Rational design of peptide ligands for the kappa opioid receptor.

Interest in opioid receptors is focused on the development of strong analgesics devoid of abuse potential and adverse side effects. This task, however, cannot be accomplished without understanding the fundamental opioid receptors structure and function as well as the modes of interactions of ligands, and thus potential drugs, with these receptors. Research in our lab has generated data-verified ligand-receptor interaction models for closely related peptide series at the mu and delta types of opioid receptors. Together with mutagenesis and chimera studies, thus allowed the proposal of specific features of the ligands and their receptors that underlie the ligands' selectivity. Following the same approach, deciphering the details of ligand interactions with the third type of opioid receptor, the kappa opioid receptor (KOR), by analysis of a KOR homology model and examination of tetrapeptide and pentapeptide scaffolds for the development of KOR pharmacophore, is the broad scope of this dissertation project. Thus, starting with the homology model of KOR, structure-based ligands were designed based on JOM-13 (Tyr-c[D-Cys-Phe-D-Pen]OH, SS) and JOM-6 (Tyr-c[D-Cys-Phe-D-Pen]NH 2, SEtS) scaffolds. Specifically, cyclic tetrapeptides were synthesized with various changes in polarity, size, lipophilicity or electronic properties of residues in position 3 and 4 to investigate conformational preferences of diverse side chains within KOR binding pocket. Subsequently, cyclic pentapeptides were studied to develop a more flexible scaffold so that the energy difference between ground and bioactive state was smaller. This could potentially improve KOR binding affinity and selectivity. These studies resulted in the development of a tetrapeptide, MP133, with KOR affinity of 38.7 nM and a pentapeptide, MP148, with KOR affinity of 1.6 nM. In addition, we have developed a novel MOR ligand with 16 pM affinity and selectivity of more than 100 times higher for MOR over DOR or KOR. More importantly, we were able to understand the requirements and features of KOR selective ligands. Among them we discovered the importance of aromatic residue in positions 3 in trans conformation, the presence of D-Cys in place of D-Pen in position 4 (in tetrapeptides) or 5 (in pentapeptides), cyclization via disulfide, and a neutral C-terminus.Ph.D.Analytical chemistryHealth and Environmental SciencesPharmaceutical sciencesPharmacy sciencesPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125479/2/3192757.pd

Nutritional state influences Nociceptin/Orphanin FQ peptide receptor expression in the dorsal raphe nucleus.

Agonists of the nociceptin/orphanin FQ (N/OFQ) peptide (NOP) receptor stimulate food intake. Concordantly, neuroanatomical localization of NOP receptor mRNA has revealed it to be highly expressed in brain regions associated with the regulation of energy balance. However, the specific mechanisms and neurochemical pathways through which physiological N/OFQ influences appetite are not well understood. To investigate this, we examined nutritional state-associated changes in NOP receptor mRNA levels throughout the rostrocaudal extent of the rat brain using in situ hybridization histochemistry (ISHH) and quantitative densitometry analysis. We observed a significant downregulation of NOP receptor mRNA in the dorsal raphe nucleus (DRN) of fasted rats compared to free-feeding rats. In contrast, no difference in NOP receptor mRNA expression was observed in the supraoptic, parventricular, ventromedial, arcuate or dorsomedial nuclei of the hypothalamus, the red nucleus, the locus coeruleus or the hypoglossal nucleus in the fasted or fed state. These data suggest that the endogenous N/OFQ system is responsive to changes in energy balance and that NOP receptors specifically within the DRN may be physiologically relevant to N/OFQ's effects on appetite

Formalization, annotation and analysis of diverse drug and probe screening assay datasets using the BioAssay Ontology (BAO).

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens

Formal BAO annotation of a PubChem screening campaign.

<p>In the campaign to identify inhibitors of Kruppel-like factor 5, the investigators screened compounds, both to identify inhibitors of KLF5 and to eliminate cytotoxic compounds. The compounds tested at each stage are shown in red boxes. The endpoints (result type) from each of the assays are listed to the right. Abbreviations: Comp: compounds, opt: optimization, conc: concentration, inh: inhibition, 1×: compound tested in singlicate, 3×: compound tested in triplicate, and CID: compound ID.</p

List of ontologies used in BAO.

<p>List of ontologies used in BAO.</p

Analysis of bio-ontologies to describe chemical biology HTS assays.

<p>Coverage of biomedical concepts/terms to describe HTS assays (shown as rows) by various existing biomedical ontologies (shown as columns) is depicted. The color codes are as follows: green: the concept is well described by the ontology, pink: the concept is partially described, red: no (or little) information is available in the ontology, yellow: the concept is imported from an external reference/ontology, blue: the ontology only includes a placeholder to an external reference of that concept.</p

Network visualization of PubChem assays (nodes) connected by BAO assay relationships (edges) to describe screening campaigns.

<p>Shown are 682 assays that are each part of a set of at least 7 connected assays comprising 85 campaigns. Assays are identified by their AID. BAO assay annotations are also shown, including, assay format (node shape), assay target main class (node color), and BAO assay relationships (edge color). Screening campaign-disease associations were obtained from the assay descriptions (shown as areas surrounded by dotted lines).</p

Summary of the standardized ‘endpoint’ in PubChem assay annotations.

<p>Summary of the standardized ‘endpoint’ in PubChem assay annotations.</p

Generation of a hierarchy-flattened BAO format.

<p>The hierarchy-flattened format of BAO contains only the most specific leaf nodes. Lead node IDs were maintained in this process. The labels/names in the flattened version of BAO reflect the class hierarchy in BAO.</p