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Towards improved quality of GPCR models by usage of multiple templates and profile-profile comparison.

Latek D, Pasznik P, Carlomagno T, Filipek S - PLoS ONE (2013)

Bottom Line: Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low.In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate.We also provide a database of precomputed GPCR models of the human receptors from that class.

View Article: PubMed Central - PubMed

Affiliation: International Institute of Molecular and Cell Biology, Warsaw, Poland. dlatek@iimcb.gov.pl

ABSTRACT

Unlabelled: G-protein coupled receptors (GPCRs) are targets of nearly one third of the drugs at the current pharmaceutical market. Despite their importance in many cellular processes the crystal structures are available for less than 20 unique GPCRs of the Rhodopsin-like class. Fortunately, even though involved in different signaling cascades, this large group of membrane proteins has preserved a uniform structure comprising seven transmembrane helices that allows quite reliable comparative modeling. Nevertheless, low sequence similarity between the GPCR family members is still a serious obstacle not only in template selection but also in providing theoretical models of acceptable quality. An additional level of difficulty is the prediction of kinks and bulges in transmembrane helices. Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low. Here, we present GPCRM, a novel method for fast and accurate generation of GPCR models using averaging of multiple template structures and profile-profile comparison. In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate. We tested our approach on all unique GPCR structures determined to date and report its performance in comparison with other computational methods targeting the Rhodopsin-like class. We also provide a database of precomputed GPCR models of the human receptors from that class.

Availability: GPCRM SERVER AND DATABASE: http://gpcrm.biomodellab.eu.

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Multiple template modeling of A2AR.The model (green) was generated by GPCRM and is superposed on the crystal structure (blue) and templates used in the model building: the β1AR adrenergic receptor (grey) and the histamine H1R (pink). The bulge observed in TMH4 in β1AR is properly transferred to the A2AR model. Additionally, incorporation of the second template (H1R) improves the kink of TMH1 in the A2A model. The TMH4 bulge can be examined in details in pictures taken from different angles presented on the left.
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pone-0056742-g004: Multiple template modeling of A2AR.The model (green) was generated by GPCRM and is superposed on the crystal structure (blue) and templates used in the model building: the β1AR adrenergic receptor (grey) and the histamine H1R (pink). The bulge observed in TMH4 in β1AR is properly transferred to the A2AR model. Additionally, incorporation of the second template (H1R) improves the kink of TMH1 in the A2A model. The TMH4 bulge can be examined in details in pictures taken from different angles presented on the left.

Mentions: Detection of bulges and kinks in TM helices is crucial for the GPCR structure modeling. In the data set used in the study there are two examples in which we could test modeling of bulges using GPCRM. The first example is modeling of the adenosine A2A receptor (A2AR) structure (PDB id: 3EML) based on β1AR (PDB id: 2VT4) and the histamine H1 receptor (H1R) (PDB id: 3RZE). There is a small bulge in TMH4 in β1AR which is not present in the case of histamine H1R. GPCRM correctly predicts a necessary gap in the alignment (Figure S2 in Supplementary Material S1) and produces a proper deformation of TMH4 in the form of a bulge (Figure 4). Although the shape of this bulge is not exactly the same as in the crystal structure of the A2AR, because it fits the coordinates of one of the templates (β1AR), its presence preserves the rest of TMH4 from taking the wrong orientation. Nevertheless, if we used only one, the most similar template with the helical bulge inside the TMH4 (β1AR), another helix (TMH1) would be kinked in the opposite direction to that in the crystal structure of A2AR. Due to the usage of the second, less similar template (H1R) the kink direction in TMH1 had been improved (Figure 4). The second example of the proper bulge detection in the GPCRM automatic mode is modeling of the κ-opioid receptor based on the CXCR4 chemokine receptor and histamine H1R (Figures S3, S4 and S5 in Supplementary Material S1). This time a bulge was not introduced in TMH2 (although present in the H1R template) in agreement with the crystal structure of the κ-opioid receptor. Based on the above two examples of A2AR and the κ-opioid receptor we conclude that GPCRM is able to either properly introduce or remove a structural bulge in transmembrane helices due to the usage of multiple templates instead of one template structure.


Towards improved quality of GPCR models by usage of multiple templates and profile-profile comparison.

