Graph analysis of β2 adrenergic receptor structures: a “social network” of GPCR residues

Abstract

PurposeG protein-coupled receptors (GPCRs) are a superfamily of membrane proteins of vast pharmaceutical interest. Here, we describe a graph theory-based analysis of the structure of the β2 adrenergic receptor (β2 AR), a prototypical GPCR. In particular, we illustrate the network of direct and indirect interactions that link each amino acid residue to any other residue of the receptor.MethodsNetworks of interconnected amino acid residues in proteins are analogous to social networks of interconnected people. Hence, they can be studied through the same analysis tools typically employed to analyze social networks – or networks in general – to reveal patterns of connectivity, influential members, and dynamicity. We focused on the analysis of closeness-centrality, which is a measure of the overall connectivity distance of the member of a network to all other members.ResultsThe residues endowed with the highest closeness-centrality are located in the middle of the seven transmembrane domains (TMs). In particular, they are mostly located in the middle of TM2, TM3, TM6 or TM7, while fewer of them are located in the middle of TM1, TM4 or TM5. At the cytosolic end of TM6, the centrality detected for the active structure is markedly lower than that detected for the corresponding residues in the inactive structures. Moreover, several residues acquire centrality when the structures are analyzed in the presence of ligands. Strikingly, there is little overlap between the residues that acquire centrality in the presence of the ligand in the blocker-bound structures and the agonist-bound structures.ConclusionsOur results reflect the fact that the receptor resembles a bow tie, with a rather tight knot of closely interconnected residues and two ends that fan out in two opposite directions: one toward the extracellular space, which hosts the ligand binding cavity, and one toward the cytosol, which hosts the G protein binding cavity. Moreover, they underscore how interaction network is by the conformational rearrangements concomitant with the activation of the receptor and by the presence of agonists or blockers.Electronic supplementary materialThe online version of this article (doi:10.1186/2193-9616-1-16) contains supplementary material, which is available to authorized users.

Computational characterization of the binding mode between oncoprotein Ets‐1 and DNA‐repair enzymes

“…One of the features of a RCA is that it can identify residues that are central to the structural integrity and play key roles in long‐range interactions. These residues have been shown to be important for protein function, fold or allosteric communication . We have focused our attention on the residues of Ets‐1 and considered as central those with a z score ≥ 2 (Table ).…”

“…These residues have been shown to be important for protein function, fold or allosteric communication. 32,33,[43][44][45] We have focused our attention on the residues of Ets-1 and considered as central those with a z score ≥ 2 (Table 1). Since the calculations have been performed on the structures of the complexes, we can therefore consider the residues on ETS emerging from these calculations to be important for the structural integrity of either the individual domain or the complex as a whole.…”

A point mutation in the β‐adrenergic‐like octopamine receptor: possible association with amitraz resistance

“…The Ile at this position may be functionally important since it is highly conserved in β‐AL OARs. The amino acids in TM1 might indirectly affect agonist binding by interacting with other residues in adjacent TMs (TM2 and TM7) 34 . Although the underlying molecular mechanism remains elusive, our findings indicate that the SNP occurs at an allosteric site capable of regulating the action of agonists.…”

Network Measures for Chemical Library Design

“…Sheftel et al. [] have reported the social network behavior of interconnected aminoacid residues of the β‐adrenergic receptor (G protein‐coupled receptor family) using “closeness centrality” measures. Closeness centrality describes the “neighborhoodness” [Sabidussi, ] of the clustered vertices in the graph: where d G = distance between the vertices v and t in graph G and v, t ϵ V (set of vertices).…”