context of the AOP network, in facilitating consideration of several important regulatory and biological challenges in characterizing chemicals that exert effects through a thyroid mechanism.

DISCUSSION

There is a substantial body of knowledge describing chemical effects on molecular and physiological regulation of TH signaling and associated adverse outcomes. Until recently, few alternative nonanimal assays were available to interrogate chemical effects on TH signaling. With the development of these new tools, screening large libraries of chemicals for interactions with molecular targets of the thyroid is now possible. Measuring early chemical interactions with targets in the thyroid pathway provides a means of linking adverse outcomes, which may be influenced by many biological processes, to a thyroid mechanism. However, the use of in vitro assays beyond chemical screening is complicated by continuing limits in our knowledge of TH signaling in important life stages and tissues, such as during fetal brain development. Nonetheless, the thyroid AOP network provides an ideal tool for defining causal linkages of a chemical exerting thyroid-dependent effects and identifying research needs to quantify these effects in support of regulatory decision making. https://doi.org/10.1289/EHP5297.

Recent Trends and Applications of Molecular Modeling in GPCR⁻Ligand Recognition and Structure-Based Drug Design

G protein-coupled receptors represent the largest family of human membrane proteins and are modulated by a variety of drugs and endogenous ligands. Molecular modeling techniques, especially enhanced sampling methods, have provided significant insight into the mechanism of GPCR–ligand recognition. Notably, the crucial role of the membrane in the ligand-receptor association process has earned much attention. Additionally, docking, together with more accurate free energy calculation methods, is playing an important role in the design of novel compounds targeting GPCRs. Here, we summarize the recent progress in the computational studies focusing on the above issues. In the future, with continuous improvement in both computational hardware and algorithms, molecular modeling would serve as an indispensable tool in a wider scope of the research concerning GPCR–ligand recognition as well as drug design targeting GPCRs.

How do reaction conditions affect the enantiopure synthesis of 2-substituted-1,4-benzodioxane derivatives?

Several biologically active compounds structurally include the enantiopure 2-substituted-1,4-benzodioxane scaffold. The straightforward racemization that affects reactions involving most of the common chemical reactives is thus a crucial issue. The developing of a completely stereo-controlled synthetic route that does not affect the enantiomeric excess is consequently mandatory. It is also important to set up a reliable chiral HPLC method, able to follow the reaction, and to improve the synthetic performances. Here, we report the chiral investigation of two different synthons, we specifically evaluated the synthetic pathways that could be run in order to afford them, avoiding the racemization processes, which could normally occur in basic conditions. In addition, we developed peculiar chiral HPLC methods in order to resolve the enantiomers, define the enantiomeric excess, and fully characterize these compounds.

Discovery of novel frizzled-7 inhibitors by targeting the receptor's transmembrane domain

Frizzled (Fzd) proteins are seven transmembrane receptors that belong to a novel and separated family of G-protein-coupled receptors (GPCRs). The Fzd receptors can respond to Wnt proteins to activate the canonical β-catenin pathway which is important for both initiation and progression of cancers. Disruption of the Wnt/β-catenin signal thus represents an opportunity for rational cancer prevention and therapy. Of the 10 members of the Fzd family, Fzd7 is the most important member involved in cancer development and progression. In the present studies, we applied structure-based virtual screening targeting the transmembrane domain (TMD) of Fzd7 to select compounds that could potentially bind to the Fzd7-TMD and block the Wnt/Fzd7 signaling and further evaluated them in biological assays. Six small molecule compounds were confirmed as Fzd7 inhibitors. The best hit, SRI37892, significantly blocked the Wnt/Fzd7 signaling with IC₅₀ values in the sub-micromolar range and inhibited cancer cell proliferation with IC₅₀ values around 2 μM. Our results provide the first proof of concept of targeting Fzd-TMD for the development of Wnt/Fzd modulators. The identified small molecular Fzd7 inhibitors can serve as a useful tool for studying the regulation mechanism(s) of Wnt/Fzd7 signaling as well as a starting point for the development of cancer therapeutic agents.

A label-free screening approach targeted protease-activated receptor 1 based on dynamic mass redistribution in living cells

Protease-activated receptor 1 (PAR-1) antagonists strongly inhibit thrombin-induced platelet aggregation and are proved to be effective as anti-thrombotic drugs.

