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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 ROC curves for thermostability predictionusing proteinstructural ensemble generated from (1) LITiCon ensemble generatedstarting from crystal structure and (2) representative conformationsfrom MD of crystal structure; (a) β1AR (LITiCon AUC:0.67. MD ensemble AUC: 0.64); (b) A2AR (LITiCon AUC: 0.64.MD ensemble AUC: 0.62).
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fig2: Comparison of ROC curves for thermostability predictionusing proteinstructural ensemble generated from (1) LITiCon ensemble generatedstarting from crystal structure and (2) representative conformationsfrom MD of crystal structure; (a) β1AR (LITiCon AUC:0.67. MD ensemble AUC: 0.64); (b) A2AR (LITiCon AUC: 0.64.MD ensemble AUC: 0.62).

Mentions: The LITiCon method used for generating conformationensemble takes one-hundredth of the computational time required forMD simulations. To evaluate the effectiveness of the conformationensemble generated from LITiCon in predicting thermostable mutations,in comparison to the ensemble obtained from long time scale MD simulations,we compared the predictions using the stability scores calculatedusing two different schemes; (1) the ensemble generated by LITiConstarting from the crystal structures and (2) structural ensemble generatedusing MD simulations starting from the crystal structure. For detailson selecting the MD conformations, please refer to the Methods section. Parts a and b of Figure 2 show a comparison of the ROC curves from the LITiCon ensembleto that from structural ensembles from MD simulations for β1AR and A2AR. For β1AR, the LITiConusing the most populated MD conformation performed the best, whereasboth the crystal structure LITiCon and structural ensembles from MDsimulation showed similar performance. In the case of A2AR, LITiCon using the crystal structure performed the best, whereasthe MD ensemble showed the worst predictability. Overall, these resultsindicate that the structural ensembles generated by LITiCon performequally or better than conformational ensembles generated using computationallyexpensive MD simulations.


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 ROC curves for thermostability predictionusing proteinstructural ensemble generated from (1) LITiCon ensemble generatedstarting from crystal structure and (2) representative conformationsfrom MD of crystal structure; (a) β1AR (LITiCon AUC:0.67. MD ensemble AUC: 0.64); (b) A2AR (LITiCon AUC: 0.64.MD ensemble AUC: 0.62).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4230369&req=5

fig2: Comparison of ROC curves for thermostability predictionusing proteinstructural ensemble generated from (1) LITiCon ensemble generatedstarting from crystal structure and (2) representative conformationsfrom MD of crystal structure; (a) β1AR (LITiCon AUC:0.67. MD ensemble AUC: 0.64); (b) A2AR (LITiCon AUC: 0.64.MD ensemble AUC: 0.62).
Mentions: The LITiCon method used for generating conformationensemble takes one-hundredth of the computational time required forMD simulations. To evaluate the effectiveness of the conformationensemble generated from LITiCon in predicting thermostable mutations,in comparison to the ensemble obtained from long time scale MD simulations,we compared the predictions using the stability scores calculatedusing two different schemes; (1) the ensemble generated by LITiConstarting from the crystal structures and (2) structural ensemble generatedusing MD simulations starting from the crystal structure. For detailson selecting the MD conformations, please refer to the Methods section. Parts a and b of Figure 2 show a comparison of the ROC curves from the LITiCon ensembleto that from structural ensembles from MD simulations for β1AR and A2AR. For β1AR, the LITiConusing the most populated MD conformation performed the best, whereasboth the crystal structure LITiCon and structural ensembles from MDsimulation showed similar performance. In the case of A2AR, LITiCon using the crystal structure performed the best, whereasthe MD ensemble showed the worst predictability. Overall, these resultsindicate that the structural ensembles generated by LITiCon performequally or better than conformational ensembles generated using computationallyexpensive MD simulations.

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