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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Main Authors: Lars Boukharta, Hugo Gutiérrez-de-Terán, Johan Aqvist
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-04-01
Series:PLoS Computational Biology
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24743773/?tool=EBI
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spelling 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
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AT johanaqvist computationalpredictionofalaninescanningandligandbindingenergeticsingproteincoupledreceptors
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