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Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors.

ABSTRACT: G protein-coupled receptors (GPCRs) are highly dynamic and often denature when extracted in detergents. Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious. We have developed a computational method to predict the position of the thermostabilizing mutations for a given GPCR sequence. We have validated the method against experimentally measured thermostability data for single mutants of the ?1-adrenergic receptor (?1AR), adenosine A2A receptor (A2AR) and neurotensin receptor 1 (NTSR1). To make these predictions we started from homology models of these receptors of varying accuracies and generated an ensemble of conformations by sampling the rigid body degrees of freedom of transmembrane helices. Then, an all-atom force field function was used to calculate the enthalpy gain, known as the "stability score" upon mutation of every residue, in these receptor structures, to alanine. For all three receptors, ?1AR, A2AR, and NTSR1, we observed that mutations of hydrophobic residues in the transmembrane domain to alanine that have high stability scores correlate with high experimental thermostability. The prediction using the stability score improves when using an ensemble of receptor conformations compared to a single structure, showing that receptor flexibility is important. We also find that our previously developed LITiCon method for generating conformation ensembles is similar in performance to predictions using ensembles obtained from microseconds of molecular dynamics simulations (which is computationally hundred times slower than LITiCon). We improved the thermostability prediction by including other properties such as residue-based stress and the extent of allosteric communication by each residue in the stability score. Our method is the first step toward a computational method for rapid prediction of thermostable mutants of GPCRs.

SUBMITTER: Bhattacharya S

PROVIDER: S-EPMC4230369 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors.

Bhattacharya Supriyo S Lee Sangbae S Grisshammer Reinhard R Tate Christopher G CG Vaidehi Nagarajan N

Journal of chemical theory and computation 20141014 11

G protein-coupled receptors (GPCRs) are highly dynamic and often denature when extracted in detergents. Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious. We have developed a computational method to predict the position of the thermostabilizing mutations for a given GPCR sequence. We have validated the method against experimentally measured thermostability data for single mutants of the β<sub>1</sub>-adrenerg ...[more]

PMID: 25400524

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Project description:BACKGROUND: The classic paradigm of heterotrimeric G-protein signaling describes a heptahelical, membrane-spanning G-protein coupled receptor that physically interacts with an intracellular G alpha subunit of the G-protein heterotrimer to transduce signals. G-protein coupled receptors comprise the largest protein superfamily in metazoa and are physiologically important as they sense highly diverse stimuli and play key roles in human disease. The heterotrimeric G-protein signaling mechanism is conserved across metazoa, and also readily identifiable in plants, but the low sequence conservation of G-protein coupled receptors hampers the identification of novel ones. Using diverse computational methods, we performed whole-proteome analyses of the three dominant model plant species, the herbaceous dicot Arabidopsis thaliana (mouse-eared cress), the monocot Oryza sativa (rice), and the woody dicot Populus trichocarpa (poplar), to identify plant protein sequences most likely to be GPCRs. RESULTS: Our stringent bioinformatic pipeline allowed the high confidence identification of candidate G-protein coupled receptors within the Arabidopsis, Oryza, and Populus proteomes. We extended these computational results through actual wet-bench experiments where we tested over half of our highest ranking Arabidopsis candidate G-protein coupled receptors for the ability to physically couple with GPA1, the sole G alpha in Arabidopsis. We found that seven out of eight tested candidate G-protein coupled receptors do in fact interact with GPA1. We show through G-protein coupled receptor classification and molecular evolutionary analyses that both individual G-protein coupled receptor candidates and candidate G-protein coupled receptor families are conserved across plant species and that, in some cases, this conservation extends to metazoans. CONCLUSION: Our computational and wet-bench results provide the first step toward understanding the diversity, conservation, and functional roles of plant candidate G-protein coupled receptors.

Dataset Information

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors.

Publications

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors.

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