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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

ROC curves for thermostability prediction usingan ensemble ofreceptor structures compared to single receptor structure. (a) β1AR (Ensemble AUC: 0.67. Single structure AUC: 0.56); (c) A2AR (Ensemble AUC: 0.64. Single structure AUC: 0.62); (e) NTSR1(Ensemble AUC: 0.64. Single structure AUC: 0.59). Enrichment as afunction of cutoff using (b) β1AR crystal structure;(d) A2AR crystal structure; (f) NTSR1 crystal structure.The mutants in the order of their measured experimental stabilityare shown in colored dots. Red, high thermostability; yellow, mediumthermostability; and empty circles, weak thermostability.
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fig1: ROC curves for thermostability prediction usingan ensemble ofreceptor structures compared to single receptor structure. (a) β1AR (Ensemble AUC: 0.67. Single structure AUC: 0.56); (c) A2AR (Ensemble AUC: 0.64. Single structure AUC: 0.62); (e) NTSR1(Ensemble AUC: 0.64. Single structure AUC: 0.59). Enrichment as afunction of cutoff using (b) β1AR crystal structure;(d) A2AR crystal structure; (f) NTSR1 crystal structure.The mutants in the order of their measured experimental stabilityare shown in colored dots. Red, high thermostability; yellow, mediumthermostability; and empty circles, weak thermostability.

Mentions: Figure 1 shows the performance of the calculatedthermostability scores starting from the crystal structures of β1AR, A2AR, and NTSR1 (PDB ID: 2VT4, 3EML, 4GRV, respectively).We have compared the predictability of thermostable mutants startingfrom the crystal structure (single conformation) to the predictabilitycalculated from an ensemble of conformations generated using the crystalstructure in Figure 1. As a measure of predictabilityof thermostability using the calculated stability score, we constructedthe Receiver Operational Characteristic (ROC) curves by plotting thetrue positive rate against the false positive rate for different cutoffsof the calculated stability score. Parts a, c, and e of Figure 1 show the ROC curves for β1AR,A2AR, and NTSR1, respectively, starting from crystal structures(single conformation) and from ensemble generated starting from thecrystal structure using LITiCon. The straight lines in these figures,also termed the “random line”, represent the ROC curvefor zero predictability. For all the receptors, the ROC curves arewell above the random line, and the predictability improves when usingan ensemble of conformations generated by LITiCon compared to usingsingle conformation from crystal structure. This indicates that smallvariations in conformation upon single point mutations are importantfor more accurate thermostability prediction compared to using thecrystal structures alone. To assess the number of single point mutationexperiments that can be reduced by using these predictions, we plottedthe enrichment factor as a function of cutoff in the number of mutationsfor β1AR, A2AR, and NTSR1 in Figure 1b, d, and f, respectively. We have also highlightedthe individual thermostable mutations at the cut-offs where they wereidentified. The range of thermostability values used for definingstrong, medium, and weak thermostable mutants for the three receptorsare shown in Table S2 of the Supporting Information. The cutoff values are different for each receptor, due to the differencein thermostability of the wild type receptor under the experimentalassay conditions. For all three receptors, the left side of the enrichmentplots (Figure 1b, d, and f) shows a higherconcentration of red and yellow mutations. Six out of nine experimentallyknown medium and strong thermostable mutants will be recovered withintop 50 predicted thermostable mutants for β1AR, fiveout of seven for A2A receptor and three out of four forNTSR1. This suggests that in the LITiConDesign method of thermostabilityprediction, the strong thermostable mutations are selectively enrichedover the weaker mutations. However, the method also missed severalstrong thermostabilizing mutations for each receptor (the red andyellow dots on the right side of Figures 1b,d, and f). These mutations were missed in all receptor models includingthe crystal structures and homology models. We have analyzed the reasonsfor missing these mutations in the discussion section.


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)

ROC curves for thermostability prediction usingan ensemble ofreceptor structures compared to single receptor structure. (a) β1AR (Ensemble AUC: 0.67. Single structure AUC: 0.56); (c) A2AR (Ensemble AUC: 0.64. Single structure AUC: 0.62); (e) NTSR1(Ensemble AUC: 0.64. Single structure AUC: 0.59). Enrichment as afunction of cutoff using (b) β1AR crystal structure;(d) A2AR crystal structure; (f) NTSR1 crystal structure.The mutants in the order of their measured experimental stabilityare shown in colored dots. Red, high thermostability; yellow, mediumthermostability; and empty circles, weak thermostability.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4230369&req=5

fig1: ROC curves for thermostability prediction usingan ensemble ofreceptor structures compared to single receptor structure. (a) β1AR (Ensemble AUC: 0.67. Single structure AUC: 0.56); (c) A2AR (Ensemble AUC: 0.64. Single structure AUC: 0.62); (e) NTSR1(Ensemble AUC: 0.64. Single structure AUC: 0.59). Enrichment as afunction of cutoff using (b) β1AR crystal structure;(d) A2AR crystal structure; (f) NTSR1 crystal structure.The mutants in the order of their measured experimental stabilityare shown in colored dots. Red, high thermostability; yellow, mediumthermostability; and empty circles, weak thermostability.
Mentions: Figure 1 shows the performance of the calculatedthermostability scores starting from the crystal structures of β1AR, A2AR, and NTSR1 (PDB ID: 2VT4, 3EML, 4GRV, respectively).We have compared the predictability of thermostable mutants startingfrom the crystal structure (single conformation) to the predictabilitycalculated from an ensemble of conformations generated using the crystalstructure in Figure 1. As a measure of predictabilityof thermostability using the calculated stability score, we constructedthe Receiver Operational Characteristic (ROC) curves by plotting thetrue positive rate against the false positive rate for different cutoffsof the calculated stability score. Parts a, c, and e of Figure 1 show the ROC curves for β1AR,A2AR, and NTSR1, respectively, starting from crystal structures(single conformation) and from ensemble generated starting from thecrystal structure using LITiCon. The straight lines in these figures,also termed the “random line”, represent the ROC curvefor zero predictability. For all the receptors, the ROC curves arewell above the random line, and the predictability improves when usingan ensemble of conformations generated by LITiCon compared to usingsingle conformation from crystal structure. This indicates that smallvariations in conformation upon single point mutations are importantfor more accurate thermostability prediction compared to using thecrystal structures alone. To assess the number of single point mutationexperiments that can be reduced by using these predictions, we plottedthe enrichment factor as a function of cutoff in the number of mutationsfor β1AR, A2AR, and NTSR1 in Figure 1b, d, and f, respectively. We have also highlightedthe individual thermostable mutations at the cut-offs where they wereidentified. The range of thermostability values used for definingstrong, medium, and weak thermostable mutants for the three receptorsare shown in Table S2 of the Supporting Information. The cutoff values are different for each receptor, due to the differencein thermostability of the wild type receptor under the experimentalassay conditions. For all three receptors, the left side of the enrichmentplots (Figure 1b, d, and f) shows a higherconcentration of red and yellow mutations. Six out of nine experimentallyknown medium and strong thermostable mutants will be recovered withintop 50 predicted thermostable mutants for β1AR, fiveout of seven for A2A receptor and three out of four forNTSR1. This suggests that in the LITiConDesign method of thermostabilityprediction, the strong thermostable mutations are selectively enrichedover the weaker mutations. However, the method also missed severalstrong thermostabilizing mutations for each receptor (the red andyellow dots on the right side of Figures 1b,d, and f). These mutations were missed in all receptor models includingthe crystal structures and homology models. We have analyzed the reasonsfor missing these mutations in the discussion section.

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