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Second extracellular loops (ECL2) of G protein-coupled receptors (GPCR) are known to play important roles by accommodating various GPCR ligands and providing ligand specificity. Despite the structural similarity among GPCR proteins, ECL2 structure is particularly hard to predict because of the relatively large size and ill-conserved sequence. In this study, we developed an efficient sampling algorithm for GPCR ECL2 that utilizes geometric constraints specific for GPCR. Two applications of the triaxial loop closure algorithm were employed to sample geometrically plausible ECL2 conformations that form a well-conserved disulfide bond with a particular transmembrane helix. Scores based on geometric constraints that effectively describe ECL2 environment were introduced to facilitate filtering of implausible ECL2 structures. All of these components are purely geometric, hence sampling and filtering can be performed with extremely low computational cost. A benchmark test was performed on seven unique GPCRs for which all-atom structures have been revealed. The result shows that the best model out of 50 sampled structures is of acceptable accuracy with the median loop RMSD less than 5 A. Combined with energy-guided global optimization, further refined ECL2 structures could be obtained. New ideas introduced in this study may be useful for developing methodologies for further GPCR modeling and docking studies.

For successful template-based protein modeling, it is important to identify relevant template proteins to the target sequence and then to generate proper multiple sequence alignment (MSA) between the target and the templates. However, in many cases, the templates obtained by global sequence search do not provide relevant structural information for local regions represented by gaps in the MSA. We have developed a method to improve the modeling accuracy of such regions by detecting unreliable local regions and utilizing local templates that can provide more reliable structural information for those regions. Our approach takes the following steps. First, a new scoring scheme that utilizes a modified information score is employed to detect unreliable local regions. Second, local templates that are aligned to the local regions more reliably are identified. Finally, the local templates are combined with the global templates to produce better 3D models. With newly obtained MSA containing global as well as local templates, protein 3D models are generate by a recently proposed model-building technique, MODELLER-CSA.