The study of G‐protein coupled receptor oligomerization with computational modeling and bioinformatics

Abstract

To achieve a structural context for the analysis of G‐protein coupled receptor (GPCR) oligomers, molecular modeling must be used to predict the corresponding interaction interfaces. The task is complicated by the paucity of detailed structural data at atomic resolution, and the large number of possible modes in which the bundles of seven transmembrane (TM) segments of the interacting GPCR monomers can be packed together into dimers and/or higher‐order oligomers. Approaches and tools offered by bioinformatics can be used to reduce the complexity of this task and, combined with computational modeling, can serve to yield testable predictions for the structural properties of oligomers. Most of the bioinformatics methods take advantage of the evolutionary relation that exists among GPCRs, as expressed in their sequences and measurable in the common elements of their structural and functional features. These common elements are responsible for the presence of detectable patterns of motifs and correlated mutations evident from the alignment of the sequences of these complex biological systems. The decoding of these patterns in terms of structural and functional determinants can provide indications about the most likely interfaces of dimerization/oligomerization of GPCRs. We review here the main approaches from bioinformatics, enhanced by computational molecular modeling, that have been used to predict likely interfaces of dimerization/oligomerization of GPCRs, and compare results from their application to rhodopsin‐like GPCRs. A compilation of the most frequently predicted GPCR oligomerization interfaces points to specific regions of TMs 4–6.

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

“…According to a meta-analysis reported by Filizola and Weinstein (2005), most of the identified residues are within TM4-TM6, further suggesting a specific role for these three helices in the dimerization/oligomerization interfaces of GPCR. -A second type of approach is based on the analysis of the three-dimensional structure (as obtained by experimental investigation or by homology modeling) of the protein under scrutiny to identify the possible surfaces of interaction with other proteins.…”

“…Several bioinformatics methods have been devised to predict the interfaces available to a given GPCR for RRI (Filizola and Weinstein, 2005;Guidolin et al, 2011a). They can, in principle, be categorized into three broad classes according to the type of strategy followed to perform the analysis (Simpson et al, 2010): -The first type of approach is based on the identification of protein regions exhibiting some property (potentially relevant for the interaction with other proteins) that can be deduced simply by the analysis of the primary structure (i.e.…”

“…Other sequence-based computational methods that have been used include variants of the 'evolutionary trace' method, 'level entropy' and 'sequence space automation' methods, and 'correlated mutation analysis' (see Filizola and Weinstein, 2005;Vohra et al, 2007;Guidolin et al, 2011a, for reviews). According to a meta-analysis reported by Filizola and Weinstein (2005), most of the identified residues are within TM4-TM6, further suggesting a specific role for these three helices in the dimerization/oligomerization interfaces of GPCR.…”

Computational Methods in Drug Design: Modeling G Protein-Coupled Receptor Monomers, Dimers, and Oligomers

“…As a result, a suitable representation of the evolutionary relationships between proteins under study is an essential requirement for the prediction of dimer/oligomer inter faces. 107 …”

“…Sequence changes occurring over evolutionary time at the dimerization interface of monomer A would be compensated for by changes at the interacting face of monomer B to preserve the interaction interface. 107 Gouldson and coworkers recently used the CMA method to analyze candidate residues for the subtype-specifi c heterodimerization observed for the chemokine, opioid, and somatostatin receptors. 109 …”