What can we learn from molecular dynamics simulations for GPCR drug design?
Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing...
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doaj-99b301ba997646b79097adbda8dda7d02020-11-24T20:59:07ZengElsevierComputational and Structural Biotechnology Journal2001-03702015-01-0113C11112110.1016/j.csbj.2014.12.002What can we learn from molecular dynamics simulations for GPCR drug design?Christofer S. TautermannDaniel SeeligerJan M. KrieglRecent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).http://www.sciencedirect.com/science/article/pii/S200103701400052XMolecular dynamics simulationsGPCRHomology modelingWater networkCC chemokine receptor 3Muscarinic acetylcholine receptor 3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Christofer S. Tautermann Daniel Seeliger Jan M. Kriegl |
spellingShingle |
Christofer S. Tautermann Daniel Seeliger Jan M. Kriegl What can we learn from molecular dynamics simulations for GPCR drug design? Computational and Structural Biotechnology Journal Molecular dynamics simulations GPCR Homology modeling Water network CC chemokine receptor 3 Muscarinic acetylcholine receptor 3 |
author_facet |
Christofer S. Tautermann Daniel Seeliger Jan M. Kriegl |
author_sort |
Christofer S. Tautermann |
title |
What can we learn from molecular dynamics simulations for GPCR drug design? |
title_short |
What can we learn from molecular dynamics simulations for GPCR drug design? |
title_full |
What can we learn from molecular dynamics simulations for GPCR drug design? |
title_fullStr |
What can we learn from molecular dynamics simulations for GPCR drug design? |
title_full_unstemmed |
What can we learn from molecular dynamics simulations for GPCR drug design? |
title_sort |
what can we learn from molecular dynamics simulations for gpcr drug design? |
publisher |
Elsevier |
series |
Computational and Structural Biotechnology Journal |
issn |
2001-0370 |
publishDate |
2015-01-01 |
description |
Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R). |
topic |
Molecular dynamics simulations GPCR Homology modeling Water network CC chemokine receptor 3 Muscarinic acetylcholine receptor 3 |
url |
http://www.sciencedirect.com/science/article/pii/S200103701400052X |
work_keys_str_mv |
AT christoferstautermann whatcanwelearnfrommoleculardynamicssimulationsforgpcrdrugdesign AT danielseeliger whatcanwelearnfrommoleculardynamicssimulationsforgpcrdrugdesign AT janmkriegl whatcanwelearnfrommoleculardynamicssimulationsforgpcrdrugdesign |
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1716783739009236992 |