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Bioinformatics and mathematical modelling in the study of receptor-receptor interactions and receptor oligomerization: focus on adenosine receptors.

Authors: GUIDOLIN, DIEGO; Ciruela F; Genedani S; Guescini M; TORTORELLA, CINZIA; ALBERTIN, GIOVANNA; Fuxe K; +1 Authors

Bioinformatics and mathematical modelling in the study of receptor-receptor interactions and receptor oligomerization: focus on adenosine receptors.

Abstract

The concept of intra-membrane receptor-receptor interactions (RRIs) between different types of G protein-coupled receptors (GPCRs) and evidence for their existence was introduced by Agnati and Fuxe in 1980/81 through the biochemical analysis of the effects of neuropeptides on the binding characteristics of monoamine receptors in membrane preparations from discrete brain regions and functional studies of the interactions between neuropeptides and monoamines in the control of specific functions such as motor control and arterial blood pressure control in animal models. Whether GPCRs can form high-order structures is still a topic of an intense debate. Increasing evidence, however, suggests that the hypothesis of the existence of high-order receptor oligomers is correct. A fundamental consequence of the view describing GPCRs as interacting structures, with the likely formation at the plasma membrane of receptor aggregates of multiple receptors (Receptor Mosaics) is that it is no longer possible to describe signal transduction simply as the result of the binding of the chemical signal to its receptor, but rather as the result of a filtering/integration of chemical signals by the Receptor Mosaics (RMs) and membrane-associated proteins. Thus, in parallel with experimental research, significant efforts were spent in bioinformatics and mathematical modelling. We review here the main approaches that have been used to assess the interaction interfaces allowing the assembly of GPCRs and to shed some light on the integrative functions emerging from the complex behaviour of these RMs. Particular attention was paid to the RMs generated by adenosine A(2A), dopamine D(2), cannabinoid CB(1), and metabotropic glutamate mGlu(5) receptors (A(2A), D(2), CB(1) and mGlu(5), respectively), and a possible approach to model the interplay between the D(2)-A(2A)-CB(1) and D(2)-A(2A)-mGlu(5) trimers is proposed.

Country
Italy
Keywords

Bioinformatic, Bioinformatic; G protein-coupled receptor; Oligomerization; Receptor–receptor interaction; Receptor Mosaic; Allosterism; Adenosine receptor, Receptor Mosaic, Adenosine receptor, Receptors, Purinergic P1, Computational Biology, Models, Theoretical, Bioinformatic, G protein-coupled receptor, Oligomerization, Receptor-receptor interaction, Receptor Mosaic, Allosterism, Adenosine receptor, Receptor–receptor interaction, Allosterism, Oligomerization, Animals, Humans, G protein-coupled receptor, Protein Multimerization, Protein Binding

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Average
Top 10%