Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4

Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

Fragment-based lead discovery on G-protein-coupled receptors

INTRODUCTION

G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets.

AREAS COVERED

This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress.

EXPERT OPINION

With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project.

X-ray structural information of GPCRs in drug design: what are the limitations and where do we go?

INTRODUCTION

In 2007, the X-ray structural determination of non-rhodopsin G-Protein coupled receptors (GPCRs), considered the most extensively targeted protein class for marketed drugs, commenced. With the relatively rapid availability of additional structures, an assessment of the progression made is needed in addition to the assessment of the understandings gleaned, deployment successes and forthcoming prospects.

AREAS COVERED

The author reviews the approaches and tools that have made it possible to determine the three dimensional structures of GPCRs using X-ray crystallography. Furthermore, the author describes the methods suited for crystallization of membrane bound GPCR proteins including the lipidic cubic phase and various protein modification approaches. The author also provides highlights, from the literature, of the structures determined to date including targets solved, the nature of the content provided (such as selectivity, activating vs. inactivating determinants) and how these structural features relate to drug design strategies.

EXPERT OPINION

The GPCR X-ray structures that have been so far determined have yielded significant information. This has presented dramatic evidence concerning their ability to impact the discovery of compounds through their action as traditional, orthosteric modulators. It is, however, noted that more challenging design strategies, such as identifying biased agonists and the use of sites remote from the orthosteric site for allosteric modulation, are still in their infancy.

The impact of introducing a histidine into an apolar cavity site on docking and ligand recognition

Simplified model binding sites allow one to isolate entangled terms in molecular energy functions. Here, we investigate the effects on ligand recognition of the introduction of a histidine into a hydrophobic cavity in lysozyme. We docked 656040 molecules and tested 26 highly and nine poorly ranked. Twenty-one highly ranked molecules bound and five were false positives, while three poorly ranked molecules were false negatives. In the 16 X-ray complexes now known, the docking predictions overlaid well with the crystallographic results. Although ligand enrichment was high, the false negatives, the false positives, and the inability to rank order illuminated weaknesses in our scoring, particularly overweighed apolar and underweighted polar terms. Adjusting these led to new problems, reflecting the entangled nature of docking scoring functions. Changes in ligand affinity relative to other lysozyme cavities speak to the subtleties of molecular recognition even in these simple sites and to their relevance for testing different models of recognition.

Rational design of small-molecule inhibitors for β-catenin/T-cell factor protein-protein interactions by bioisostere replacement

A new hot spot-based design strategy using bioisostere replacement is reported to rationally design nonpeptidic small-molecule inhibitors for protein-protein interactions. This method is applied to design new potent inhibitors for β-catenin/T-cell factor (Tcf) interactions. Three hot spot regions of Tcf for binding to β-catenin were quantitatively evaluated; the key binding elements around K435 and K508 of β-catenin were derived; a bioisostere library was used to generate new fragments that can match the proposed critical binding elements. The most potent inhibitor, with a molecular weight of 230, has a Kd of 0.531 μM for binding to β-catenin and a Ki of 3.14 μM to completely disrupt β-catenin/Tcf interactions. The binding mode of the designed inhibitors was validated by the site-directed mutagenesis and structure-activity relationship (SAR) studies. This study provides a new approach to design new small-molecule inhibitors that bind to β-catenin and effectively disrupt β-catenin/Tcf interactions specific for canonical Wnt signaling.

Colloidal aggregation causes inhibition of G protein-coupled receptors

Colloidal aggregation is the dominant mechanism for artifactual inhibition of soluble proteins, and controls against it are now widely deployed. Conversely, investigating this mechanism for membrane-bound receptors has proven difficult. Here we investigate the activity of four well-characterized aggregators against three G protein-coupled receptors (GPCRs) recognizing peptide and protein ligands. Each of the aggregators was active at micromolar concentrations against the three GPCRs in cell-based assays. This activity could be attenuated by either centrifugation of the inhibitor stock solution or by addition of Tween-80 detergent. In the absence of agonist, the aggregators acted as inverse agonists, consistent with a direct receptor interaction. Meanwhile, several literature GPCR ligands that resemble aggregators themselves formed colloids, by both physical and enzymological tests. These observations suggest that some GPCRs may be artifactually antagonized by colloidal aggregates, an effect that merits the attention of investigators in this field.

