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Refinement of G protein-coupled receptor structure models: Improving the prediction of loop conformations and the virtual ligand screening performances

G protein-coupled receptors (GPCRs) constitute the largest superfamily of membrane proteins. They mediate most of the physiological processes of the human body and form the largest group of potential drug targets. Therefore, knowledge of their three-dimensional structure is important for structure-based drug design. Due to the limited availability of the experimental structures of GPCRs, computational methods are often used for deriving the structural information. GPCRs have a common structural topology that is comprised of seven transmembrane helices interconnected by intra- and extracellular loops. Homology modeling is the computational approach that is commonly used for modeling the transmembrane helical domains of GPCRs. Depending upon the quality of template used, these homology models exhibit varying degrees of inaccuracies. We have previously explored the extent to which inaccuracies present in homology models of the transmembrane helical domains of GPCRs can affect loop prediction [1]. We have also investigated the effect of presence and absence of other extracellular loops on individual loop modeling. We found that loop prediction in GPCR models is much more difficult than loop reconstruction in crystal structures because of the imprecise positioning of loop anchors in the models, although modeling an extracellular loop in the presence of other extracellular loops helps in improving the accuracy of its prediction. Therefore, reducing the errors in loop anchors is crucial for GPCR structure prediction. To address this and to improve the usability of GPCR homology models for structure-based drug design, we have developed a Ligand Directed Modeling (LDM) method that involves geometric protein sampling and ligand docking. The method was evaluated for capacity to refine the GPCR models built across a range of templates with varying degrees of sequence similarity with the target. LDM reduced the errors in loop anchor positions and improved the performance of these models in virtual ligand screenings. Thus, this Ligand Directed Modeling method is efficient in improving the quality of GPCR structure models.