Synthesis and biology of ring-modified l-Histidine containing thyrotropin-releasing hormone (TRH) analogues

Thyrotropin-releasing hormone (TRH) analogues bearing halogen groups (Cl, Br and I) at the C-2 and/or C-5 position, and the alkyl group (CH3, C2H5, C3H7, CH2C6H5) at the N-1 position of the imidazole ring of the central histidine residue were synthesized and evaluated for the receptor binding, calcium mobilization (FLIPR), and IP-1 assay at the HEK mTRHR1 and HEK mTRHR2 expressing cell lines. The most promising analogue 7k showed 925-fold selectivity for HEK mTRH-R2 receptor subtype in the IP-1 assay, 272-fold selectivity for HEK mTRH-R2 receptor subtype in the FLIPR assay, and 21-fold receptor binding specificity at HEK TRH-R2 receptor subtype. The peptide 7k was evaluated in vitro in a brain membrane competitive binding assay, and for stability analysis in the presence of TRH-DE, in vivo. The analogue 7k showed decrease in the sleeping time by more than 76% in a pentobarbital-induced sleeping assay, and showed comparatively less elevation in the TSH level in the blood, in vivo. The computational homology modeling of TRH-R1 and TRH-R2 and docking study with the most potent peptide 7k provide impetus to design CNS specific TRH analogues.

Discovery of a low affinity thyrotropin-releasing hormone (TRH)-like peptide that exhibits potent inhibition of scopolamine-induced memory impairment in mice

TRH-like peptides were synthesized in which the critical N-terminus residue L-pGlu was replaced with various heteroaromatic rings, and the central residue histidine with 1-alkyl-L-histidines. All synthesized TRH-like peptides were evaluated in vitro as agonists in HEK mTRH-R1 and HEK mTRH-R2 cell lines, an expressing receptor binding assay (IC50), and cell signaling assay (EC50). The analeptic potential of the synthesized peptides was evaluated in vivo by using the antagonism of a pentobarbital-induced sleeping time. The peptides 6a, 6c and 6e were found to activate TRH-R2 with potencies (EC50) of 0.002 μM, 0.28 μM and 0.049 μM, respectively. In contrast, for signaling activation of TRH-R1, the same peptides required higher concentration of 0.414 μM, 50 μM and 19.1 μM, respectively in the FLIPR assay. The results showed that these peptides were 207, 178 and 389-fold selective towards TRH-R2 receptor subtype. In the antagonism of a pentobarbital-induced sleeping time assay, peptide 6c showed a 58.5% reduction in sleeping time. The peptide 6c exhibited high stability in rat blood plasma, a superior effect on the scopolamine-induced cognition impairment mice model, safe effects on the cardiovascular system, and general behavior using a functional observation battery (FOB).

Advancements in therapeutically targeting orphan GPCRs

G-protein coupled receptors (GPCRs) are popular biological targets for drug discovery and development. To date there are more than 140 orphan GPCRs, i.e., receptors whose endogenous ligands are unknown. Traditionally orphan GPCRs have been difficult to study and the development of therapeutic compounds targeting these receptors has been extremely slow although these GPCRs are considered important targets based on their distribution and behavioral phenotype as revealed by animals lacking the receptor. Recent advances in several methods used to study orphan receptors, including protein crystallography and homology modeling are likely to be useful in the identification of therapeutics targeting these receptors. In the past 13 years, over a dozen different Class A GPCRs have been crystallized; this trend is exciting, since homology modeling of GPCRs has previously been limited by the availability of solved structures. As the number of solved GPCR structures continues to grow so does the number of templates that can be used to generate increasingly accurate models of phylogenetically related orphan GPCRs. The availability of solved structures along with the advances in using multiple templates to build models (in combination with molecular dynamics simulations that reveal structural information not provided by crystallographic data and methods for modeling hard-to-predict flexible loop regions) have improved the quality of GPCR homology models. This, in turn, has improved the success rates of virtual ligand screens that use homology models to identify potential receptor binding compounds. Experimental testing of the predicted hits and validation using traditional GPCR pharmacological approaches can be used to drive ligand-based efforts to probe orphan receptor biology as well as to define the chemotypes and chemical scaffolds important for binding. As a result of these advances, orphan GPCRs are emerging from relative obscurity as a new class of drug targets.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Computational studies to predict or explain G protein coupled receptor polypharmacology

