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Nucleic Acids Res. 2017 Jul 3;45(W1):W408-W415. doi: 10.1093/nar/gkx399.

GPCR-SSFE 2.0-a fragment-based molecular modeling web tool for Class A G-protein coupled receptors.

Author information

1
Department of Structural Biology, Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), D-13125 Berlin, Germany.
2
Institute of Medical Physics and Biophysics, Charité-Universitätsmedizin, D-10117 Berlin, Germany.
3
Institute of Experimental Pediatric Endocrinology, Charité-Universitätsmedizin, D-13353 Berlin, Germany.
4
Institute of Medical Physics and Biophysics, Leipzig University, D-04107 Leipzig, Germany.

Abstract

G-protein coupled receptors (GPCRs) are key players in signal transduction and therefore a large proportion of pharmaceutical drugs target these receptors. Structural data of GPCRs are sparse yet important for elucidating the molecular basis of GPCR-related diseases and for performing structure-based drug design. To ameliorate this problem, GPCR-SSFE 2.0 (http://www.ssfa-7tmr.de/ssfe2/), an intuitive web server dedicated to providing three-dimensional Class A GPCR homology models has been developed. The updated web server includes 27 inactive template structures and incorporates various new functionalities. Uniquely, it uses a fingerprint correlation scoring strategy for identifying the optimal templates, which we demonstrate captures structural features that sequence similarity alone is unable to do. Template selection is carried out separately for each helix, allowing both single-template models and fragment-based models to be built. Additionally, GPCR-SSFE 2.0 stores a comprehensive set of pre-calculated and downloadable homology models and also incorporates interactive loop modeling using the tool SL2, allowing knowledge-based input by the user to guide the selection process. For visual analysis, the NGL viewer is embedded into the result pages. Finally, blind-testing using two recently published structures shows that GPCR-SSFE 2.0 performs comparably or better than other state-of-the art GPCR modeling web servers.

PMID:
28582569
PMCID:
PMC5570183
DOI:
10.1093/nar/gkx399
[Indexed for MEDLINE]
Free PMC Article

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