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Immunoprecipitation of opioid receptor-Go-protein complexes using specific GTP-binding-protein antisera.

ABSTRACT: Solubilization of opioid receptors from rat cortical membranes that retained high-affinity guanine nucleotide-sensitive agonist binding was achieved using 10 mM CHAPS. We report the nature of the interactions of mu and delta opioid receptors with the guanine nucleotide-binding protein G(o) by immunoprecipitation of CHAPS extracts with selective G(o)alpha-subunit protein antisera. Antiserum IM1 raised against amino acids 22-35 of G(o)alpha selectively co-immunoprecipitated G(o)alpha-mu and G(o)alpha-delta opioid receptor complexes detected in the immunoprecipitates by specific [3H][D-Ala2,N-Me-Phe4,Gly5-ol]enkephalin and [3H][D-Ser2,Leu5,Thr6]enkephalin binding respectively. By contrast, antisera directed against the C-terminal decapeptide (OC2) and the N-terminal hexadecapeptide (ON1) of isoforms of G(o)alpha were unable to immunoprecipitate solubilized opioid receptor-G(o) complexes, although both were able to immunoprecipitate solubilized G(o)alpha and have been shown to reduce the affinity of [D-Ala2,D-Leu5]enkephalin for opioid receptors in rat cortical membranes [Georgoussi, Carr and Milligan (1993) Mol. Pharmacol. 44, 62-69]. These findings demonstrate that CHAPS-solubilized mu and delta opioid receptors from rat cortical membranes form stable complexes with one or more variants of G(o).

SUBMITTER: Georgoussi Z

PROVIDER: S-EPMC1136483 | biostudies-other | 1995 Feb

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:Identifying protein complexes from protein-protein interaction (PPI) network is one of the most important tasks in proteomics. Existing computational methods try to incorporate a variety of biological evidences to enhance the quality of predicted complexes. However, it is still a challenge to integrate different types of biological information into the complexes discovery process under a unified framework. Recently, attributed network embedding methods have be proved to be remarkably effective in generating vector representations for nodes in the network. In the transformed vector space, both the topological proximity and node attributed affinity between different nodes are preserved. Therefore, such attributed network embedding methods provide us a unified framework to integrate various biological evidences into the protein complexes identification process. RESULTS:In this article, we propose a new method called GANE to predict protein complexes based on Gene Ontology (GO) attributed network embedding. Firstly, it learns the vector representation for each protein from a GO attributed PPI network. Based on the pair-wise vector representation similarity, a weighted adjacency matrix is constructed. Secondly, it uses the clique mining method to generate candidate cores. Consequently, seed cores are obtained by ranking candidate cores based on their densities on the weighted adjacency matrix and removing redundant cores. For each seed core, its attachments are the proteins with correlation score that is larger than a given threshold. The combination of a seed core and its attachment proteins is reported as a predicted protein complex by the GANE algorithm. For performance evaluation, we compared GANE with six protein complex identification methods on five yeast PPI networks. Experimental results showes that GANE performs better than the competing algorithms in terms of different evaluation metrics. CONCLUSIONS:GANE provides a framework that integrate many valuable and different biological information into the task of protein complex identification. The protein vector representation learned from our attributed PPI network can also be used in other tasks, such as PPI prediction and disease gene prediction.

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Immunoprecipitation of opioid receptor-Go-protein complexes using specific GTP-binding-protein antisera.

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