Since G protein-coupled receptors (GPCRs) belong to a very large superfamily of evolutionarily related receptors (>800 members in humans), and due to the rapid progress on their structural biology, they are ideal candidates for polypharmacology studies. Broad screening and bioinformatics/chemoinformatics have been applied to understanding off-target effects of GPCR ligands. It is now feasible to approach the question of GPCR polypharmacology using molecular modeling and the available X-ray GPCR structures. As an example, large and sterically constrained adenosine derivatives (potent adenosine receptor ligands with low conformational freedom and multiple extended substituents) were screened for binding at diverse receptors. Unanticipated off-target interactions, including at biogenic amine receptors, were then modeled using a structure-based approach to provide a consistent understanding of recognition. A conserved Asp in TM3 changed its role from counterion for biogenic amines to characteristic H-bonding to adenosine. The same systematic approach could potentially be applied to many GPCRs or other receptors using other sets of congeneric ligands.

C(X)CR in silico: Computer-aided prediction of chemokine receptor-ligand interactions

This review will focus on the construction, refinement, and validation of chemokine receptor models for the purpose of structure-based virtual screening and ligand design. The review will present a comparative analysis of ligand binding pockets in chemokine receptors, including a review of the recently released CXCR4 X-ray structures, and their implication on chemokine receptor (homology) modeling. The recommended protein-ligand modeling procedure as well as the use of experimental anchors to steer the modeling procedure is discussed and an overview of several successful structure-based ligand discovery and design studies is provided. This review shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for chemokine receptors.:

Ligand pose and orientational sampling in molecular docking

Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×10¹⁰ to 4×10¹⁰ to 1×10¹¹ to 2×10¹¹ to 5×10¹¹ mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

Optimized method of G-protein-coupled receptor homology modeling: its application to the discovery of novel CXCR7 ligands

Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening.

Thyrotropin-releasing hormone receptor type 1 (TRH-R1), not TRH-R2, primarily mediates taltirelin actions in the CNS of mice

Thyrotropin-releasing hormone receptor type 2 (TRH-R2), not TRH-R1, has been proposed to mediate the CNS effects of TRH and its more effective analog taltirelin (TAL). Consistent with this idea, TAL exhibited higher binding affinity and signaling potency at mouse TRH-R2 than TRH-R1 in a model cell system. We used TRH-R1 knockout (R1ko), R2ko and R1/R2ko mice to determine which receptor mediates the CNS effects of TAL. There was no TRH-R1 mRNA in R1ko and R1/R2ko mice and no TRH-R2 mRNA in R2ko and R1/R2ko mice. Specific [(3)H]MeTRH binding to whole brain membranes was 5% of wild type (WT) for R1ko mice, 100% for R2ko mice and 0% for R1/R2ko mice, indicating TRH-R1 is the predominant receptor expressed in the brain. In arousal assays, TAL shortened sleep time with pentobarbital sedation in WT and R2ko mice by 44 and 49% and with ketamine/xylazine sedation by 66 and 55%, but had no effect in R1ko and R1/R2ko mice. In a tail flick assay of nociception, TAL increased response latency by 65 and 70% in WT and R2ko mice, but had no effect in R1ko and R1/R2ko mice. In a tail suspension test of depression-like behavior, TAL increased mobility time by 49 and 37% in WT and R2ko mice, but had no effect in R1ko and R1/R2ko mice. Thus, in contrast to the generally accepted view that the CNS effects of TAL are mediated by TRH-R2, these effects are mediated primarily if not exclusively by TRH-R1 in mice.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Application of Monte Carlo-based receptor ensemble docking to virtual screening for GPCR ligands

Receptor ensemble docking (RED) is an effective strategy to account for receptor flexibility in the course of a docking-based virtual screening campaign. Such an approach can be applied when multiple crystal structures of a receptor have been solved, but it can also be applied when only a single crystal structure is available. In this case, alternative structures can be generated from the latter by computational means and subsequently applied to RED. Here, we illustrate how such conformers can be generated by subjecting a crystal structure to Monte Carlo conformational searches. Through a controlled virtual screening experiment, we then show the applicability of such a strategy to the identification of ligands of the β(2) adrenergic receptor, a G protein-coupled receptor activated by epinephrine. Requiring the availability of one crystal structure only, this strategy is applicable to all systems for which multiple experimentally elucidated structures are not available.

