Continuum Solvation Models in the Linear Interaction Energy Method

Carlsson, Jens; Andér, Martin; Nervall, Martin; Åqvist, Johan

doi:10.1021/jp056929t

Cited by 52 publications

(57 citation statements)

References 44 publications

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“…Notably, the most recent versions feature an empirically determined β factor and a third term representing the solvent accessible surface area (SASA), as proposed by Jorgensen and coworkers [58]. Furthermore, the methodology has been adapted in order to function with Poisson-Boltzmann (PB) and Generalized Born (GB) continuum models of solvation with levels of accuracy comparable to those obtained with explicit solvent [59].…”

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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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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“…MM-GBSA utilizes the Generalized Born approximation, which is much more efficient for evaluating hydration energy than solving the Poisson-Boltzmann equation, and still provides fairly good results. Linear Interaction Energy (LIE) was originally developed to calculate electrostatic and van der Waals potentials on an all-atom model with the explicit dealing of solvent (72) and then extended to the continuum model (81). These methods are considerably slower than scoring of single conformations, but faster than rigorous thermodynamic integration (TI) calculations (82,83), and provide more precise prediction than scoring of single conformation methods do (73, 83 -88).…”

Section: Empirical Scoring Function Methodsmentioning

confidence: 99%

Prediction of T-Cell Epitope

Tsurui¹,

Takahashi²

2007

J Pharmacol Sci

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Abstract. The prevailing methods to predict T-cell epitopes are reviewed. Motif matching, matrix, support vector machine (SVM), and empirical scoring function methods are mainly reviewed; and the thermodynamic integration (TI) method using all-atom molecular dynamics (MD) simulation is mentioned briefly. The motif matching method appeared first and developed with the increased understanding of the characteristic structure of MHC-peptide complexes, that is, pockets aligned in the groove and corresponding residues fitting on them. This method is now becoming outdated due to the insufficiency and inaccuracy of information. The matrix method, the generalization of interaction between pockets of MHC and residues of bound peptide to all the positions in the groove, is the most prevalent one. Efficiency of calculation makes this method appropriate to scan for candidates of T-cell epitopes within whole expressed proteins in an organ or even in a body. A large amount of experimental binding data is necessary to determine a matrix. SVM is a relative of the artificial neural network, especially direct generalization of a linear Perceptron. By incorporating non-binder data and adopting encoding that reflects the physical properties of amino acids, its performance becomes quite high. Empirical scoring functions apparently seem to be founded on a physical basis. However, the estimates directly derived from the method using only structural data are far from practical use. Through regression with binding data of a series of ligands and receptors, this method predicts binding affinity with appropriate accuracy. The TI method using MD requires only structural data and a general atomic parameter, that is, force field, and hence theoretically most consistent; however, the extent of perturbation, inaccuracy of the force field, the necessity of an immense amount of calculations, and continued difficulty of sampling an adequate structure hamper the application of this method in practical use.

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“…The LIE model was successfully used in combination with docking algorithms and proved to be able to correctly predict binding modes for a set of HIV-1 reverse transcriptase inhibitors [70,72]. Furthermore, the LIE approach in combination with two continuum models, PBE and GB models has been tested to predict ligand-binding free energy for a set of inhibitors against aspartic protease plasmepsin II [73]. The continuum models in this hybrid approach have been used in evaluation of ligand-water interaction energies, and close agreement between experimental binding free energies and predicted results has been reported [73].…”

Section: Macroscopic Models Turn Microscopicmentioning

confidence: 99%