Matching Cavities in G Protein-Coupled Receptors to Infer Ligand-Binding Sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of <i>any</i> ligand for <i>any</i> GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors

Matching Cavities in G Protein-Coupled Receptors to Infer Ligand-Binding Sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of <i>any</i> ligand for <i>any</i> GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors

Matching Cavities in G Protein-Coupled Receptors to Infer Ligand-Binding Sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of <i>any</i> ligand for <i>any</i> GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors

Chemically Diverse Helix-Constrained Peptides Using Selenocysteine Crosslinking

The use of selenocysteines and various cross-linkers to induce helicity in a bioactive peptide is described. The higher reactivity of selenocysteine, relative to cysteine, facilitates rapid cross-linking within unprotected linear peptides under mild aqueous conditions. Alkylating agents of variable topology and electrophilicity were used to link pairs of selenocysteines within a p53 peptide. Facile selenoether formation enables diverse tailoring of the helical peptide structure

Transcriptional regulation¹ of HDAC and sirtuin enzymes by PAR2 activation via either 2f-LIGRLO-NH₂ or Trypsin.

<p><i>¹Expression level expressed as fold differences of control (control  = 1.0)</i>.</p><p><i>²Peptide: 2f-LIGRLO-NH₂ 1 µM</i>.</p><p><i>³Trypsin: Human Trypsin 50 nM</i>.</p

Microarray analysis of PAR2 induced gene expression.

<p>HEK293 cells were treated with either trypsin (50 nM) or 2f-LIGRLO-NH₂ (1 µM). Total RNA was harvested at different time points after treatment (Trypsin, 6 h; Agonist, 1.5, 3, 6, 12 h). Control and treated cDNAs were hybridized to the ∼19,000 cDNA microchip and results were quantified, normalized and filtered as outlined in experimental procedures. (A) Heatmap (red, up-regulated; green, down-regulated) featuring 5 gene clusters. Three clusters show similar modulation by PAR2 peptide agonist and trypsin (1, 2, 3). Cluster 1 shows genes up-regulated by PAR2 activation, clusters 2 and 3 show genes suppressed by PAR2. Clusters 4 and 5 show opposing effects resulting from PAR2 peptide agonist versus trypsin. (B) Scatter plot of genes regulated by trypsin and PAR2 agonist peptide after 6 h treatment. Dots in right hand top quadrant are genes up-regulated by both trypsin and peptide, those in bottom left hand quadrant are genes down-regulated by both treatments. Blue lines depict regions where gene expression increased or decreased by≤3 fold.</p

Genes regulated by both PAR1 and PAR2 activation.

<p><i>¹Expression level expressed as fold differences of control (control  = 1.0).</i></p><p><i>²PAR1 results extracted from McLaughlin et.al. (2005) J.Biol.Chem.280:22172-22180.</i></p><p><i>³PAR2 2f-O: 2f-LIGRLO-NH₂ 1 µM.</i></p><p><i>⁴Trypsin: Human Trypsin 50 nM.</i></p

Effects of PAR2 activation on complement pathways.

<p>Relationship between genes regulated by PAR2 (black arrows) and classical, alternative and lectin pathways of complement activation. Five key genes regulated by PAR2 activation were C1q, C1r, C4b, factor D and CD55 (decay accelerating factor). C1r/q subunits of C1 are in the classical pathway; C4b is essential for formation of C3 convertase in the classical and lectin pathways; Factor D is similarly important for C3 convertase in the alternative pathway; CD55 actively breaks down C3 convertase contributing to anti-complement effects of PAR2. Such regulation of all these genes will limit formation of pro-inflammatory anaphylatoxins (C3a, C5a) and membrane attack complex.</p

Correlation between microarray gene expression and qRT-PCR.

<p>HEK293 cells were treated with trypsin or 2f-LIGRLO-NH₂ and total RNA was extracted 6 h later. Data represent the average of 3 independent experiments. Columns (negative control, white; 2f-LIGRLO-NH₂, grey; trypsin, black) show representative regulation of PAR2, DUSP6, WWOX, ITGB4, p73, Amphiregulin, SERPINB2, Il-8 precursor, ERK-1, COX-2, TNF-9, TNF-15, HDAC-7A, EGR-1 and EGR-2 by PAR2 activation. Results from qRT-PCR were compared to microarray data at equal times.</p

Cell cycle genes down-regulated¹ through PAR2 activation by either 2f-LIGRLO-NH₂ or trypsin.

<p><i>¹Expression level expressed as fold differences of control (control  = 1.0)</i>.</p><p><i>²2f-O: 2f-LIGRLO-NH₂ 1 µM</i> .</p><p><i>³Trypsin: Human Trypsin 50 nM</i>.</p