The study of antagonist-binding domains of the human V1a vasopressin receptor was performed using a radioiodinated photoreactive peptide antagonist. This ligand displayed a high affinity for the receptor expressed in Chinese hamster ovary cell membranes, and specifically labeled two protein bands with apparent molecular mass at 85-90 and 46 kDa. Our results clearly show that the V1a receptor is degraded during incubation with the ligand and that the 46-kDa species is probably the result of the 85-90-kDa species proteolytic cleavage. Truncation of the receptor was then confirmed by deglycosylation with N-glycosidase F. A monoclonal antibody directed against a c-Myc epitope added at the receptor NH 2 terminus allowed immunoprecipitation of the 85-90-kDa photolabeled species. The 46-kDa photolabeled protein never immunoprecipitated, indicating that the truncated form of the receptor lacks the NH 2 terminus region. To localize photolabeled domains of the receptor, the 46-kDa protein was cleaved with V8 and/or Lys-C endoproteinases. The identity of the smallest photolabeled fragment, observed at approximately 6 kDa, was then confirmed by mutation of the potential V8 cleavage sites. Our results indicate that covalent labeling of the vasopressin V1a receptor with the photoreactive antagonist occurs in a region including transmembrane domain VII (residues Asn 327 -Lys 370 ).Neurohypophysial hormones, arginine-vasopressin (AVP) 1 and oxytocin, exert a wide range of physiological effects through binding to specific membrane receptors belonging to the G protein-coupled receptor (GPCR) superfamily. To date, three AVP receptor subtypes and one oxytocin receptor have been pharmacologically and functionally described (1). V1a, V1b, and oxytocin receptors activate phospholipase C, resulting in the production of inositol 1,4,5-trisphosphate and diacylglycerol, mobilization of intracellular calcium and activation of protein kinase C. V2 receptors stimulate adenylyl cyclase, resulting in the accumulation of cyclic AMP and activation of protein kinase A. All receptor subtypes from several mammalian species have been recently cloned (2-5), as well as closely related receptors from bony fishes and invertebrates (6, 7).Analysis of the primary sequence of these receptors suggests that they possess the same general architecture with seven transmembrane helices as other well characterized G proteincoupled receptors. Moreover, the comparison of their amino acid sequence reveals significant homology within the putative transmembrane regions (TM) and within the first and second extracellular loops as well. The natural ligands for the receptors of the AVP/oxytocin family are also closely structurally related. All are nonapeptides composed of a 6-amino acid disulfide-linked ring and a COOH terminus tripeptide.Peptides of the AVP/oxytocin series were subjected to an extensive analysis of structure-activity relationships. These studies (for review, see Refs. 8 -11) led to the production of a profusion of valuable pharmacological probes for asse...

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We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.

Despite their opposite effects on signal transduction, the nonapeptide hormone arginine-vasopressin (AVP) and its V 1a receptor-selective cyclic peptide antagonist d(CH 2 ) 5 [Tyr(Me)2]AVP display homologous primary structures, differing only at residues 1 and 2. These structural similarities led us to hypothesize that both ligands could interact with the same binding pocket in the V 1a receptor. To determine receptor residues responsible for discriminating binding of agonist and antagonist ligands, we performed site-directed mutagenesis of conserved aromatic and hydrophilic residues as well as nonconserved residues, all located in the transmembrane binding pocket of the V 1a receptor. Mutation of aromatic residues of transmembrane region VI (W304, F307, F308) reduced affinity for the d(CH 2 ) 5 [Tyr(Me)2]AVP and markedly decreased affinity for the unrelated strongly hydrophobic V 1a -selective nonpeptide antagonist SR 49059. Replacement of these aromatic residues had no effect on AVP binding, but increased AVP-induced coupling efficacy of the receptor for its G protein. Mutating hydrophilic residues Q108, K128 and Q185 in transmembrane regions II, III and IV, respectively, led to a decrease in affinity for both agonists and antagonists. Finally, the nonconserved residues T333 and A334 in transmembrane region VII, controlled the V 1a /V 2 binding selectivity for both nonpeptide and cyclic peptide antagonists. Thus, because conserved aromatic residues of the V 1a receptor binding pocket seem essential for antagonists and do not contribute at all to the binding of agonists, we propose that these residues differentiate agonist vs. antagonist ligand binding.Keywords: vasopressin receptors; antagonist-binding sites; signal transduction; site-directed mutagenesis; three-dimensional model. G protein-coupled receptors (GPCR) constitute an extremely important target in medicinal chemistry. Thus, there is considerable interest in the docking of the natural ligands and their analogues to these receptors, both for rational drug design and for a better understanding of their functional architecture. To date, the structures of binding sites of peptide hormones have been elucidated primarily by a combination of molecular modeling hypotheses and validation by site-directed mutagenesis analysis (reviewed in [1±3]). Such an approach was applied to the receptors specific for the mammalian hormones arginine-vasopressin (AVP) and oxytocin (OT), which constitute typical GPCR. Indeed, a detailed 3D model of bound AVP to the rat V 1a receptor has been developed and then experimentally verified [4]. Interestingly, AVP binds to its receptor in a hydrophobic pocket of 15±20 A Ê defined by the transmembrane regions (TMs), in a position similar to that of the cationic neurotransmitters [5]. The residues responsible for the binding of AVP are highly conserved and the agonist-binding site was proposed to be common through the different AVP/OT receptor subtypes. Molecular modeling of the V 2 receptor, docking of the hormone and peptide structu...

In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.