Luc Roumen

Pharmacological modulation of chemokine receptor function

G protein‐coupled chemokine receptors and their peptidergic ligands are interesting therapeutic targets due to their involvement in various immune‐related diseases, including rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, chronic obstructive pulmonary disease, HIV‐1 infection and cancer. To tackle these diseases, a lot of effort has been focused on discovery and development of small‐molecule chemokine receptor antagonists. This has been rewarded by the market approval of two novel chemokine receptor inhibitors, AMD3100 (CXCR4) and Maraviroc (CCR5) for stem cell mobilization and treatment of HIV‐1 infection respectively. The recent GPCR crystal structures together with mutagenesis and pharmacological studies have aided in understanding how small‐molecule ligands interact with chemokine receptors. Many of these ligands display behaviour deviating from simple competition and do not interact with the chemokine binding site, providing evidence for an allosteric mode of action. This review aims to give an overview of the evidence supporting modulation of this intriguing receptor family by a range of ligands, including small molecules, peptides and antibodies. Moreover, the computer‐assisted modelling of chemokine receptor–ligand interactions is discussed in view of GPCR crystal structures. Finally, the implications of concepts such as functional selectivity and chemokine receptor dimerization are considered.

From heptahelical bundle to hits from the Haystack: structure-based virtual screening for GPCR ligands.

This review will focus on the construction, refinement, and validation of G-protein-coupled receptor (GPCR) structural models for the purpose of structure-based virtual screening (SBVS) and ligand design. The review will present a comparative analysis of GPCR crystal structures and their implication on GPCR (homology) modeling. The challenges associated with steps along the modeling workflow will be discussed: the use of experimental anchors to steer the modeling procedure, amino acid sequence alignment and template selection, receptor structure refinement, loop modeling, ligand-binding mode prediction, and virtual screening for novel ligands. An overview of several successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family.

Molecular determinants of ligand binding modes in the histamine H4 receptor: Linking ligand-based 3D-QSAR models to in silico guided receptor mutagenesis studies

The histamine H(4) receptor (H(4)R) is a G protein-coupled receptor (GPCR) that plays an important role in inflammation. Similar to the homologous histamine H(3) receptor (H(3)R), two acidic residues in the H(4)R binding pocket, D(3.32) and E(5.46), act as essential hydrogen bond acceptors of positively ionizable hydrogen bond donors in H(4)R ligands. Given the symmetric distribution of these complementary pharmacophore features in H(4)R and its ligands, different alternative ligand binding mode hypotheses have been proposed. The current study focuses on the elucidation of the molecular determinants of H(4)R-ligand binding modes by combining (3D) quantitative structure-activity relationship (QSAR), protein homology modeling, molecular dynamics simulations, and site-directed mutagenesis studies. We have designed and synthesized a series of clobenpropit (N-(4-chlorobenzyl)-S-[3-(4(5)-imidazolyl)propyl]isothiourea) derivatives to investigate H(4)R-ligand interactions and ligand binding orientations. Interestingly, our studies indicate that clobenpropit (2) itself can bind to H(4)R in two distinct binding modes, while the addition of a cyclohexyl group to the clobenpropit isothiourea moiety allows VUF5228 (5) to adopt only one specific binding mode in the H(4)R binding pocket. Our ligand-steered, experimentally supported protein modeling method gives new insights into ligand recognition by H(4)R and can be used as a general approach to elucidate the structure of protein-ligand complexes.

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.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

A pharmacophore describes the arrangement of molecular features a ligand must contain to efficaciously bind a receptor. Pharmacophore models are developed to improve molecular understanding of ligand–protein interactions, and can be used as a tool to identify novel compounds that fulfil the pharmacophore requirements and have a high probability of being biologically active. Protein structure-based pharmacophores (SBPs) derive these molecular features by conversion of protein properties to reciprocal ligand space. Unlike ligand-based pharmacophore models, which require templates of ligands in their bioactive conformation, SBPs do not depend on ligand information. The current review describes the different steps in the construction of SBPs: (i) protein structure preparation, (ii) binding site detection, (iii) pharmacophore feature definition, and (iv) pharmacophore feature selection. We show that the SBP modeling workflow poses different challenges than ligand-based pharmacophore modeling, including the definition of protein pharmacophore features essential for ligand binding. A comprehensive overview of different SBP modeling and screening methods and applications is provided to illustrate that SBPs can be efficiently used for virtual screening, ligand binding mode prediction, and binding site similarity detection. Our review demonstrates that SBPs are valuable tools for hit and lead optimization, compound library design and target hopping, especially in cases where ligand information is scarce.

