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Evolution of Angiotensin Peptides and Peptidomimetics as AT2 Receptor Agonists
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University.ORCID iD: 0000-0001-5578-7996
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. eThe Beijer Laboratory, Department of Pharmaceutical Biosciences, Division of Biological Research on Drug Dependence, BMC, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

The biological functions of Angiotensin II (Ang II), a central peptide in the Renin-Angiotensin System (RAS), are mediated by two G-protein coupled receptors, AT1R and AT2R. Activation of AT1R by Ang II elicits strong hypertensive effects and inhibitors of the enzymes responsible for the synthesis of Ang II, as well as compounds that act as antagonists at AT1R, have served important targets for drug intervention. On the other hand, AT2R is upregulated in events of tissue damage and its activation by Ang II results in opposite consequences to AT1R activation, i.e. vasodilation, anti-fibrotic and anti-inflammatory effects. Hence, in recent years AT2R emerged as a promising drug target. The first drug-like selective AT2R agonist C21 was discovered by our group and is in Phase II clinical trials for idiopathic pulmonary fibrosis (IPF). Herein, the chemical evolution of AT2R peptide agonists was studied by identification of pharmacophoric points, bioactive conformations and key interactions with the receptor. Eleven important peptides and peptidomimetics, with different structure, affinity and selectivity, were selected. All compounds, including sarile, an Ang II analogue, were previously confirmed to act as AT2R agonists. Based on the recently released crystal structures of AT1R and AT2R in complex with sarile (which acts as a partial agonist at AT1R), we proposed a common binding model for the series of peptides. The binding modes were analysed by means of molecular docking and Molecular Dynamics simulations, and further explanation of structure-activity relationships was achieved by binding affinity predictions with Free Energy Perturbation calculations on short peptides. In light of the long-term goal of designing potent and AT2R selective drugs with long duration in vivo, the calculations on C21 can be used to cover the gap between peptides and drug-like AT2R agonists.  

National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-395742OAI: oai:DiVA.org:uu-395742DiVA, id: diva2:1365132
Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2019-11-07
In thesis
1. Computational prediction of ligand binding in peptide G-protein coupled receptors
Open this publication in new window or tab >>Computational prediction of ligand binding in peptide G-protein coupled receptors
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

G-protein coupled receptors (GPCRs) are a superfamily of membrane receptors involved in a wide variety of biological processes, and their malfunction is associated with many diseases. Consequently, GPCRs are targeted by one-third of the drugs on the market, and constitute the focus of active public and private research in the search of more effective drugs. The GPCR families that are activated by endogenous peptides are particularly challenging for the drug design process, which in this case contemplates peptides, peptidomimetics and small molecules, as selective activators (agonists) or blockers (antagonists) of the particular receptor subtype of interest. This process benefits of a detailed understanding of how known ligands bind to the receptors. Homology modelling, molecular dynamics (MD) and free energy perturbation (FEP) are computational methods used to predict binding modes and binding affinities. In this thesis, these techniques are applied (and even further developed) in combination with novel experimental data provided by our collaborators, in order to elucidate the molecular determinants of endogenous peptide ligands, analogues and mimetics to two families of peptide-binding receptors: the neuropeptide Y (NPY) and the Angiotensin II receptors.

The NPY signaling system is responsible for the regulation of food intake and its malfunction is connected to obesity, a risk factor for diseases such as diabetes and cancer. In this thesis, we focused on the elucidation of the binding mode of endogenous peptide ligands and studied the structural effect of receptor mutants, with the aim of helping in future drug design on the Y2 receptor subtype, as well as understanding the effect of receptor polymorphisms on the Y4 subtype. We further used this system to refine and test our computational protocol for the prediction of binding free energies, by characterizing the binding mode of a peptidomimetic antagonist to the Y1 receptor.

The AT2 receptor is another interesting drug target, as its activation by the Angiotensin II peptide elicits responses that counterbalance the hypertensive effects caused by activation of the AT1 receptor by the same ligand. Moreover, AT2 is upregulated in events of tissue damage. We characterized the chemical evolution of peptide and peptidomimetic agonists at this receptor, with the aim to identify a set of pharmacophoric points and key interactions with AT2. The outcome of this study allowed the establishment of a clear explanation of structure-activity relationships, and will be the starting point for further ligand-design efforts at this receptor.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 59
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1875
Keywords
GPCR, neuropeptide Y, angiotensin II receptor, molecular dynamics, free energy perturbation, homology modelling, computer simulations, peptide binding, peptidomimetics, binding free energy.
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-395761 (URN)978-91-513-0796-1 (ISBN)
Public defence
2019-12-12, B22, BMC, Husargatan 3, Uppsala, 13:00 (English)
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Supervisors
Available from: 2019-11-21 Created: 2019-10-23 Last updated: 2019-11-21

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