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Boukharta, Lars
Publications (4 of 4) Show all publications
Boukharta, L. (2014). Computational Modelling of Ligand Complexes with G-Protein Coupled Receptors, Ion Channels and Enzymes. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
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. p. 61
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1105
Keywords
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
Boukharta, L., Gutierréz de Terán, H. & Åqvist, J. (2014). Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors. PloS Computational Biology, 10(4), e1003585
Open this publication in new window or tab >>Computational prediction of alanine scanning and ligand binding energetics in G-protein coupled receptors
2014 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 4, p. e1003585-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.

National Category
Bioinformatics (Computational Biology) Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-212102 (URN)10.1371/journal.pcbi.1003585 (DOI)000336507500014 ()
Available from: 2013-12-05 Created: 2013-12-05 Last updated: 2018-01-11Bibliographically approved
Shamsudin Khan, Y., Gutiérrez de Terán, H., Boukharta, L. & Åqvist, J. (2014). Toward an Optimal Docking and Free Energy Calculation Scheme in Ligand Design with Application to COX-1 Inhibitors. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 54(5), 1488-1499
Open this publication in new window or tab >>Toward an Optimal Docking and Free Energy Calculation Scheme in Ligand Design with Application to COX-1 Inhibitors
2014 (English)In: JOURNAL OF CHEMICAL INFORMATION AND MODELING, ISSN 1549-9596, Vol. 54, no 5, p. 1488-1499Article in journal (Refereed) Published
Abstract [en]

Cyclooxygenase-1 (COX-1) is one of the main targets of most pain-relieving pharmaceuticals. Although the enzyme is well characterized, it is known to be a difficult target for automated molecular docking and scoring. We collected from the literature a structurally diverse set of 45 nonsteroidal anti-inflammatory drugs (NSAIDs) and COX-2-selective inhibitors (coxibs) with a wide range of binding affinities for COX-1. The binding of this data set to a homology model of human COX-1 was analyzed with different combinations of molecular docking algorithms, scoring functions, and the linear interaction energy (LIE) method for estimating binding affinities. It is found that the computational protocols for estimation of binding affinities are extremely sensitive to the initial orientations of the ligands in the binding pocket. To overcome this limitation, we propose a systematic exploration of docking poses using the LIE calculations as a postscoring function. This scheme yields predictions in excellent agreement with experiment, with a mean unsigned error of 0.9 kcal/mol for binding free energies and structures of high quality. A significant improvement of the results is also seen when averaging over experimental data from several independent measurements.

National Category
Bioinformatics and Systems Biology
Research subject
Biology with specialization in Molecular Biotechnology
Identifiers
urn:nbn:se:uu:diva-225268 (URN)10.1021/ci500151f (DOI)000336637400019 ()
Available from: 2014-05-29 Created: 2014-05-29 Last updated: 2017-08-23Bibliographically approved
Xu, B., Fällmar, H., Boukharta, L., Pruner, J., Lundell, I., Mohell, N., . . . Larhammar, D. (2013). Mutagenesis and Computational Modeling of Human G‑Protein-Coupled Receptor Y2 for Neuropeptide Y and Peptide YY. Biochemistry, 52(45), 7987-7998
Open this publication in new window or tab >>Mutagenesis and Computational Modeling of Human G‑Protein-Coupled Receptor Y2 for Neuropeptide Y and Peptide YY
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2013 (English)In: Biochemistry, ISSN 0006-2960, E-ISSN 1520-4995, Vol. 52, no 45, p. 7987-7998Article in journal (Refereed) Published
Abstract [en]

Neuropeptide Y and peptide YY receptor type 2 (Y2) is involved in appetite regulation and several other physiological processes. We have investigated the structure of the human Y2 receptor. Computational modeling of receptor–agonist interactions was used as a guide to design a series of receptor mutants, followed by binding assays using full-length and truncated peptide agonists and the Y2-specific antagonist BIIE0246. Our model suggested a hydrogen bond network among highly conserved residues Thr2.61, Gln3.32, and His7.39, which could play roles in ligand binding and/or receptor structure. In addition, the C-terminus of the peptide could make contact with residues Tyr5.38 and Leu6.51. Mutagenesis of all these positions, followed by binding assays, provides experimental support for our computational model: most of the mutants for the residues forming the proposed hydrogen bond network displayed reduced peptide agonist affinities as well as reduced hPYY3-36 potency in a functional assay. The Ala and Leu mutants of Gln3.32 and His7.39 disrupted membrane expression of the receptor. Combined with the modeling, the experimental results support roles for these hydrogen bond network residues in peptide binding as well as receptor architecture. The reduced agonist affinity for mutants of Tyr5.38 and Leu6.51 supports their role in a binding pocket surrounding the invariant tyrosine at position 36 of the peptide ligands. The results for antagonist BIIE0246 suggest several differences in interactions compared to those of the peptides. Our results lead to a new structural model for NPY family receptors and peptide binding.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2013
National Category
Natural Sciences Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-154994 (URN)10.1021/bi400830c (DOI)000330017700012 ()
Available from: 2011-08-04 Created: 2011-06-14 Last updated: 2019-01-03Bibliographically approved
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