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Vasile, S. (2019). Computational prediction of ligand binding in peptide G-protein coupled receptors. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
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)
Opponent
Supervisors
Available from: 2019-11-21 Created: 2019-10-23 Last updated: 2019-11-21
Shebanits, K., Vasile, S., Xu, B., Gutiérrez-de-Terán, H. & Larhammar, D. (2019). Functional characterization in vitro of twelve naturally occurring variants of the human pancreatic polypeptide receptor NPY4R. Neuropeptides, 76, Article ID 101933.
Open this publication in new window or tab >>Functional characterization in vitro of twelve naturally occurring variants of the human pancreatic polypeptide receptor NPY4R
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2019 (English)In: Neuropeptides, ISSN 0143-4179, E-ISSN 1532-2785, Vol. 76, article id 101933Article in journal (Refereed) Published
Abstract [en]

Obesity has become a global health problem and therefore understanding of the mechanisms regulating hunger and satiety is of utmost importance for the development of new treatment strategies. The Y4 receptor, encoded by the NPY4R gene, and its ligand pancreatic polypeptide (PP) have been reported to mediate a satiety signal. Multiple genetic studies have reported an association between NPY4R copy number and body weight. The gene also displays several SNP variants, many of which lead to amino acid differences, making it interesting to study. We have investigated the functional properties of 12 naturally occurring amino acid sequence variants of the Y4 and interpret the results in relation to sequence conservation and our structural model of the human Y4 receptor protein. Three receptor variants, Cys201ECL2Tyr, Val2716.41Leu and Asn3187.49Asp, were found to completely lose functional response, measured as inositol phosphate turnover, while retaining membrane expression. They display high sequence conservation and have important roles in the receptor structure. For two receptor variants the potency of PP was significantly decreased, Cys34NTSer (EC50 = 2.9 nM, p < .001) and Val1353.46Met (EC50 = 3.0 nM, p < .01), compared to wild-type Y4 (EC50 = 0.68 nM). Cys34 forms a disulphide bond with Cys298, linking the N-terminal part to ECL3. The Val1353.46Met variant has an amino acid replacement located in the TM3 helix, one helix turn above the highly conserved ERH motif. This position has influence on the network of residues involved in receptor activation and subsequent inactivation. Sequence conservation and the structural model are consistent with these results. The remaining seven positions had no significant effect on the receptor's functional response compared to wild-type Y4. These positions display more variation during evolution. Understanding of the interactions between the Y4 receptor and its native PP agonist and the effects of amino acid variation on its functional response will hopefully lead to future therapeutic possibilities.

Keywords
Y4, SNP, Mutagenesis, Functional pharmacology, Structural modelling
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-356572 (URN)10.1016/j.npep.2019.05.004 (DOI)000482249700004 ()31230758 (PubMedID)
Funder
Swedish Research Council, K2013-55 x -22189-01-2The Swedish Brain Foundation, F02016-0217
Available from: 2018-08-01 Created: 2018-08-01 Last updated: 2019-12-06Bibliographically approved
Jespers, W., Isaksen, G. V., Andberg, T. A., Vasile, S., van Veen, A., Åqvist, J., . . . Gutiérrez-de-Terán, H. (2019). QresFEP: An Automated Protocol for Free Energy Calculations of Protein Mutations in Q. Journal of Chemical Theory and Computation, 15(10), 5461-5473
Open this publication in new window or tab >>QresFEP: An Automated Protocol for Free Energy Calculations of Protein Mutations in Q
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2019 (English)In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 15, no 10, p. 5461-5473Article in journal (Refereed) Published
Abstract [en]

Predicting the effect of single-point mutations on protein stability or protein-ligand binding is a major challenge in computational biology. Free energy calculations constitute the most rigorous approach to this problem, though the estimation of converged values for amino acid mutations remains challenging. To overcome this limitation, we developed tailored protocols to calculate free energy shifts associated with single-point mutations. We herein describe the QresFEP protocol, which includes an extension of our recent protocols to cover all amino acids mutations, based on the latest versions of the OPLS-AA force field. QresFEP is implemented in an application programming interface framework and the graphic interface QGui, for the molecular dynamics software Q. The complete protocol is benchmarked in several model systems, optimizing a number of sampling parameters and the implementation of Zwanzig's exponential formula and Bennet's acceptance ratio methods. QresFEP shows an excellent performance on estimating the hydration free energies of amino acid side-chain mimics, including their charged analogues. We also examined its performance on a protein-ligand binding problem of pharmaceutical relevance, the antagonism of neuropeptide Y1 G protein-coupled receptor. Here, the calculations show very good agreement with the experimental effect of 16 mutations on the binding of antagonists BIBP3226, in line with our recent applications in this field. Finally, the characterization of 43 mutations of T4-lysozyme reveals the capacity of our protocol to assess variations of the thermal stability of proteins, achieving a similar performance to alternative free energy perturbation (FEP) approaches. In summary, QresFEP is a robust, versatile, and user-friendly computational FEP protocol to examine biochemical effects of single-point mutations with high accuracy.

National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-395642 (URN)10.1021/acs.jctc.9b00538 (DOI)000489678700026 ()31436990 (PubMedID)
Funder
The Research Council of Norway, 262695 274858Swedish Research CouncilKnut and Alice Wallenberg FoundationeSSENCE - An eScience Collaboration
Available from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-11-06Bibliographically approved
Vasile, S., Esguerra, M., Jespers, W., Oliveira, A., Sallander, J., Åqvist, J. & Gutiérrez-de-Terán, H. (2018). Characterization of Ligand Binding to GPCRs Through Computational Methods.. In: Computational Methods for GPCR Drug Discovery: (pp. 23-44). New York, NY: Humana Press, 1705
Open this publication in new window or tab >>Characterization of Ligand Binding to GPCRs Through Computational Methods.
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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
Keywords
Free energy perturbation, Homology modeling, Molecular dynamics, Structure-based drug design
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-395745 (URN)10.1007/978-1-4939-7465-8_2 (DOI)29188557 (PubMedID)
Available from: 2019-10-23 Created: 2019-10-23 Last updated: 2020-01-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5578-7996

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