New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors

Methodological advances in X-ray crystallography have made possible the recent solution of X-ray structures of pharmaceutically important G protein-coupled receptors (GPCRs), including receptors for biogenic amines, peptides, a nucleoside, and a sphingolipid. These high-resolution structures have greatly increased our understanding of ligand recognition and receptor activation. Conformational changes associated with activation common to several receptors entail outward movements of the intracellular side of transmembrane helix 6 (TM6) and movements of TM5 toward TM6. Movements associated with specific agonists or receptors have also been described [e.g., extracellular loop (EL) 3 in the A(2A) adenosine receptor]. The binding sites of different receptors partly overlap but differ significantly in ligand orientation, depth, and breadth of contact areas in TM regions and the involvement of the ELs. A current challenge is how to use this structural information for the rational design of novel potent and selective ligands. For example, new chemotypes were discovered as antagonists of various GPCRs by subjecting chemical libraries to in silico docking in the X-ray structures. The vast majority of GPCR structures and their ligand complexes are still unsolved, and no structures are known outside of family A GPCRs. Molecular modeling, informed by supporting information from site-directed mutagenesis and structure-activity relationships, has been validated as a useful tool to extend structural insights to related GPCRs and to analyze docking of other ligands in already crystallized GPCRs.

Drug-drug interaction through molecular structure similarity analysis

BACKGROUND

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

METHODS

We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.

RESULTS

The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.

CONCLUSION

The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

Persistent signaling by thyrotropin-releasing hormone receptors correlates with G-protein and receptor levels

G-protein-coupled receptors with dissociable agonists for thyrotropin, parathyroid hormone, and sphingosine-1-phosphate were found to signal persistently hours after agonist withdrawal. Here we show that mouse thyrotropin-releasing hormone (TRH) receptors, subtypes 2 and 1(TRH-R2 and TRH-R1), can signal persistently in HEK-EM293 cells under appropriate conditions, but TRH-R2 exhibits higher persistent signaling activity. Both receptors couple primarily to Gα(q/11). To gain insight into the mechanism of persistent signaling, we compared proximal steps of inositolmonophosphate (IP1) signaling by TRH-Rs. Persistent signaling was not caused by slower dissociation of TRH from TRH-R2 (t(1/2)=77 ± 8.1 min) compared with TRH-R1 (t(1/2)=82 ± 12 min) and was independent of internalization, as inhibition of internalization did not affect persistent signaling (115% of control), but required continuously activated receptors, as an inverse agonist decreased persistent signaling by 60%. Gα(q/11) knockdown decreased persistent signaling by TRH-R2 by 82%, and overexpression of Gα(q/11) induced persistent signaling in cells expressing TRH-R1. Lastly, persistent signaling was induced in cells expressing high levels of TRH-R1. We suggest that persistent signaling by TRHRs is exhibited when sufficient levels of agonist/receptor/G-protein complexes are established and maintained and that TRH-R2 forms and maintains these complexes more efficiently than TRH-R1.

Structural modelling and dynamics of proteins for insights into drug interactions

Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.

Predicting the biological activities through QSAR analysis and docking-based scoring

Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.

Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.

Ligand discovery from a dopamine D3 receptor homology model and crystal structure

G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no experimental structures are available. The determination of the D(3) receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homology model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.

Understanding functional residues of the cannabinoid CB1

The brain cannabinoid (CB(1)) receptor that mediates numerous physiological processes in response to marijuana and other psychoactive compounds is a G protein coupled receptor (GPCR) and shares common structural features with many rhodopsin class GPCRs. For the rational development of therapeutic agents targeting the CB(1) receptor, understanding of the ligand-specific CB(1) receptor interactions responsible for unique G protein signals is crucial. For a more than a decade, a combination of mutagenesis and computational modeling approaches has been successfully employed to study the ligand-specific CB(1) receptor interactions. In this review, after a brief discussion about recent advances in understanding of some structural and functional features of GPCRs commonly applicable to the CB(1) receptor, the CB(1) receptor functional residues reported from mutational studies are divided into three different types, ligand binding (B), receptor stabilization (S) and receptor activation (A) residues, to delineate the nature of the binding pockets of anandamide, CP55940, WIN55212-2 and SR141716A and to describe the molecular events of the ligand-specific CB(1) receptor activation from ligand binding to G protein signaling. Taken these CB(1) receptor functional residues, some of which are unique to the CB(1) receptor, together with the biophysical knowledge accumulated for the GPCR active state, it is possible to propose the early stages of the CB(1) receptor activation process that not only provide some insights into understanding molecular mechanisms of receptor activation but also are applicable for identifying new therapeutic agents by applying the validated structure-based approaches, such as virtual high throughput screening (HTS) and fragment-based approach (FBA).

