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QresFEP: An Automated Protocol for Free Energy Calculations of Protein Mutations in Q
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0002-4951-9220
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Arctic Univ Norway, Univ Tromso, Dept Chem, Hylleraas Ctr Quantum Mol Sci, N-9037 Tromso, Norway.
Arctic Univ Norway, Univ Tromso, Dept Chem, Hylleraas Ctr Quantum Mol Sci, N-9037 Tromso, Norway.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0001-5578-7996
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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.

Place, publisher, year, edition, pages
2019. Vol. 15, no 10, p. 5461-5473
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-395642DOI: 10.1021/acs.jctc.9b00538ISI: 000489678700026PubMedID: 31436990OAI: oai:DiVA.org:uu-395642DiVA, id: diva2:1363025
Funder
The Research Council of Norway, 262695 274858Swedish Research CouncilKnut and Alice Wallenberg FoundationeSSENCE - An eScience CollaborationAvailable from: 2019-10-22 Created: 2019-10-22 Last updated: 2019-11-06Bibliographically approved
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)
Opponent
Supervisors
Available from: 2019-11-21 Created: 2019-10-23 Last updated: 2019-11-21

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Jespers, WillemVasile, SilvanaÅqvist, JohanGutiérrez-de-Terán, Hugo

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