Identification of a Novel Allosteric Binding Site in the CXCR2 Chemokine Receptor

We have shown previously that different chemical classes of small-molecule antagonists of the human chemokine CXCR2 receptor interact with distinct binding sites of the receptor. Although an intracellular binding site for diarylurea CXCR2 antagonists, such as N-(2-bromophenyl)-N′-(7-cyano-1H-benzotriazol-4-yl)urea (SB265610), and thiazolopyrimidine compounds was recently mapped by mutagenesis studies, we now report on an imidazolylpyrimidine antagonist binding pocket in the transmembrane domain of CXCR2. Using different CXCR2 orthologs, chimeric proteins, site-directed mutagenesis, and in silico modeling, we have elucidated the binding mode of this antagonist. Our in silico-guided mutagenesis studies indicate that the ligand binding cavity for imidazolylpyrimidine compounds in CXCR2 is located between transmembrane (TM) helices 3 (Phe1303.36), 5 (Ser2175.44, Phe2205.47), and 6 (Asn2686.52, Leu2716.55) and suggest that these antagonists enter CXCR2 via the TM5-TM6 interface. It is noteworthy that the same interface is postulated as the ligand entry channel in the opsin receptor and is occupied by lipid molecules in the recently solved crystal structure of the CXCR4 chemokine receptor, suggesting a general ligand entrance mechanism for nonpolar ligands to G protein-coupled receptors. The identification of a novel allosteric binding cavity in the TM domain of CXCR2, in addition to the previously identified intracellular binding site, shows the diversity in ligand recognition mechanisms by this receptor and offers new opportunities for the structure-based design of small allosteric modulators of CXCR2 in the future.

A Prospective Cross-Screening Study on G Protein-Coupled Receptors: Lessons Learned in Virtual Compound Library Design

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.

Identification of Overlapping but Differential Binding Sites for the High-Affinity CXCR3 Antagonists NBI-74330 and VUF11211

CXC chemokine receptor CXCR3 and/or its main three ligands CXCL9, CXCL10, and CXCL11 are highly upregulated in a variety of diseases. As such, considerable efforts have been made to develop small-molecule receptor CXCR3 antagonists, yielding distinct chemical classes of antagonists blocking binding and/or function of CXCR3 chemokines. Although it is suggested that these compounds bind in an allosteric fashion, thus far no evidence has been provided regarding the molecular details of their interaction with CXCR3. Using site-directed mutagenesis complemented with in silico homology modeling, we report the binding modes of two high-affinity CXCR3 antagonists of distinct chemotypes: VUF11211 [(S)-5-chloro-6-(4-(1-(4-chlorobenzyl)piperidin-4-yl)-3-ethylpiperazin-1-yl)-N-ethylnicotinamide] (piperazinyl-piperidine) with a rigid elongated structure containing two basic groups and NBI-74330 [(R)-N-(1-(3-(4-ethoxyphenyl)-4-oxo-3,4-dihydropyrido[2,3-d]pyrimidin-2-yl)ethyl)-2-(4-fluoro-3-(trifluoromethyl)phenyl)-N-(pyridin-3-ylmethyl)acetamide] (8-azaquinazolinone) without any basic group. Here we show that NBI-74330 is anchored in the transmembrane minor pocket lined by helices 2 (W2.60, D2.63), 3 (F3.32), and 7 (S7.39, Y7.43), whereas VUF11211 extends from the minor pocket into the major pocket of the transmembrane domains, located between residues in helices 1 (Y1.39), 2 (W2.60), 3 (F3.32), 4 (D4.60), 6 (Y6.51), and 7 (S7.39, Y7.43). Mutation of these residues did not affect CXCL11 binding significantly, confirming the allosteric nature of the interaction of these small molecules with CXCR3. Moreover, the model derived from our in silico–guided studies fits well with the already published structure–activity relationship data on these ligands. Altogether, in this study, we show overlapping, yet different binding sites for two high-affinity CXCR3 antagonists, which offer new opportunities for the structure-based design of allosteric modulators for CXCR3.

In Silico Veritas: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions

Recently the first community-wide assessments of the prediction of the structures of complexes between proteins and small molecule ligands have been reported in the so called GPCR Dock 2008 and 2010 assessments. In the current review we discuss the different steps along the protein-ligand modeling workflow by critically analyzing the modeling strategies we used to predict the structures of protein-ligand complexes we submitted to the recent GPCR Dock 2010 challenge. These representative test cases, focusing on the pharmaceutically relevant G Protein-Coupled Receptors, are used to demonstrate the strengths and challenges of the different modeling methods. Our analysis indicates that the proper performance of the sequence alignment, introduction of structural adjustments guided by experimental data, and the usage of experimental data to identify protein-ligand interactions are critical steps in the protein-ligand modeling protocol.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

We have explored the possibilities and challenges of structure-based virtual screening (SBVS) against the human histamine H4 receptor (H4R), a key player in inflammatory responses. Several SBVS strategies, employing different H4R ligand conformations, were validated and optimized with respect to their ability to discriminate small fragment-like H4R ligands from true inactive fragments, and compared to ligand-based virtual screening (LBVS) approaches. SBVS studies with a molecular interaction fingerprint (IFP) scoring method enabled the identification of H4R ligands that were not identified in LBVS runs, demonstrating the scaffold hopping potential of combining molecular docking and IFP scoring. Retrospective VS evaluations against H4R homology models based on the histamine H1 receptor (H1R) crystal structure did not give higher enrichments of H4R ligands than H4R models based on the beta-2 adrenergic receptor (β2R). Complementary prospective SBVS studies against β2R-based and H1R-based H4R homology models led to the discovery of different new fragment-like H4R ligand chemotypes. Of the 37 tested compounds, 9 fragments (representing 5 different scaffolds) had affinities between 0.14 and 6.3 μM at the H4R.