The use of GPCR structures in drug design

Structure-based drug discovery is routinely applied to soluble targets such as proteases and kinases. It is only recently that multiple high-resolution X-ray structures of G protein-coupled receptors (GPCRs) have become available. Here we review the technology developments that have led to the recent plethora of GPCR structures. These include developments in protein expression and purification as well as techniques to stabilize receptors and crystallize them. We discuss the findings derived from the new structures with regard to understanding GPCR function and pharmacology. Finally, we examine the utility of structure-based drug discovery approaches including homology modeling, virtual screening, and fragment screening for GPCRs in the context of what has been learnt from other target classes.

Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models

G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.

Unraveling the structure and function of G protein-coupled receptors through NMR spectroscopy

G protein-coupled receptors (GPCRs) are a large superfamily of signaling proteins expressed on the plasma membrane. They are involved in a wide range of physiological processes and, therefore, are exploited as drug targets in a multitude of therapeutic areas. In this extent, knowledge of structural and functional properties of GPCRs may greatly facilitate rational design of modulator compounds. Solution and solid-state nuclear magnetic resonance (NMR) spectroscopy represents a powerful method to gather atomistic insights into protein structure and dynamics. In spite of the difficulties inherent the solution of the structure of membrane proteins through NMR, these methods have been successfully applied, sometimes in combination with molecular modeling, to the determination of the structure of GPCR fragments, the mapping of receptor-ligand interactions, and the study of the conformational changes associated with the activation of the receptors. In this review, we provide a summary of the NMR contributions to the study of the structure and function of GPCRs, also in light of the published crystal structures.

Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor

In this study, we evaluated the applicability of ligand-based and structure-based models to quantitative affinity predictions and virtual screenings for ligands of the beta(2)-adrenergic receptor, a G protein-coupled receptor (GPCR). We also devised and evaluated a number of consensus models obtained through partial least square regressions, to combine the strengths of the individual components. In all cases, the bioactive conformation of each ligand was derived from molecular docking at the crystal structure of the receptor. We identified the most effective models applicable to the different scenarios, in the presence or in the absence of a training set. For ranking the affinity of closely related analogs when a training set is available, a ligand-based consensus model (LI-CM) seems to be the best choice, while the structure-based MM-GBSA score seems the best alternative in the absence of a training set. For virtual screening purposes, the structure-based MM-GBSA score was found to be the method of choice. Consensus models consistently had performances superior or close to those of the best individual components, and were endowed with a significantly increased robustness. Given multiple models with no a priori knowledge of their predictive capabilities, constructing a consensus model ensures results very close to those that the best model alone would have yielded.

Constitutively active thyrotropin and thyrotropin-releasing hormone receptors and their inverse agonists

Receptors for thyrotropin-releasing hormone (TRH) and thyrotropin (thyroid-stimulating hormone-TSH) are important regulators of the function of the TSH-producing cells of the anterior pituitary gland and the thyroid gland, respectively, and thereby play a central role in thyroid hormone homeostasis. Although the roles of TRH- and TSH-stimulated signaling in these endocrine glands are well understood, these receptors are expressed in other sites and their roles in these extraglandular tissues are less well known. Moreover, one of the two subtypes of TRH receptors (TRH-R2) and the single TSH receptor (TSHR) exhibit constitutive signaling activity and the roles of constitutive signaling by these receptors are poorly understood. One approach to studying constitutive signaling is to use inverse agonists. In this chapter, we will describe the experimental procedures used to measure constitutive signaling by TRH-R2 and TSHR and the effects of their specific inverse agonists.

