Revisiting Automated G-Protein Coupled Receptor Modeling: The Benefit of Additional Template Structures for a Neurokinin-1 Receptor Model

Abstract

The feasibility of automated procedures for the modeling of G-protein coupled receptors (GPCR) is investigated on the example of the human neurokinin-1 (NK1) receptor. We use a combined method of homology modeling and molecular docking and analyze the information content of the resulting docking complexes regarding the binding mode for further refinements. Moreover, we explore the impact of different template structures, the bovine rhodopsin structure, the human beta(2) adrenergic receptor, and in particular a combination of both templates to include backbone flexibility in the target conformational space. Our results for NK1 modeling demonstrate that model selection from a set of decoys can in general not solely rely on docking experiments but still requires additional mutagenesis data. However, an enrichment factor of 2.6 in a nearly fully automated approach indicates that reasonable models can be created automatically if both available templates are used for model construction. Thus, the recently resolved GPCR structures open new ways to improve the model building fundamentally.

Physical Binding Pocket Induction for Affinity Prediction

“…The work done by groups to employ data fitting approaches to select and refine homology models of proteins for activity prediction is the closest in spirit to what we report here (7; 48; 49). These approaches have been applied with some success to GPCR ligand discovery.…”

Comparative Sequence and Structural Analyses of G-Protein-Coupled Receptor Crystal Structures and Implications for Molecular Models

“…Consequently, we suggest that the use of multiple templates when building comparative models of GPCRs is likely to lead to more accurate results. Indeed, a recent study demonstrated that automated modelling of human neurokinin-1 (NK1) receptor was enriched by a factor of 2.6 when a combination of bRHO and hB2AR were used to construct models rather than when used as single templates [58]. The recent blind assessment of methods for GPCR structure modelling revealed that the best predictions relied on homology modelling approaches and that progress in the GPCR homology model building field will require improvements in the current prediction methods to “add value” to the best available templates [59].…”

“…The recent blind assessment of methods for GPCR structure modelling revealed that the best predictions relied on homology modelling approaches and that progress in the GPCR homology model building field will require improvements in the current prediction methods to “add value” to the best available templates [59]. The mutagenesis data stored in GPCR databases such as the SSFA [60], GRIS [61] and GPCRDB [62] provide a means of verifying homology models through identification of structure-function relationships of particular sidechains [58], [63]. We believe that our analysis of the recently solved GPCR structures contributes to a more consistent method for GPCR template selection that opens new ways to fundamentally improve the quality of GPCR homology model building.…”

IKKβ inhibitors identification part II: Ligand and structure-based virtual screening

“…Employing more than one template increase the backbone flexibility and thus expanding the accessible conformational space. 33 The automated sequence alignment and analysis of the template and target is carried out using the Clustal X 1.81 34 and the GeneDoc 2.6 35 programs. The alignment depicts the G-loop and the hinge regions have a high sequence similarity, whereas the activation loop (A-loop) has lesser similarity (Fig.…”