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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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Comparison of various methods for the alignment generation in GPCRM.Here, we plotted ClustalW2 identity scores versus the alignment accuracy (the upper plot) or versus the difference between the accuracy provided by profile-profile alignment and PSA or MSA (the lower plot). The ClustalW2 score and PDB id for both the target and template proteins are provided on the right panel.
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pone-0056742-g003: Comparison of various methods for the alignment generation in GPCRM.Here, we plotted ClustalW2 identity scores versus the alignment accuracy (the upper plot) or versus the difference between the accuracy provided by profile-profile alignment and PSA or MSA (the lower plot). The ClustalW2 score and PDB id for both the target and template proteins are provided on the right panel.

Mentions: As it is shown in Figure 3 (the upper part), the most accurate alignment was produced by a profile-profile comparison. Also the bottom part of Figure 3 clearly shows that the alignment based on either PSA or MSA, as implemented for example in GPCR-Modsim, can be significantly improved by the usage of sequence profiles and the ‘anchored realignment’ step. Nevertheless, a substantial improvement was observed mostly in the area of low sequence identity. Decreased accuracy in the case of high sequence identity can be explained by the fact that additional homologous sequences in the profiles might simply introduce a background noise. Such observations agree with earlier studies on the usage of sequence profiles [23]–[25]. Nevertheless, when the sequence identity was high (over 34% and 60% - see Figure 3), the most accurate alignment (PSA) was easily selected using the GPCRM alignment scoring scheme.


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)

Comparison of various methods for the alignment generation in GPCRM.Here, we plotted ClustalW2 identity scores versus the alignment accuracy (the upper plot) or versus the difference between the accuracy provided by profile-profile alignment and PSA or MSA (the lower plot). The ClustalW2 score and PDB id for both the target and template proteins are provided on the right panel.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0056742-g003: Comparison of various methods for the alignment generation in GPCRM.Here, we plotted ClustalW2 identity scores versus the alignment accuracy (the upper plot) or versus the difference between the accuracy provided by profile-profile alignment and PSA or MSA (the lower plot). The ClustalW2 score and PDB id for both the target and template proteins are provided on the right panel.
Mentions: As it is shown in Figure 3 (the upper part), the most accurate alignment was produced by a profile-profile comparison. Also the bottom part of Figure 3 clearly shows that the alignment based on either PSA or MSA, as implemented for example in GPCR-Modsim, can be significantly improved by the usage of sequence profiles and the ‘anchored realignment’ step. Nevertheless, a substantial improvement was observed mostly in the area of low sequence identity. Decreased accuracy in the case of high sequence identity can be explained by the fact that additional homologous sequences in the profiles might simply introduce a background noise. Such observations agree with earlier studies on the usage of sequence profiles [23]–[25]. Nevertheless, when the sequence identity was high (over 34% and 60% - see Figure 3), the most accurate alignment (PSA) was easily selected using the GPCRM alignment scoring scheme.

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