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Combining docking, molecular dynamics and the linear interaction energy method to predict binding modes and affinities for non-nucleoside inhibitors to HIV-1 reverse transcriptase
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
2008 (English)In: Journal of Medicinal Chemistry, ISSN 0022-2623, Vol. 51, no 9, 2648-56 p.Article in journal (Refereed) Published
Abstract [en]

Docking, scoring, molecular dynamics (MD), and the linear interaction energy (LIE) method are used here to predict binding modes and affinities for a set of 43 non-nucleoside inhibitors to HIV-1 reverse transcriptase. Starting from a crystallographic structure, the binding modes of 43 inhibitors are predicted using automated docking. The Goldscore scoring function and the LIE method are then used to determine the relative binding free energies for the inhibitors. The Goldscore scoring function does not reproduce the relative binding affinities for the inhibitors, while the standard parametrization of the LIE method reproduces the experimental binding free energies for 39 inhibitors with an R (2) = 0.70 and an unsigned average error of 0.8 kcal/mol. The present calculations provide a validation of the combination of docking, MD, and LIE as a powerful tool in structure-based drug design, and the methodology is easily scalable for attaining a higher throughput of compounds.

Place, publisher, year, edition, pages
2008. Vol. 51, no 9, 2648-56 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-97214DOI: 10.1021/jm7012198ISI: 000255500000010PubMedID: 18410085OAI: oai:DiVA.org:uu-97214DiVA: diva2:172048
Available from: 2008-04-29 Created: 2008-04-29 Last updated: 2014-01-24Bibliographically approved
In thesis
1. Challenges in Computational Biochemistry: Solvation and Ligand Binding
Open this publication in new window or tab >>Challenges in Computational Biochemistry: Solvation and Ligand Binding
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Accurate calculations of free energies for molecular association and solvation are important for the understanding of biochemical processes, and are useful in many pharmaceutical applications. In this thesis, molecular dynamics (MD) simulations are used to calculate thermodynamic properties for solvation and ligand binding.

The thermodynamic integration technique is used to calculate pKa values for three aspartic acid residues in two different proteins. MD simulations are carried out in explicit and Generalized-Born continuum solvent. The calculated pKa values are in qualitative agreement with experiment in both cases. A combination of MD simulations and a continuum electrostatics method is applied to examine pKa shifts in wild-type and mutant epoxide hydrolase. The calculated pKa values support a model that can explain some of the pH dependent properties of this enzyme.

Development of the linear interaction energy (LIE) method for calculating solvation and binding free energies is presented. A new model for estimating the electrostatic term in the LIE method is derived and is shown to reproduce experimental free energies of hydration. An LIE method based on a continuum solvent representation is also developed and it is shown to reproduce binding free energies for inhibitors of a malaria enzyme. The possibility of using a combination of docking, MD and the LIE method to predict binding affinities for large datasets of ligands is also investigated. Good agreement with experiment is found for a set of non-nucleoside inhibitors of HIV-1 reverse transcriptase.

Approaches for decomposing solvation and binding free energies into enthalpic and entropic components are also examined. Methods for calculating the translational and rotational binding entropies for a ligand are presented. The possibility to calculate ion hydration free energies and entropies for alkali metal ions by using rigorous free energy techniques is also investigated and the results agree well with experimental data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 62 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 432
Keyword
Molecular biology, computer simulations, molecular dynamics, solvation free energy, Generalized-Born, Poisson-Boltzmann, ligand binding, binding free energy, linear interaction energy, binding entropy, hydration entropy, Molekylärbiologi
Identifiers
urn:nbn:se:uu:diva-8738 (URN)978-91-554-7200-9 (ISBN)
Public defence
2008-05-23, B7:101, BMC, Husargatan 3, Uppsala, 13:15
Opponent
Supervisors
Available from: 2008-04-29 Created: 2008-04-29Bibliographically approved
2. 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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