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Characterization of Ligand Binding to GPCRs Through Computational Methods.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.ORCID iD: 0000-0001-5578-7996
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.ORCID iD: 0000-0002-4951-9220
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
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2018 (English)In: Computational Methods for GPCR Drug Discovery, New York, NY: Humana Press, 2018, Vol. 1705, p. 23-44Chapter in book (Refereed)
Abstract [en]

The recent increase in available G protein-coupled receptor structures now contributes decisively to the structure-based ligand design. In this context, computational approaches in combination with medicinal chemistry and pharmacology are extremely helpful. Here, we provide an update on our structure-based computational protocols, used to answer key questions related to GPCR-ligand binding. All combined, these techniques can shed light on ligand binding modes, determine the molecular basis of conformational selection, for agonists and antagonists, as well as of subtype selectivity. To illustrate each of these questions, we will consider examples from existing projects on three families of class A (rhodopsin-like) GPCRs: one small-molecule (nucleotide-like) family, i.e., the adenosine receptors, and two peptide-binding receptors: neuropeptide-Y and angiotensin II receptors. The successful application of the same computational protocols to investigate this diverse group of receptor families gives an idea of the general applicability of our methodology in the characterization of GPCR-ligand binding.

Place, publisher, year, edition, pages
New York, NY: Humana Press, 2018. Vol. 1705, p. 23-44
Keywords [en]
Free energy perturbation, Homology modeling, Molecular dynamics, Structure-based drug design
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-395745DOI: 10.1007/978-1-4939-7465-8_2PubMedID: 29188557Libris ID: 22107626OAI: oai:DiVA.org:uu-395745DiVA, id: diva2:1365143
Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2020-01-22Bibliographically approved

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Vasile, SilvanaEsguerra, MauricioJespers, WillemOliveira, AnaSallander, JessicaÅqvist, JohanGutiérrez-de-Terán, Hugo

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Vasile, SilvanaEsguerra, MauricioJespers, WillemOliveira, AnaSallander, JessicaÅqvist, JohanGutiérrez-de-Terán, Hugo
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