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Cysteine-524 is not the only residue involved in the formation of disulphide-bonded dimers of the insulin receptor.

ABSTRACT: The human insulin receptor (hIR) is a member of the transmembrane tyrosine kinase receptor family. It is a disulphide-linked homodimer which can be reduced to two insulin-binding monomers by mild reduction of class-I disulphide bonds. The number of disulphide bonds between the alpha- and beta-chains within the monomer or between the monomers in the dimer is not known, although one dimer bond involving hIR Cys-524 has recently been identified [Schaffer and Ljungqvist (1992) Biochem. Biophys. Res. Commun. 189, 650-653]. In the present report hIR Cys-524 was converted into alanine by site-directed mutagenesis and expressed at high levels in Chinese hamster ovary (CHO) cells. The mutant receptor was processed normally and shown to bind insulin normally, with ED50 and KD values not different from those of the wild-type hIR. It was still a disulphide-linked dimer as judged by SDS/PAGE, indicating that there are alpha-alpha-chain disulphide bonds additional to the Cys-524 linkage in the insulin receptor dimer. Insulin-stimulated receptor autophosphorylation and kinase activity of the mutated receptor were both impaired compared with that of the wild-type receptor by 49% and 53% respectively. CHO cells overexpressing the mutant receptor, however, did not show a reduced capacity to stimulate glucose utilization, indicative that the level of receptor expression was sufficient to saturate downstream insulin action. These findings indicate that alpha-alpha disulphides additional to that provided by Cys-524 hold the receptor dimer together and that mutagenesis of Cys-524 reduces the ability of the receptor to signal insulin action subsequent to hormone binding.

SUBMITTER: Macaulay SL

PROVIDER: S-EPMC1137366 | biostudies-other | 1994 Oct

REPOSITORIES: biostudies-other

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Project description:BackgroundPredicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.ResultsIn the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.ConclusionsIn this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

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Cysteine-524 is not the only residue involved in the formation of disulphide-bonded dimers of the insulin receptor.

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