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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

Allosteric hub score and local stress are plotted againstthermostabilityfor each residue position in (a, b) β1AR and (c,d) A2AR. Residues on the right side of the dotted verticalline are considered thermostable. Residues that have high allosterichub score or stress and poor thermostability are highlighted in red.The overall inverse correlation between stress and thermostabilityis shown by the regression lines in b and d.
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fig6: Allosteric hub score and local stress are plotted againstthermostabilityfor each residue position in (a, b) β1AR and (c,d) A2AR. Residues on the right side of the dotted verticalline are considered thermostable. Residues that have high allosterichub score or stress and poor thermostability are highlighted in red.The overall inverse correlation between stress and thermostabilityis shown by the regression lines in b and d.

Mentions: In thispaper, besides the enthalpy, we have investigated the effectof using the net stress or force on each residue and allosteric hubscore in thermostability predictions. Parts a and c of Figure 6 show the correlation between the allosteric hubscores for each residue in β1AR and A2AR crystal structures and the experimental thermostability score ofthe corresponding alanine mutant. We observe an inverse correlationbetween the allosteric hub score and the thermostability. Residuesthat are the strongest allosteric hubs (allosteric hub score >40,highlighted in red) show poor thermostability scores upon mutation.


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)

Allosteric hub score and local stress are plotted againstthermostabilityfor each residue position in (a, b) β1AR and (c,d) A2AR. Residues on the right side of the dotted verticalline are considered thermostable. Residues that have high allosterichub score or stress and poor thermostability are highlighted in red.The overall inverse correlation between stress and thermostabilityis shown by the regression lines in b and d.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230369&req=5

fig6: Allosteric hub score and local stress are plotted againstthermostabilityfor each residue position in (a, b) β1AR and (c,d) A2AR. Residues on the right side of the dotted verticalline are considered thermostable. Residues that have high allosterichub score or stress and poor thermostability are highlighted in red.The overall inverse correlation between stress and thermostabilityis shown by the regression lines in b and d.
Mentions: In thispaper, besides the enthalpy, we have investigated the effectof using the net stress or force on each residue and allosteric hubscore in thermostability predictions. Parts a and c of Figure 6 show the correlation between the allosteric hubscores for each residue in β1AR and A2AR crystal structures and the experimental thermostability score ofthe corresponding alanine mutant. We observe an inverse correlationbetween the allosteric hub score and the thermostability. Residuesthat are the strongest allosteric hubs (allosteric hub score >40,highlighted in red) show poor thermostability scores upon mutation.

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