Homology modeling of a Class A GPCR in the inactive conformation: A quantitative analysis of the correlation between model/template sequence identity and model accuracy

Abstract

With the present work we quantitatively studied the modellability of the inactive state of Class A G protein-coupled receptors (GPCRs). Specifically, we constructed models of one of the Class A GPCRs for which structures solved in the inactive state are available, namely the β2 AR, using as templates each of the other class members for which structures solved in the inactive state are also available. Our results showed a detectable linear correlation between model accuracy and model/template sequence identity. This suggests that the likely accuracy of the homology models that can be built for a given receptor can be generally forecasted on the basis of the available templates. We also probed whether sequence alignments that allow for the presence of gaps within the transmembrane domains to account for structural irregularities afford better models than the classical alignment procedures that do not allow for the presence of gaps within such domains. As our results indicated, although the overall differences are very subtle, the inclusion of internal gaps within the transmembrane domains has a noticeable a beneficial effect on the local structural accuracy of the domain in question.

Binding, Thermodynamics, and Selectivity of a Non-peptide Antagonist to the Melanocortin-4 Receptor

“…Using the GPCR-SSFE 2.0 modeling server ( Worth et al, 2017 ), we found the inactive-state structure of the lyso-phospholipid sphingosine 1-phosphate receptor [S1PR1, PDB entry 3V2Y ( Hanson et al, 2012 )] with a sequence similarity in the transmembrane region of 56% and a sequence identity to hMC4R of 30% (Blossum62 matrix, Supplementary Figure S1 ). Such high sequence similarity between the suggested template and MC4R is important for the accuracy of the structural model ( Costanzi et al, 2016 ). The template structure is characterized by a leucine at position 5.50 in TM5 ( Ballesteros and Weinstein, 1995 ) and in consequence shows a regular α-helical conformation, as also expected for hMC4R.…”

Chiral analogues of (+)-cyclazosin as potent α1B-adrenoceptor selective antagonist

“…34,35 An initial automatic sequence alignment was first obtained with the Blosum 62 matrix, with penalties for the opening and the extension of gaps set to 4 and 3, respectively. This initial alignment was then inspected and manually adjusted, if needed, to ensure the matching of the motifs that identify each of the seven TMs and the absence of internal gaps within the boundaries of the membrane-spanning domains.…”

“…34 Differences in sequence length between the two aligned receptors were accounted for by placing gaps in the loop domains, following our recently described procedures for the alignment of domains that have fewer residues in the modeled receptor than in the template ( deletions in the modeled receptor ) and for the alignment of domains that have more residues in the modeled receptor than in the template ( insertions in the modeled receptor ). 35 Finally, as a number of residues in the N-terminus, C-terminus and third intracellular loop (IL3) of the D 2 receptor are not solved in the crystal structure, we expunged from the sequence alignment the residues of the α 1B -adrenoceptor that extend beyond those crystallographically solved for the template. The resulting alignment is shown in Figure 5.…”

Development of the first in vivo GPR17 ligand through an iterative drug discovery pipeline: A novel disease-modifying strategy for multiple sclerosis

“…We did so using class-A GPCR comparative modelling, a well-known bioinformatics strategy aimed at gathering novel structural information on this receptor family that indeed encompasses about 70% of all drug-targets [48,49]. Although GPR17 experimental structure has not been solved yet, the availability of homologous proteins to be used as templates, combined with specific class-A GPCR modelling techniques [50,51], allowed us to obtain an accurate and validated model, with a well-shaped binding site, useful for the subsequent HTS procedures [29,31,36,52,53]. Accordingly, in the very last years, homology modelling has been successfully applied also by other groups for identifying both similar and chemically diverse GPR17 ligands [23,54].…”