Structure Modeling of All Identified G Protein–Coupled Receptors in the Human Genome

Yang Zhang,Jeffrey Skolnick,Mark E Devries,Diana Murray

doi:10.1371/journal.pcbi.0020013

Yang Zhang, Jeffrey Skolnick + Show 2 more

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https://doi.org/10.1371/journal.pcbi.0020013

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Abstract

G protein–coupled receptors (GPCRs), encoded by about 5% of human genes, comprise the largest family of integral membrane proteins and act as cell surface receptors responsible for the transduction of endogenous signal into a cellular response. Although tertiary structural information is crucial for function annotation and drug design, there are few experimentally determined GPCR structures. To address this issue, we employ the recently developed threading assembly refinement (TASSER) method to generate structure predictions for all 907 putative GPCRs in the human genome. Unlike traditional homology modeling approaches, TASSER modeling does not require solved homologous template structures; moreover, it often refines the structures closer to native. These features are essential for the comprehensive modeling of all human GPCRs when close homologous templates are absent. Based on a benchmarked confidence score, approximately 820 predicted models should have the correct folds. The majority of GPCR models share the characteristic seven-transmembrane helix topology, but 45 ORFs are predicted to have different structures. This is due to GPCR fragments that are predominantly from extracellular or intracellular domains as well as database annotation errors. Our preliminary validation includes the automated modeling of bovine rhodopsin, the only solved GPCR in the Protein Data Bank. With homologous templates excluded, the final model built by TASSER has a global Cα root-mean-squared deviation from native of 4.6 Å, with a root-mean-squared deviation in the transmembrane helix region of 2.1 Å. Models of several representative GPCRs are compared with mutagenesis and affinity labeling data, and consistent agreement is demonstrated. Structure clustering of the predicted models shows that GPCRs with similar structures tend to belong to a similar functional class even when their sequences are diverse. These results demonstrate the usefulness and robustness of the in silico models for GPCR functional analysis. All predicted GPCR models are freely available for noncommercial users on our Web site (http://www.bioinformatics.buffalo.edu/GPCR).

Highlights

Gprotein–coupled receptors (GPCRs) are integral membrane proteins embedded in the cell surface that transmit signals to cells in response to stimuli such as light, Ca2þ, odorants, amino acids, nucleotides, peptides, or proteins and mediate many physiological functions through their interaction with heterotrimeric G proteins [1,2]
Threading Results On threading the 907 GPCR sequences through our template library, a representative protein set covering Protein Data Bank (PDB) at the level of 35% sequence identity, PROSPECTOR_3 [14] assigns 778 sequences as easy targets, with average alignment coverage of 78%
Unlike traditional comparative modeling (CM) methods, threading assembly refinement (TASSER) does not require that the structures of homologous templates be solved, an essential attribute for the successful modeling of the whole set of human GPCR proteins, because most GPCRs have no close evolutionary relationship to proteins in the PDB

Summary

Introduction

Gprotein–coupled receptors (GPCRs) are integral membrane proteins embedded in the cell surface that transmit signals to cells in response to stimuli such as light, Ca2þ, odorants, amino acids, nucleotides, peptides, or proteins and mediate many physiological functions through their interaction with heterotrimeric G proteins [1,2]. While knowledge of a protein’s structure furnishes important information for understanding its function and for drug design [6], progress in solving GPCR structures has been slow [7]. High concentrations of dissolved proteins are needed; and as yet no complete GPCR structure has been solved by the method. It is unlikely that a significant number of high-resolution GPCR structures will be experimentally solved in the very near future. This situation limits the use of structure-based approaches for drug design and restricts research into the mechanisms that control ligand binding to GPCRs, activation and regulation of GPCRs, and signal transduction mediated by GPCRs [9]

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