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Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. (Uppsala RNA Research Centre - URRC)
2014 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 4, e1003585- p.Article in journal (Refereed) Published
Abstract [en]

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. Author Summary G-protein coupled receptors constitute a family of drug targets of outstanding interest, with more than 30% of the marketed drugs targeting a GPCR. The combination of site-directed mutagenesis, biochemical experiments and computationally generated 3D structural models has traditionally been used to investigate these receptors. The increasing number of GPCR crystal structures now paves the way for detailed characterization of receptor-ligand interactions and energetics using advanced computer simulations. Here, we present an accurate computational scheme to predict and interpret the effects of alanine scanning experiments, based on molecular dynamics free energy simulations. We apply the technique to antagonist binding to the neuropeptide Y receptor Y1, the structure of which is still unknown. A structural model of a Y1-antagonist complex was derived and used as starting point for computational characterization of the effects on binding of alanine substitutions at thirteen different receptor positions. Further, we used the model and computational scheme to predict the binding of a series of seven antagonist analogs. The results are in excellent agreement with available experimental data and provide validation of both the methodology and structural models of the complexes.

Place, publisher, year, edition, pages
2014. Vol. 10, no 4, e1003585- p.
National Category
Bioinformatics (Computational Biology) Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-212102DOI: 10.1371/journal.pcbi.1003585ISI: 000336507500014OAI: oai:DiVA.org:uu-212102DiVA: diva2:676224
Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Computational Modelling of Ligand Complexes with G-Protein Coupled Receptors, Ion Channels and Enzymes
Open this publication in new window or tab >>Computational Modelling of Ligand Complexes with G-Protein Coupled Receptors, Ion Channels and Enzymes
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Accurate predictions of binding free energies from computer simulations are an invaluable resource for understanding biochemical processes and drug action. The primary aim of the work described in the thesis was to predict and understand ligand binding to several proteins of major pharmaceutical importance using computational methods.

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 G-protein coupled receptor and a 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. Site-directed mutagenesis, homology modelling and docking were further used to characterize agonist binding to the human neuropeptide Y2 receptor, which is important in feeding behavior and an obesity drug target.  In a separate project, homology modelling was also used for rationalization of mutagenesis data for an integron integrase involved in antibiotic resistance.

Blockade of the hERG potassium channel by various drug-like compounds, potentially causing serious cardiac side effects, is a major problem in drug development. We have used a homology model of hERG to conduct molecular docking experiments with a series of channel blockers, followed by molecular dynamics simulations of the complexes and evaluation of binding free energies with the linear interaction energy method. The calculations are in good agreement with experimental binding affinities and allow for a rationalization of three-dimensional structure-activity relationships with implications for design of new compounds. Docking, scoring, molecular dynamics, and the linear interaction energy method were also used to predict binding modes and affinities for a large set of inhibitors to HIV-1 reverse transcriptase. Good agreement with experiment was found and the work provides a validation of the methodology as a powerful tool in structure-based drug design. It is also easily scalable for higher throughput of compounds.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 61 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1105
Keyword
computer simulations, molecular dynamics, ligand binding, free energy perturbation, linear interaction energy, binding free energy, homology modelling, structure prediction, alanine scanning, site-directed mutagenesis, hERG, GPCR, neuropeptide Y, HIV-1 reverse transcriptase, integron integrase
National Category
Theoretical Chemistry Structural Biology Biochemistry and Molecular Biology
Research subject
Molecular Biotechnology
Identifiers
urn:nbn:se:uu:diva-212103 (URN)978-91-554-8823-9 (ISBN)
Public defence
2014-01-31, B42, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-01-10 Created: 2013-12-05 Last updated: 2014-01-24

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Boukharta, LarsGutierréz de Terán, HugoÅqvist, Johan

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