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

Bhattacharya S, Lee S, Grisshammer R, Tate CG, Vaidehi N - J Chem Theory Comput (2014)

Bottom Line: Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious.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 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.

View Article: PubMed Central - PubMed

Affiliation: Division of Immunology, Beckman Research Institute of the City of Hope , 1500 East Duarte Rd, Duarte, California 91010, United States.

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 β1-adrenergic receptor (β1AR), 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, β1AR, 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.

No MeSH data available.


Related in: MedlinePlus

Comparison of thermostability prediction using different receptorstructures. For performance comparison, the metric AUC (area underROC curve) is used. For positive predictability, the AUC varies between0.5 and 1. The higher the AUC, the greater is the predictability.The darker color bars are the AUC calculated using the LITiCon ensembleof structures, and the lighter colored bars have been calculated usingsingle conformation either from crystal structures or homology modelsas indicated on the x-axis below each set of bars.In the x-axis, “crystal structure”refers to the crystal structure of the corresponding receptor. Theblue bars that are labeled “homology β2AR”refers to the homology model of β1AR derived fromthe β2AR inactive state crystal structure as template.
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fig5: Comparison of thermostability prediction using different receptorstructures. For performance comparison, the metric AUC (area underROC curve) is used. For positive predictability, the AUC varies between0.5 and 1. The higher the AUC, the greater is the predictability.The darker color bars are the AUC calculated using the LITiCon ensembleof structures, and the lighter colored bars have been calculated usingsingle conformation either from crystal structures or homology modelsas indicated on the x-axis below each set of bars.In the x-axis, “crystal structure”refers to the crystal structure of the corresponding receptor. Theblue bars that are labeled “homology β2AR”refers to the homology model of β1AR derived fromthe β2AR inactive state crystal structure as template.

Mentions: For each homology model, we have compared the performanceof using LITiCon ensembles of receptor conformations to that usingsingle receptor structures. For each receptor model, we have calculatedthe area under the ROC curve (AUC) for the thermostability predictionsusing LITiCon as well as single receptor structure. AUC is directlyproportional to the predictability of the computation scheme, andfor computation schemes with zero predictability, the AUC is closeto 0.5. Figure 5 shows the comparison of AUCfor LITiCon generated ensembles versus single receptor structure foreach homology model of β1AR, A2AR, andNTSR1. Similar to the crystal structures, most of the homology modelsshow moderate to large improvement in predictability by using ensemblesof receptor conformations compared to single receptor structures.The improvement in performance is more prominent in the high accuracyβ1AR models that were derived using close homologuetemplate structures such as β2AR and D3DR. For homology models of β1AR derived using distanttemplates such as A2AR and CXCR4, the performance advantageof LITiCon over single receptor structure is negligible.


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

Bhattacharya S, Lee S, Grisshammer R, Tate CG, Vaidehi N - J Chem Theory Comput (2014)

Comparison of thermostability prediction using different receptorstructures. For performance comparison, the metric AUC (area underROC curve) is used. For positive predictability, the AUC varies between0.5 and 1. The higher the AUC, the greater is the predictability.The darker color bars are the AUC calculated using the LITiCon ensembleof structures, and the lighter colored bars have been calculated usingsingle conformation either from crystal structures or homology modelsas indicated on the x-axis below each set of bars.In the x-axis, “crystal structure”refers to the crystal structure of the corresponding receptor. Theblue bars that are labeled “homology β2AR”refers to the homology model of β1AR derived fromthe β2AR inactive state crystal structure as template.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4230369&req=5

fig5: Comparison of thermostability prediction using different receptorstructures. For performance comparison, the metric AUC (area underROC curve) is used. For positive predictability, the AUC varies between0.5 and 1. The higher the AUC, the greater is the predictability.The darker color bars are the AUC calculated using the LITiCon ensembleof structures, and the lighter colored bars have been calculated usingsingle conformation either from crystal structures or homology modelsas indicated on the x-axis below each set of bars.In the x-axis, “crystal structure”refers to the crystal structure of the corresponding receptor. Theblue bars that are labeled “homology β2AR”refers to the homology model of β1AR derived fromthe β2AR inactive state crystal structure as template.
Mentions: For each homology model, we have compared the performanceof using LITiCon ensembles of receptor conformations to that usingsingle receptor structures. For each receptor model, we have calculatedthe area under the ROC curve (AUC) for the thermostability predictionsusing LITiCon as well as single receptor structure. AUC is directlyproportional to the predictability of the computation scheme, andfor computation schemes with zero predictability, the AUC is closeto 0.5. Figure 5 shows the comparison of AUCfor LITiCon generated ensembles versus single receptor structure foreach homology model of β1AR, A2AR, andNTSR1. Similar to the crystal structures, most of the homology modelsshow moderate to large improvement in predictability by using ensemblesof receptor conformations compared to single receptor structures.The improvement in performance is more prominent in the high accuracyβ1AR models that were derived using close homologuetemplate structures such as β2AR and D3DR. For homology models of β1AR derived using distanttemplates such as A2AR and CXCR4, the performance advantageof LITiCon over single receptor structure is negligible.

Bottom Line: Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious.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 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.

View Article: PubMed Central - PubMed

Affiliation: Division of Immunology, Beckman Research Institute of the City of Hope , 1500 East Duarte Rd, Duarte, California 91010, United States.

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 β1-adrenergic receptor (β1AR), 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, β1AR, 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.

No MeSH data available.


Related in: MedlinePlus