Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development.

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes- such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Structural characteristics of lysophosphatidic acid biological targets.

Lysophosphatidic acid (LPA; 1-acyl-3-phosphoglycerol) exerts its biological activity through both extracellular and intracellular targets. Receptor targets include the cell-surface G-protein-coupled receptors LPA(1-4) and the nuclear PPAR-gamma (peroxisome-proliferator-activated receptor gamma). Enzyme targets include the secreted cancer cell motility factor, autotaxin, and the transmembrane phosphatases, LPP1-3 (where LPP stands for lipid phosphate phosphatase). Ion channel targets include the two pore domain ion channels in the TREK family, TREK-1, TREK-2 and TRAAK. Structural features of these targets and their interactions with LPA are reviewed.

HIV-1 integrase inhibition: binding sites, structure activity relationships and future perspectives.

The integrase enzyme encoded by the human immunodeficiency virus plays an integral role in the viral life cycle, but is as yet unexploited as a clinical drug target. Integrase processes the viral DNA in the cytoplasm, translocates to the nucleus, and catalyzes viral DNA insertion into the host genome. A wide variety of chemical structures inhibit integrase in vitro, yet few of these apparently promising compounds have demonstrated similar efficacy in vivo. Multiple binding targets have been identified for different integrase inhibitors. These targets include the integrase enzyme prior to substrate binding, the viral DNA substrate, and the preintegration complex consisting of oligomeric integrase and the viral DNA. Some known inhibitors are effective only in the presence of divalent manganese as the active site metal ion cofactor, whereas others do not discriminate between manganese and magnesium ions. Integrase inhibition in response to ligand binding at one of multiple sites renders derivation of a simple set of structure activity relationships challenging. Progress toward this goal is reviewed in the context of experimental and theoretical structural information about integrase.

A single amino acid determines lysophospholipid specificity of the S1P1 (EDG1) and LPA1 (EDG2) phospholipid growth factor receptors.

The phospholipid growth factors sphingosine-1-phosphate (S1P) and lysophosphatidic acid (LPA) are ligands for the related G protein-coupled receptors S1P(1)/EDG1 and LPA(1)/EDG2, respectively. We have developed a model of LPA(1) that predicts interactions between three polar residues and LPA. One of these, glutamine 125, which is conserved in the LPA receptor subfamily (LPA(1)/EDG2, LPA(2)/EDG4, and LPA(3)/EDG7), hydrogen bonds with the LPA hydroxyl group. Our previous S1P(1) study identified that the corresponding glutamate residue, conserved in all S1P receptors, ion pairs with the S1P ammonium. These two results predict that this residue might influence ligand recognition and specificity. Characterization of glutamate/glutamine interchange point mutants of S1P(1) and LPA(1) validated this prediction as the presence of glutamate was required for S1P recognition, whereas LPA recognition was possible with either glutamine or glutamate. The most likely explanation for this dual specificity behavior is a shift in the equilibrium between the acid and conjugate base forms of glutamic acid due to other amino acids surrounding that position in LPA(1), producing a mixture of receptors including those having an anionic glutamate that recognize S1P and others with a neutral glutamic acid that recognize LPA. Thus, computational modeling of these receptors provided valid information necessary for understanding the molecular pharmacology of these receptors.

Short-chain phosphatidates are subtype-selective antagonists of lysophosphatidic acid receptors.

Lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P) are members of the phospholipid growth factor family. A major limitation in the field to date has been a lack of receptor subtype-specific agonists and antagonists. Here, we report that dioctylglycerol pyrophosphate and dioctylphosphatidic acid are selective antagonists of the LPA(1) and LPA(3) receptors, but prefer LPA(3) by an order of magnitude. Neither molecule had an agonistic or antagonistic effect on LPA(2) receptor. Consistent with this receptor subtype selectivity, dioctylglycerol pyrophosphate inhibited cellular responses to LPA in NIH3T3 fibroblasts, HEY ovarian cancer cells, PC12 pheochromocytoma cells, and Xenopus laevis oocytes. Responses elicited by S1P in these cell lines that endogenously express S1P(1), S1P(2), S1P(3), and S1P(5) receptors were unaffected by dioctylglycerol pyrophosphate. Responses evoked by the G protein-coupled receptor ligands acetylcholine, serotonin, ATP, and thrombin receptor-activating peptide were similarly unaffected, suggesting that the short-chain phosphatidates are receptor subtype-specific lysophosphatidate antagonists.