Latek D, Pasznik P, Carlomagno T, Filipek S - PLoS ONE (2013)

Multiple template modeling of A2AR.The model (green) was generated by GPCRM and is superposed on the crystal structure (blue) and templates used in the model building: the β1AR adrenergic receptor (grey) and the histamine H1R (pink). The bulge observed in TMH4 in β1AR is properly transferred to the A2AR model. Additionally, incorporation of the second template (H1R) improves the kink of TMH1 in the A2A model. The TMH4 bulge can be examined in details in pictures taken from different angles presented on the left.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585245&req=5

pone-0056742-g004: Multiple template modeling of A2AR.The model (green) was generated by GPCRM and is superposed on the crystal structure (blue) and templates used in the model building: the β1AR adrenergic receptor (grey) and the histamine H1R (pink). The bulge observed in TMH4 in β1AR is properly transferred to the A2AR model. Additionally, incorporation of the second template (H1R) improves the kink of TMH1 in the A2A model. The TMH4 bulge can be examined in details in pictures taken from different angles presented on the left.
Mentions: Detection of bulges and kinks in TM helices is crucial for the GPCR structure modeling. In the data set used in the study there are two examples in which we could test modeling of bulges using GPCRM. The first example is modeling of the adenosine A2A receptor (A2AR) structure (PDB id: 3EML) based on β1AR (PDB id: 2VT4) and the histamine H1 receptor (H1R) (PDB id: 3RZE). There is a small bulge in TMH4 in β1AR which is not present in the case of histamine H1R. GPCRM correctly predicts a necessary gap in the alignment (Figure S2 in Supplementary Material S1) and produces a proper deformation of TMH4 in the form of a bulge (Figure 4). Although the shape of this bulge is not exactly the same as in the crystal structure of the A2AR, because it fits the coordinates of one of the templates (β1AR), its presence preserves the rest of TMH4 from taking the wrong orientation. Nevertheless, if we used only one, the most similar template with the helical bulge inside the TMH4 (β1AR), another helix (TMH1) would be kinked in the opposite direction to that in the crystal structure of A2AR. Due to the usage of the second, less similar template (H1R) the kink direction in TMH1 had been improved (Figure 4). The second example of the proper bulge detection in the GPCRM automatic mode is modeling of the κ-opioid receptor based on the CXCR4 chemokine receptor and histamine H1R (Figures S3, S4 and S5 in Supplementary Material S1). This time a bulge was not introduced in TMH2 (although present in the H1R template) in agreement with the crystal structure of the κ-opioid receptor. Based on the above two examples of A2AR and the κ-opioid receptor we conclude that GPCRM is able to either properly introduce or remove a structural bulge in transmembrane helices due to the usage of multiple templates instead of one template structure.

Bottom Line: Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low.In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate.We also provide a database of precomputed GPCR models of the human receptors from that class.

View Article: PubMed Central - PubMed

Affiliation: International Institute of Molecular and Cell Biology, Warsaw, Poland. dlatek@iimcb.gov.pl

ABSTRACT

Unlabelled: G-protein coupled receptors (GPCRs) are targets of nearly one third of the drugs at the current pharmaceutical market. Despite their importance in many cellular processes the crystal structures are available for less than 20 unique GPCRs of the Rhodopsin-like class. Fortunately, even though involved in different signaling cascades, this large group of membrane proteins has preserved a uniform structure comprising seven transmembrane helices that allows quite reliable comparative modeling. Nevertheless, low sequence similarity between the GPCR family members is still a serious obstacle not only in template selection but also in providing theoretical models of acceptable quality. An additional level of difficulty is the prediction of kinks and bulges in transmembrane helices. Usage of multiple templates and generation of alignments based on sequence profiles may increase the rate of success in difficult cases of comparative modeling in which the sequence similarity between GPCRs is exceptionally low. Here, we present GPCRM, a novel method for fast and accurate generation of GPCR models using averaging of multiple template structures and profile-profile comparison. In particular, GPCRM is the first GPCR structure predictor incorporating two distinct loop modeling techniques: Modeller and Rosetta together with the filtering of models based on the Z-coordinate. We tested our approach on all unique GPCR structures determined to date and report its performance in comparison with other computational methods targeting the Rhodopsin-like class. We also provide a database of precomputed GPCR models of the human receptors from that class.

Availability: GPCRM SERVER AND DATABASE: http://gpcrm.biomodellab.eu.

Show MeSH