Comparative Analysis of Putative Agonist‐Binding Modes in the Human A<sub>1</sub> Adenosine Receptor

Gutiérrez‐de‐Terán, Hugo; Pastor, Manuel; Centeno, Nuría B.; Åqvist, Johan; Sanz, Ferrán

doi:10.1002/cbic.200300817

Cited by 12 publications

(11 citation statements)

References 52 publications

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“…(1) is just a constant that cannot be concerned with the relative translational and rotational entropic terms to binding and that such contributions must be parameterized into the α parameter. Alternative, these authors have demonstrated that the α parameter is given by the slopes of the linear relationship found in water and receptors, between the ΔΔG nonpolar or <V vdw l‐s > energies and the number of heavy atoms for 25 different ligands,15, 16, 18, 22, 23, 51 while the γ correction term is related to their intercepts at zero ligand size 5, 6, 45…”

Section: Resultsmentioning

confidence: 99%

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

et al. 2010

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The standard parameterization of the Linear Interaction Energy (LIE) method has been applied with quite good results to reproduce the experimental absolute binding free energies for several protein-ligand systems. However, we found that this parameterization failed to reproduce the experimental binding free energy of Plasmepsin II (PlmII) in complexes with inhibitors belonging to four dissimilar scaffolds. To overcome this fact, we developed three approaches of LIE, which combine systematic approaches to predict the inhibitor-specific values of α, β, and γ parameters, to gauge their ability to calculate the absolute binding free energies for these PlmII-Inhibitor complexes. Specifically: (i) we modified the linear relationship between the weighted nonpolar desolvation ratio (WNDR) and the α parameter, by introducing two models of the β parameter determined by the free energy perturbation (FEP) method in the absence of the constant term γ, and (ii) we developed a new parameterization model to investigate the linear correlation between WNDR and the correction term γ. Using these parameterizations, we were able to reproduce the experimental binding free energy from these systems with mean absolute errors lower than 1.5 kcal/mol.

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Section: Resultsmentioning

confidence: 99%

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

et al. 2010

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“…Using fixed values for and , while optimizing , not only provided the best absolute binding free energies for the ligands but also showed that the coefficients of the LIE method are independent of the force field used and that only might need to be optimized to account for the hydrophobicity of the active site. Gutierrez-de-Teran et al 157 used a two-step approach to analyze the binding modes of different agonists on human A 1 adenosine receptor (hA 1 AR). The natural agonist adenosine and three synthetic derivatives were docked onto a theoretical model of hA 1 AR.…”

Section: B Linear Interaction Energy Methodsmentioning

confidence: 99%

Combining docking and molecular dynamic simulations in drug design

2006

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A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.

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“…In particular, Gutiérrez-de-Terán and coworkers applied it with the aim of discriminating among two different binding modes of agonists of the adenosine A 1 receptor generated by automatic docking [60]. More recently, we applied the LIE method to the calculation of the activity of a series of antagonists of the P2Y 1 receptor [7].…”

Section: Structure-based Methodologiesmentioning

confidence: 99%

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Costanzi

Tikhonova

Harden

2008

J Comput Aided Mol Des

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SummaryAccurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

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Comparative Analysis of Putative Agonist‐Binding Modes in the Human A₁ Adenosine Receptor

Cited by 12 publications

References 52 publications

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

Combining docking and molecular dynamic simulations in drug design

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

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Comparative Analysis of Putative Agonist‐Binding Modes in the Human A1 Adenosine Receptor

Cited by 12 publications

References 52 publications

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

New parameterization approaches of the LIE method to improve free energy calculations of PlmII‐Inhibitors complexes

Combining docking and molecular dynamic simulations in drug design

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Contact Info

Product

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Comparative Analysis of Putative Agonist‐Binding Modes in the Human A₁ Adenosine Receptor