Molecular models of N-benzyladriamycin-14-valerate (AD 198) in complex with the phorbol ester-binding C1b domain of protein kinase C-delta.

N-Benzyladriamycin-14-valerate (AD 198) is a semisynthetic anthracycline with experimental antitumor activity superior to that of doxorubicin (DOX). AD 198, unlike DOX, only weakly binds DNA, is a poor inhibitor of topoisomerase II, and circumvents anthracycline-resistance mechanisms, suggesting a unique mechanism of action for this novel analogue. The phorbol ester receptors, protein kinase C (PKC) and beta2-chimaerin, were recently identified as selective targets for AD 198 in vitro. In vitro, AD 198 competes with [3H]PDBu for binding to a peptide containing the isolated C1b domain of PKC-delta (deltaC1b domain). In the present study molecular modeling is used to investigate the interaction of AD 198 with the deltaC1b domain. Three models are identified wherein AD 198 binds into the groove formed between amino acid residues 6-13 and 21-27 of the deltaC1b domain in a manner similar to that reported for phorbol-13-acetate and other ligands of the C1 domain. Two of the identified models are consistent with previous experimental data demonstrating the importance of the 14-valerate side chain of AD 198 in binding to the C1 domain as well as current data demonstrating that translocation of PKC-alpha to the membrane requires the 14-valerate substituent. In this regard, the carbonyl of the 14-valerate participates in hydrogen bonding to the deltaC1b while the acyl chain is positioned for stabilization of the membrane-bound protein-ligand complex in a manner analogous to the acyl chains of the phorbol esters. These studies provide a structural basis for the interaction of AD 198 with the deltaC1b domain and a starting point for the rational design of potential new drugs targeting PKC and other proteins with C1 domains.

Identification of Edg1 receptor residues that recognize sphingosine 1-phosphate.

Originating from its DNA sequence, a computational model of the Edg1 receptor has been developed that predicts critical interactions with its ligand, sphingosine 1-phosphate. The basic amino acids Arg(120) and Arg(292) ion pair with the phosphate, whereas the acidic Glu(121) residue ion pairs with the ammonium moiety of sphingosine 1-phosphate. The requirement of these interactions for specific ligand recognition has been confirmed through examination of site-directed mutants by radioligand binding, ligand-induced [(35)S]GTPgammaS binding, and receptor internalization assays. These ion-pairing interactions explain the ligand specificity of the Edg1 receptor and provide insight into ligand specificity differences within the Edg receptor family. This computational map of the ligand binding pocket provides information necessary for understanding the molecular pharmacology of this receptor, thus underlining the potential of the computational method in predicting ligand-receptor interactions.

Computational studies of sialyllactones: methods and uses.

N-Acetylneuraminic acid (1) is a common sugar in many biological recognition processes. Neuraminidase enzymes recognize and cleave terminal sialic acids from cell surfaces. Viral entry into host cells requires neuraminidase activity, thus inhibition of neuraminidase is a useful strategy for development of drugs for viral infections. A recent crystal structure for influenza viral neuraminidase with sialic acid bound shows that the sialic acid is in a boat conformation [Prot Struct Funct Genet 14: 327 (1992)]. Our studies seek to determine if structural pre-organization can be achieved through the use of sialyllactones. Determination of whether siallylactones are pre-organized in a binding conformation requires conformational analysis. Our inability to find a systematic study comparing the results obtained by various computational methods for carbohydrate modeling led us to compare two different conformational analysis techniques, four different force fields, and three different solvent models. The computational models were compared based on their ability to reproduce experimental coupling constants for sialic acid, sialyl-1,4-lactone, and sialyl-1,7-lactone derivatives. This study has shown that the MM3 forcefield using the implicit solvent model for water implemented in Macromodel best reproduces the experimental coupling constants. The low-energy conformations generated by this combination of computational methods are pre-organized toward conformations which fit well into the active site of neuraminidase.