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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.

Item does not contain fulltext The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.

Contains fulltext : 103510.pdf (Publisher’s version ) (Closed access) 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 beta-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.

Here, we report the identification and optimization of 1-(4-(pyridin-2-yl)benzyl)imidazolidine-2,4-dione derivatives as a novel chemotype with selective cannabinoid CB2 receptor agonist activity. 1 is a potent and selective cannabinoid CB2 receptor agonist (hCB2 pEC(50) = 8.6). The compound was found to be metabolically unstable, which resulted in low oral bioavailability in rat (F(po) = 4%) and possessed off-target activity at the hERG ion channel (pK(i) = 5.5). Systematic modification of physicochemical properties, such as lipophilicity and basicity, was used to optimize the pharmacokinetic profile and hERG affinity of this novel class of cannabinoid CB2 receptor agonists. This led to the identification of 44 as a potent, selective, and orally bioavailable cannabinoid CB2 receptor agonist (hCB2 pEC(50) = 8.0; hERG pK(i)4; F(po) = 100%), which was active in a rat spinal nerve ligation model of neuropathic pain.

Olfactomedin-4 (OLFM-4) is an extracellular matrix protein that is highly expressed in human endometrium. We have examined the regulation and function of OLFM-4 in normal endometrium and in cases of endometriosis and endometrial cancer. OLFM-4 expression levels are highest in proliferative-phase endometrium, and 17 beta-estradiol up-regulates OLFM-4 mRNA in endometrial explant cultures. Using the luciferase reporter under control of the OLFM-4 promoter, it was shown that both 17 beta-estradiol and OH-tamoxifen induce luciferase activity, and epidermal growth factor receptor-1 is required for this estrogenic response. In turn, EGF activates the OLFM-4 promoter, and estrogen receptor-alpha is needed for the complete EGF response. The cellular functions of OLFM-4 were examined by its expression in OLFM-4-negative HEK-293 cells, which resulted in decreased vimentin expression and cell adherence as well as increased apoptosis resistance. In cases of endometriosis and endometrial cancer, OLFM-4 expression correlated with the presence of epidermal growth factor receptor-1 and estrogen receptor-alpha (or estrogen signaling). An increase of OLFM-4 mRNA was observed in the endometrium of endometriosis patients. No change in OLFM-4 expression levels were observed in patients with endometrial cancer relative with controts. In conclusion, cross-talk between estrogen and EGF signaling regulates OLFM-4 expression. The role of OLFM-4 in endometrial tissue remodeling before the secretory phase and during the predisposition and early events in endometriosis can be postulated but requires additional investigation. (Am J Pathol 2010, 177:2495-2508: DOI: 10.2353/ajpath.2010.100026

Background G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs. Results Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method. Conclusions The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.