Modeling the 3D Structure of Rhodopsin Using a De Novo Approach to Build G-protein−Coupled Receptors

1999 ◽  
Vol 103 (13) ◽  
pp. 2520-2527 ◽  
Author(s):  
Marta Filizola ◽  
Maria Cartenì-Farina ◽  
Juan J. Perez
Keyword(s):  
G Protein ◽  
De Novo ◽  
3D Structure ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

scholarly journals patGPCR: A Multitemplate Approach for Improving 3D Structure Prediction of Transmembrane Helices of G-Protein-Coupled Receptors

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hongjie Wu ◽  
Qiang Lü ◽  
Lijun Quan ◽  
Peide Qian ◽  
Xiaoyan Xia
Keyword(s):  
G Protein ◽  
Structure Prediction ◽  
Transmembrane Helix ◽  
3D Structure ◽  
G Protein Coupled Receptors ◽  
Ligand Docking ◽  
Molecular Function ◽  
Transmembrane Helices ◽  
G Protein Coupled ◽  
Multiple Template

The structures of the seven transmembrane helices of G-protein-coupled receptors are critically involved in many aspects of these receptors, such as receptor stability, ligand docking, and molecular function. Most of the previous multitemplate approaches have built a “super” template with very little merging of aligned fragments from different templates. Here, we present a parallelized multitemplate approach, patGPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, which employs a bundle-packing related energy function that extends on the RosettaMem energy, parallelizes eight pipelines for transmembrane helix refinement and exchanges the optimized helix structures from multiple templates. We have investigated the performance of patGPCR on a test set containing eight determined G-protein-coupled receptors. The results indicate that patGPCR improves the TM RMSD of the predicted models by 33.64% on average against a single-template method. Compared with other homology approaches, the best models for five of the eight targets built by patGPCR had a lower TM RMSD than that obtained from SWISS-MODEL; patGPCR also showed lower average TM RMSD than single-template and multiple-template MODELLER.

scholarly journals Mutations Affecting the Oligomerization Interface of G-Protein-Coupled Receptors Revealed by a Novel De Novo Protein Design Framework

2008 ◽  
Vol 94 (7) ◽  
pp. 2470-2481 ◽  
Author(s):  
Martin S. Taylor ◽  
Ho K. Fung ◽  
Rohit Rajgaria ◽  
Marta Filizola ◽  
Harel Weinstein ◽  
...  
Keyword(s):  
G Protein ◽  
Protein Design ◽  
De Novo ◽  
G Protein Coupled Receptors ◽  
Design Framework ◽  
De Novo Protein Design ◽  
G Protein Coupled

scholarly journals Minireview: Insights into G Protein-Coupled Receptor Function Using Molecular Models**The work from our laboratories was supported by USPHS Grant DK-43036.

Endocrinology ◽  
2001 ◽  
Vol 142 (1) ◽  
pp. 2-10 ◽  
Author(s):  
Marvin C. Gershengorn ◽  
Roman Osman
Keyword(s):  
G Protein ◽  
Clinical Medicine ◽  
3D Structure ◽  
G Protein Coupled Receptors ◽  
Biological Research ◽  
Molecular Models ◽  
Powerful Approach ◽  
Medicine Today ◽  
Experimental Findings ◽  
G Protein Coupled

Abstract G protein-coupled receptors (GPCRs) represent the largest family of signal-transducing molecules known. They convey signals for light and many extracellular regulatory molecules. GPCRs have been found to be dysfunctional/dysregulated in a growing number of human diseases and have been estimated to be the targets of more than 30% of the drugs used in clinical medicine today. Thus, understanding how GPCRs function at the molecular level is an important goal of biological research. In order to understand function at this level, it is necessary to delineate the 3D structure of these receptors. Recently, the 3D structure of rhodopsin has been resolved, but in the absence of experimentally determined 3D structures of other GPCRs, a powerful approach is to construct a theoretical model for the receptor and refine it based on experimental results. Computer-generated models for many GPCRs have been constructed. In this article, we will review these studies. We will place the greatest emphasis on an iterative, bi-directional approach in which models are used to generate hypotheses that are tested by experimentation and the experimental findings are, in turn, used to refine the model. The success of this approach is due to the synergistic interaction between theory and experiment.

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