A Strategy Combining Differential Low-Throughput Screening and Virtual Screening (DLS-VS) Accelerating the Discovery of new Modulators for the Orphan GPR34 Receptor

The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators specific for the considered receptor among a set of receptors, through the screening of a small library with diverse chemical compounds. Then, an active molecular model of the receptor is built by homology to a validated template, and it is progressively refined by rotamers modification for key side-chains, by VS of the already screened library, and by iterative selection of the model generating the best enrichment. The refined active model is finally used for the VS of a large chemical library and the selection of a small set of compounds for experimental testing. Applied to the orphan receptor GPR34, the DLS-VS strategy combined the experimental screening of 20 000 compounds and the virtual screening of 1 250 000 compounds. It identified one agonist and eight inverse agonists, showing a high chemical diversity. We describe the method. The strategy can be applied to other GPCRs.

Assignment of pterin deaminase activity to an enzyme of unknown function guided by homology modeling and docking

Of the over 22 million protein sequences in the nonredundant TrEMBL database, fewer than 1% have experimentally confirmed functions. Structure-based methods have been used to predict enzyme activities from experimentally determined structures; however, for the vast majority of proteins, no such structures are available. Here, homology models of a functionally uncharacterized amidohydrolase from Agrobacterium radiobacter K84 (Arad3529) were computed on the basis of a remote template structure. The protein backbone of two loops near the active site was remodeled, resulting in four distinct active site conformations. Substrates of Arad3529 were predicted by docking of 57,672 high-energy intermediate (HEI) forms of 6440 metabolites against these four homology models. On the basis of docking ranks and geometries, a set of modified pterins were suggested as candidate substrates for Arad3529. The predictions were tested by enzymology experiments, and Arad3529 deaminated many pterin metabolites (substrate, k(cat)/K(m) [M(-1) s(-1)]): formylpterin, 5.2 × 10(6); pterin-6-carboxylate, 4.0 × 10(6); pterin-7-carboxylate, 3.7 × 10(6); pterin, 3.3 × 10(6); hydroxymethylpterin, 1.2 × 10(6); biopterin, 1.0 × 10(6); d-(+)-neopterin, 3.1 × 10(5); isoxanthopterin, 2.8 × 10(5); sepiapterin, 1.3 × 10(5); folate, 1.3 × 10(5), xanthopterin, 1.17 × 10(5); and 7,8-dihydrohydroxymethylpterin, 3.3 × 10(4). While pterin is a ubiquitous oxidative product of folate degradation, genomic analysis suggests that the first step of an undescribed pterin degradation pathway is catalyzed by Arad3529. Homology model-based virtual screening, especially with modeling of protein backbone flexibility, may be broadly useful for enzyme function annotation and discovering new pathways and drug targets.

The GPCR Network: a large-scale collaboration to determine human GPCR structure and function

G protein-coupled receptors (GPCRs) are targeted by ∼30-40% of marketed drugs, and their key roles in normal physiology and in disease demonstrate that an understanding of their structure and function is valuable to researchers in both basic science and drug discovery. However, until recently, detailed structural information on this protein family was limited by challenges in X-ray crystallographic analysis of such membrane proteins. The GPCR Network was created in 2010 with the goal of structurally characterizing 15-25 representative human GPCRs within 5 years, based on an active outreach programme addressing an interdisciplinary community of scientists interested in GPCR structure, chemistry and biology. Here, we provide an overview of how this collaborative effort has enabled the structural determination and characterization of eight human GPCRs so far, and discuss some of the challenges that remain in gaining more detailed insights into structure-function relationships in this receptor superfamily.