The family of G protein-coupled receptors: an example of membrane proteins

The G protein coupled receptors belong to the largest group of membrane proteins that regulates many essential physiological properties and represents an important class of drug targets. In this chapter, we show how the synergy between a laboratory experiment and computational modeling leads to structural delineation of the ligand binding pocket and how the knowledge of ligand-protein interactions is used for rational local and global drug design in which the structural knowledge of a particular receptor and its ligands is used for drug design of this particular GPCR and others.

Toward the discovery of vaccine adjuvants: coupling in silico screening and in vitro analysis of antagonist binding to human and mouse CCR4 receptors

Background

Adjuvants enhance or modify an immune response that is made to an antigen. An antagonist of the chemokine CCR4 receptor can display adjuvant-like properties by diminishing the ability of CD4+CD25+ regulatory T cells (Tregs) to down-regulate immune responses.

Methodology

Here, we have used protein modelling to create a plausible chemokine receptor model with the aim of using virtual screening to identify potential small molecule chemokine antagonists. A combination of homology modelling and molecular docking was used to create a model of the CCR4 receptor in order to investigate potential lead compounds that display antagonistic properties. Three-dimensional structure-based virtual screening of the CCR4 receptor identified 116 small molecules that were calculated to have a high affinity for the receptor; these were tested experimentally for CCR4 antagonism. Fifteen of these small molecules were shown to inhibit specifically CCR4-mediated cell migration, including that of CCR4⁺ Tregs.

Significance

Our CCR4 antagonists act as adjuvants augmenting human T cell proliferation in an in vitro immune response model and compound SP50 increases T cell and antibody responses in vivo when combined with vaccine antigens of Mycobacterium tuberculosis and Plasmodium yoelii in mice.

The impact of GPCR structures on pharmacology and structure-based drug design

After many years of effort, recent technical breakthroughs have enabled the X-ray crystal structures of three G-protein-coupled receptors (GPCRs) (beta1 and beta2 adrenergic and adenosine A(2a)) to be solved in addition to rhodopsin. GPCRs, like other membrane proteins, have lagged behind soluble drug targets such as kinases and proteases in the number of structures available and the level of understanding of these targets and their interaction with drugs. The availability of increasing numbers of structures of GPCRs is set to greatly increase our understanding of some of the key issues in GPCR biology. In particular, what constitutes the different receptor conformations that are involved in signalling and the molecular changes which occur upon receptor activation. How future GPCR structures might alter our views on areas such as agonist-directed signalling and allosteric regulation as well as dimerization is discussed. Knowledge of crystal structures in complex with small molecules will enable techniques in drug discovery and design, which have previously only been applied to soluble targets, to now be used for GPCR targets. These methods include structure-based drug design, virtual screening and fragment screening. This review considers how these methods have been used to address problems in drug discovery for kinase and protease targets and therefore how such methods are likely to impact GPCR drug discovery in the future.

Docking and chemoinformatic screens for new ligands and targets

Computer-based docking screens are now widely used to discover new ligands for targets of known structure; in the last two years alone, the discovery of ligands for more than 20 proteins has been reported. Recently, investigators have also turned to predicting new substrates for enzymes of unknown function, taking docking in a wholly new direction. Increasingly, the hit rates, the true-positives, and the false-positives from the docking screens are being compared to those from empirical, high-throughput screens, revealing the strengths, weaknesses, and complementarities of both techniques. The recent efflorescence of GPCR structures has made these quintessential drug targets available to structure-based approaches. Consistent with their 'druggability', the docking screens have returned high hit rates and potent molecules. Finally, in the last several years, an approach almost exactly opposite to docking has also appeared; this pharmacological network approach begins not with the structure of the target but rather those of drug molecules and asks, given a pattern of chemistry in the ligands, what targets may a particular drug bind to? This method, which returns to an older, pharmacology logic, has been surprisingly successful in predicting new 'off-targets' for established drugs.

Closing the side-chain gap in protein loop modeling

The success of structure-based drug design relies on accurate protein modeling where one of the key issues is the modeling and refinement of loops. This study takes a critical look at modeled loops, determining the effect of re-sampling side-chains after the loop conformation has been generated. The results are evaluated in terms of backbone and side-chain conformations with respect to the native loop. While models can contain loops with high quality backbone conformations, the side-chain orientations could be poor, and therefore unsuitable for ligand docking and structure-based design. In this study, we report on the ability to model loop side-chains accurately using a variety of commercially available algorithms that include rotamer libraries, systematic torsion scans and knowledge-based methods.