ISSN 1470-3947 (print)
ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology

Published by BioScientifica
Endocrine Abstracts (2012) 29 S25.2 
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Light on the structural communication in gonadotropin hormone receptors: implications in genetic diseases

F. Fanelli

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The network paradigm is being increasingly used to describe the topology and dynamics of complex systems. The representation of protein structures as networks of interacting amino acids has in fact been used to investigate and elucidate complex phenomena such as protein folding and unfolding, protein stability, the role of structurally and functionally important residues, protein-protein and protein-DNA interactions and intra- and inter-protein communication and allosterism.

The graph theory was combined with fluctuation dynamics in the framework of Protein Structure Network (PSN) analysis to investigate the structural communication in the inactive and active states of the Gonadotropin Hormone Receptors (GHRs). The analysis of wild type and spontaneously occurring GHR mutants served to identify key amino acids that are part of the regulatory network responsible for propagating communication between the extracellular and intracellular poles of the receptors. Highly conserved amino acids in the rhodopsin family of G Protein Coupled Receptors (GPCRs) participate in the protein structural stability as highly connected nodes in the network (i.e. hubs) in both the inactive and active states. Moreover, they behave as the most frequent nodes in the communication paths between the extracellular and intracellular sides.

Hub distribution reflects the existence of a diffuse intramolecular communication inside and between the two poles of the helix bundle, which makes pathogenic mutations share similar phenotypes irrespective of topological and physico-chemical differences between them. Spontaneously occurring gain-of-function and loss-of-function mutations induce perturbations in the structure network that characterize the wild type form. In this framework, the computational models are useful tools for structure-based identification of ligands able to correct the genetic defect by restoring the essential features of the native structure network.

Declaration of interest: The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Funding: This work was supported, however funding details are unavailable.

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