Pocket-based drug design: exploring pocket space

The identification and application of druggable pockets of targets play a key role in in silico drug design, which is a fundamental step in structure-based drug design. Herein, some recent progresses and developments of the computational analysis of pockets have been covered. Also, the pockets at the protein-protein interfaces (PPI) have been considered to further explore the pocket space for drug discovery. We have presented two case studies targeting the kinetic pockets generated by normal mode analysis and molecular dynamics method, respectively, in which we focus upon incorporating the pocket flexibility into the two-dimensional virtual screening with both affinity and specificity. We applied the specificity and affinity (SPA) score to quantitatively estimate affinity and evaluate specificity using the intrinsic specificity ratio (ISR) as a quantitative criterion. In one of two cases, we also included some applications of pockets located at the dimer interfaces to emphasize the role of PPI in drug discovery. This review will attempt to summarize the current status of this pocket issue and will present some prospective avenues of further inquiry.

Limits of ligand selectivity from docking to models: in silico screening for A(1) adenosine receptor antagonists

G protein-coupled receptors (GPCRs) are attractive targets for pharmaceutical research. With the recent determination of several GPCR X-ray structures, the applicability of structure-based computational methods for ligand identification, such as docking, has increased. Yet, as only about 1% of GPCRs have a known structure, receptor homology modeling remains necessary. In order to investigate the usability of homology models and the inherent selectivity of a particular model in relation to close homologs, we constructed multiple homology models for the A(1) adenosine receptor (A(1)AR) and docked ∼2.2 M lead-like compounds. High-ranking molecules were tested on the A(1)AR as well as the close homologs A(2A)AR and A(3)AR. While the screen yielded numerous potent and novel ligands (hit rate 21% and highest affinity of 400 nM), it delivered few selective compounds. Moreover, most compounds appeared in the top ranks of only one model. These findings have implications for future screens.

Structure-function of the G protein-coupled receptor superfamily

During the past few years, crystallography of G protein-coupled receptors (GPCRs) has experienced exponential growth, resulting in the determination of the structures of 16 distinct receptors-9 of them in 2012 alone. Including closely related subtype homology models, this coverage amounts to approximately 12% of the human GPCR superfamily. The adrenergic, rhodopsin, and adenosine receptor systems are also described by agonist-bound active-state structures, including a structure of the receptor-G protein complex for the β(2)-adrenergic receptor. Biochemical and biophysical techniques, such as nuclear magnetic resonance and hydrogen-deuterium exchange coupled with mass spectrometry, are providing complementary insights into ligand-dependent dynamic equilibrium between different functional states. Additional details revealed by high-resolution structures illustrate the receptors as allosteric machines that are controlled not only by ligands but also by ions, lipids, cholesterol, and water. This wealth of data is helping redefine our knowledge of how GPCRs recognize such a diverse array of ligands and how they transmit signals 30 angstroms across the cell membrane; it also is shedding light on a structural basis of GPCR allosteric modulation and biased signaling.

Ligand-receptor interaction platforms and their applications for drug discovery

INTRODUCTION

The study of drug-target interactions is essential for the understanding of biological processes and for the efforts to develop new therapeutic molecules. Increased ligand-binding assays have coincided with the advances in reagents, detection and instrumentation technologies, the expansion in therapeutic targets of interest, and the increasingly recognized importance of biochemical aspects of drug-target interactions in determining the clinical performance of drug molecules. Nowadays, ligand-binding assays can determine every aspect of many drug-target interactions.

AREAS COVERED

Given that ligand-target interactions are very diverse, the author has decided to focus on the binding of small molecules to protein targets. This article first reviews the key biochemical aspects of drug-target interactions, and then discusses the detection principles of various ligand-binding techniques in the context of their primary applications for drug discovery and development.

EXPERT OPINION

Equilibrium-binding affinity should not be used as a solo indicator for the in vivo pharmacology of drugs. The clinical relevance of drug-binding kinetics demands high throughput kinetics early in drug discovery. The dependence of ligand binding and function on the conformation of targets necessitates solution-based and whole cell-based ligand-binding assays. The increasing need to examine ligand binding at the proteome level, driven by the clinical importance of the polypharmacology of ligands, has started to make the structure-based in silico binding screen an indispensable technique for drug discovery and development. Integration of different ligand-binding assays is important to improve the efficiency of the drug discovery and development process.

Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking

A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target’s ligands by their Bemis–Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.