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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 only enthalpy basedscore and by combining with allosteric hub score and residue basedstress for (a) β1AR and (b) A2AR. Thepercent recovery of thermostable positives by screening differentcut-offs of residue mutations are compared.
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fig7: Comparison of thermostability prediction using only enthalpy basedscore and by combining with allosteric hub score and residue basedstress for (a) β1AR and (b) A2AR. Thepercent recovery of thermostable positives by screening differentcut-offs of residue mutations are compared.

Mentions: To studythe feasibility of using stress or allosteric communication in predictingthermostability, we plotted the normal distance from the random line(ndis) as a function of allosteric hub score and stress cut-offs,as shown in Supporting Information Figure S2. The normal distance, ndis, is a measure of predictability of thermostablemutants and is explained in Text S1 and Figure S1 of Supporting Information. Parts a and bof Figure S2 show the predictability when no enthalpy scorecutoff is used. Parts c and d of Figure S2 show the predictability among mutants that have enthalpy scoresabove a cutoff of −1 kcal/mol (optimal cutoff for both receptorsfor maximizing TPR and minimizing FPR). The black and dark blue regionsin Supporting Information Figure S2 showno predictability, whereas the yellow and red areas show the highestpredictability. For both β1AR and A2AR,an allosteric hub score of 40 and internal stress of 7000 pN werefound to be the optimal cut-offs for predicting thermostability, asindicated by the maxima in Figure S2 (redcircle). Thus, combining stress and allosteric hub score with thestability score from enthalpy improves thermostability predictionas shown by the increased red region in FigureS2b and d. For A2AR, the improvement observed byadding allosteric hub and stress information over enthalpy score wasmore significant than for β1AR. We calculated theenrichments for different percent cutoffs using enthalpy score aloneand including allosteric hub and stress information for both β1AR and A2AR, as shown in Figure 7. For all three metrics, the optimal score cut-offs were usedas mentioned before. For both β1AR and A2AR, we find modest improvement in enrichment for lower cut-offs, whileincluding allosteric hub and stress information. For β1AR, the improvement was found to be the highest at a cutoff of 35%,while for A2AR, the maximum enrichment was obtained at20% cutoff.


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 only enthalpy basedscore and by combining with allosteric hub score and residue basedstress for (a) β1AR and (b) A2AR. Thepercent recovery of thermostable positives by screening differentcut-offs of residue mutations are compared.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4230369&req=5

fig7: Comparison of thermostability prediction using only enthalpy basedscore and by combining with allosteric hub score and residue basedstress for (a) β1AR and (b) A2AR. Thepercent recovery of thermostable positives by screening differentcut-offs of residue mutations are compared.
Mentions: To studythe feasibility of using stress or allosteric communication in predictingthermostability, we plotted the normal distance from the random line(ndis) as a function of allosteric hub score and stress cut-offs,as shown in Supporting Information Figure S2. The normal distance, ndis, is a measure of predictability of thermostablemutants and is explained in Text S1 and Figure S1 of Supporting Information. Parts a and bof Figure S2 show the predictability when no enthalpy scorecutoff is used. Parts c and d of Figure S2 show the predictability among mutants that have enthalpy scoresabove a cutoff of −1 kcal/mol (optimal cutoff for both receptorsfor maximizing TPR and minimizing FPR). The black and dark blue regionsin Supporting Information Figure S2 showno predictability, whereas the yellow and red areas show the highestpredictability. For both β1AR and A2AR,an allosteric hub score of 40 and internal stress of 7000 pN werefound to be the optimal cut-offs for predicting thermostability, asindicated by the maxima in Figure S2 (redcircle). Thus, combining stress and allosteric hub score with thestability score from enthalpy improves thermostability predictionas shown by the increased red region in FigureS2b and d. For A2AR, the improvement observed byadding allosteric hub and stress information over enthalpy score wasmore significant than for β1AR. We calculated theenrichments for different percent cutoffs using enthalpy score aloneand including allosteric hub and stress information for both β1AR and A2AR, as shown in Figure 7. For all three metrics, the optimal score cut-offs were usedas mentioned before. For both β1AR and A2AR, we find modest improvement in enrichment for lower cut-offs, whileincluding allosteric hub and stress information. For β1AR, the improvement was found to be the highest at a cutoff of 35%,while for A2AR, the maximum enrichment was obtained at20% cutoff.

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