Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors.
Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand bindi...
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2014-04-01
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doaj-ad9c8b7c7b9a4a5eb841bf423e044df62021-04-21T15:36:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582014-04-01104e100358510.1371/journal.pcbi.1003585Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors.Lars BoukhartaHugo Gutiérrez-de-TeránJohan AqvistSite-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand binding modes. However, systematic approaches for accurate calculations of the corresponding binding free energies are still lacking. Here, we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24743773/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lars Boukharta Hugo Gutiérrez-de-Terán Johan Aqvist |
spellingShingle |
Lars Boukharta Hugo Gutiérrez-de-Terán Johan Aqvist Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. PLoS Computational Biology |
author_facet |
Lars Boukharta Hugo Gutiérrez-de-Terán Johan Aqvist |
author_sort |
Lars Boukharta |
title |
Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. |
title_short |
Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. |
title_full |
Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. |
title_fullStr |
Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. |
title_full_unstemmed |
Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. |
title_sort |
computational prediction of alanine scanning and ligand binding energetics in g-protein coupled receptors. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2014-04-01 |
description |
Site-directed mutagenesis combined with binding affinity measurements is widely used to probe the nature of ligand interactions with GPCRs. Such experiments, as well as structure-activity relationships for series of ligands, are usually interpreted with computationally derived models of ligand binding modes. However, systematic approaches for accurate calculations of the corresponding binding free energies are still lacking. Here, we report a computational strategy to quantitatively predict the effects of alanine scanning and ligand modifications based on molecular dynamics free energy simulations. A smooth stepwise scheme for free energy perturbation calculations is derived and applied to a series of thirteen alanine mutations of the human neuropeptide Y1 receptor and series of eight analogous antagonists. The robustness and accuracy of the method enables univocal interpretation of existing mutagenesis and binding data. We show how these calculations can be used to validate structural models and demonstrate their ability to discriminate against suboptimal ones. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24743773/?tool=EBI |
work_keys_str_mv |
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_version_ |
1714667307248123904 |