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

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-base...

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Autores principales: Worth, Catherine L., Kreuchwig, Franziska, Tiemann, Johanna K.S., Kreuchwig, Annika, Ritschel, Michele, Kleinau, Gunnar, Hildebrand, Peter W., Krause, Gerd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570183/
https://www.ncbi.nlm.nih.gov/pubmed/28582569
http://dx.doi.org/10.1093/nar/gkx399
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author Worth, Catherine L.
Kreuchwig, Franziska
Tiemann, Johanna K.S.
Kreuchwig, Annika
Ritschel, Michele
Kleinau, Gunnar
Hildebrand, Peter W.
Krause, Gerd
author_facet Worth, Catherine L.
Kreuchwig, Franziska
Tiemann, Johanna K.S.
Kreuchwig, Annika
Ritschel, Michele
Kleinau, Gunnar
Hildebrand, Peter W.
Krause, Gerd
author_sort Worth, Catherine L.
collection PubMed
description 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.
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spelling pubmed-55701832017-08-29 GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors Worth, Catherine L. Kreuchwig, Franziska Tiemann, Johanna K.S. Kreuchwig, Annika Ritschel, Michele Kleinau, Gunnar Hildebrand, Peter W. Krause, Gerd Nucleic Acids Res Web Server Issue 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. Oxford University Press 2017-07-03 2017-06-05 /pmc/articles/PMC5570183/ /pubmed/28582569 http://dx.doi.org/10.1093/nar/gkx399 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Worth, Catherine L.
Kreuchwig, Franziska
Tiemann, Johanna K.S.
Kreuchwig, Annika
Ritschel, Michele
Kleinau, Gunnar
Hildebrand, Peter W.
Krause, Gerd
GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title_full GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title_fullStr GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title_full_unstemmed GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title_short GPCR-SSFE 2.0—a fragment-based molecular modeling web tool for Class A G-protein coupled receptors
title_sort gpcr-ssfe 2.0—a fragment-based molecular modeling web tool for class a g-protein coupled receptors
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570183/
https://www.ncbi.nlm.nih.gov/pubmed/28582569
http://dx.doi.org/10.1093/nar/gkx399
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