Binding Constants of Neuraminidase Inhibitors:  An Investigation of the Linear Interaction Energy Method

Wall, Ian D.; Leach, Andrew R.; Salt, David W.; Ford, Martyn G.; Essex, Jonathan W.

doi:10.1021/jm990105g

Cited by 81 publications

(109 citation statements)

References 37 publications

(82 reference statements)

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“…The proteins studied include dihydrofolate reductase [99], thrombin [100], and others [95][96][97][101][102][103]. In practice, the van der Waals coefficient α is usually adjusted empirically to fit the experimental data for a subset of ligands; β is sometimes adjusted, too.…”

Section: Linear Interaction Energy Approachesmentioning

confidence: 99%

Free Energy Calculations: Approximate Methods for Biological Macromolecules

Simonson

2007

Springer Series in CHEMICAL PHYSICS

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In this chapter we present the most important simplified free energy methods in use today and the main biological problems that have motivated their development. The area in which these methods are perhaps the most valuable is the study of molecular recognition between biological molecules, such as an enzyme and a substrate or inhibitor. Noncovalent association between biomolecules is a key element of the biochemistry and information flow in living systems. Many competing effects can contribute to receptor-ligand binding [1]: changes in rotational, translational, conformational, and vibrational entropy of the partners, entropy changes associated with solvent ordering around hydrophobic or charged groups, solute conformational strain, changes in electrostatic and van der Waals interactions within and between the partners and the solvent, counterion reorganization. Experimental studies often combine structure determination methods with point mutagenesis and thermodynamic measurements to obtain information on the binding [2]. However, there are considerable difficulties in the experimental analysis of longer-range electrostatic contributions, the cooperativity between amino acid residues of a protein, or disordered solvent, for example. Such effects can be determined using rigorous free energy simulations, described in the earlier chapters of this book. They can also be incorporated, at different levels of accuracy, into simplified free energy methods.The basic principle of a receptor-ligand binding analysis by free energy simulations is explained in Fig. 12.1 [3]. Most applications focus on binding free energy differences between a series of ligands or protein mutants. For a review of the relevant statistical thermodynamics see [1]. Like experiments, the rigorous free energy simulation method requires a reversible (or near-reversible [4,5]) path between the initial and final states. The Helmholtz free energy change along the horizontal legs in Fig

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Section: Linear Interaction Energy Approachesmentioning

confidence: 99%

Free Energy Calculations: Approximate Methods for Biological Macromolecules

Simonson

2007

Springer Series in CHEMICAL PHYSICS

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“…Nevertheless, treatment of the LIE coefficients as entirely free parameters, in a QSAR-type manner, can still apparently yield very reasonable results. 20,21 Here we report results based on MD simulations of nine different P450cam-ligand complexes, seven of which were also investigated by Paulsen and Ornstein. 18 Three commonly used force fields are employed in these calculations, namely Gromos87, Amber95, and OPLS-AA.…”

Section: Introductionmentioning

confidence: 97%

Binding affinity prediction with different force fields: Examination of the linear interaction energy method

2004

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A systematic study of the linear interaction energy (LIE) method and the possible dependence of its parameterization on the force field and system (receptor binding site) is reported. We have calculated the binding free energy for nine different ligands in complex with P450cam using three different force fields (Amber95, Gromos87, and OPLS-AA). The results from these LIE calculations using our earlier parameterization give relative free energies of binding that agree remarkably well with the experimental data. However, the absolute energies are too positive for all three force fields, and it is clear that an additional constant term (gamma) is required in this case. Out of five examined LIE models, the same one emerges as the best for all three force fields, and this, in fact, corresponds to our earlier one apart from the addition of the constant gamma, which is almost identical for the three force fields. Thus, the present free energy calculations clearly indicate that the coefficients of the LIE method are independent of the force field used. Their relation to solvation free energies is also demonstrated. The only free parameter of the best model is gamma, which is found to depend on the hydrophobicity of the binding site. We also attempt to quantify the binding site hydrophobicity of four different proteins which shows that the ordering of gamma's for these sites reflects the fraction of hydrophobic surface area.

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“…With the MM/GBSA method, the ability of the generalised Born and Poisson-Boltzmann methods to calculate polar solvation free-energy will be compared. In passing, it should be mentioned that similar studies have been published previously, but only for protein-ligand systems [see for instance [21][22][23][24]]. However, rigorous studies of these issues for host-guest systems have not been performed.…”

Section: Introductionmentioning

confidence: 98%

MM/GBSA and LIE estimates of host–guest affinities: dependence on charges and solvation model

Genheden

2011

J Comput Aided Mol Des

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The affinities of two sets of guest-host systems were estimated using the popular end-point methods MM/GBSA (molecular-mechanics with generalised Born and surface-area solvation) and LIE (linear interaction energy). A set of six primary alcohols that bind to α-cyclodextrin (α-CD) and a set of eight guest molecules to cucurbit[8]uril (CB8) were considered. Three different charge schemes were used to obtain charges for the host and guest molecules, viz., AM1-BCC, RESP, and the recently suggested xAvESP (which average ESP charges over a number of molecular dynamics snapshots). Furthermore, both the generalised Born and Poisson-Boltzmann solvation models were used in the MM/GBSA calculations. The two solvation models perform equally well in predicting relative affinities, and hence there is no point in using the more expensive Poisson-Boltzmann model for these systems. Both the LIE and MM/GBSA estimates are shown to be robust with respect to the charge model, and therefore it is recommended to use the cheapest AM1-BCC charges. Using AM1-BCC charges, the MM/GBSA method gave a MADtr (mean absolute deviation after removal of systematic error) of 17 kJ/mol and a correlation coefficient (r (2)) of 0.67 for the CB8 complexes, and a MADtr of 10 kJ/mol and an r (2) of 0.96 for the α-CD complexes. The LIE method gave a MADtr of 20 kJ/mol and an r (2) of 0.10 for the CB8 complexes, after optimisation of the non-polar scaling parameter. For the α-CD complexes, no optimisation was necessary and the method gave a MADtr of 2 kJ/mol and a r (2) of 0.96. These results indicate that both MM/GBSA and LIE are able to estimate host-guest affinities accurately.

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Binding Constants of Neuraminidase Inhibitors: An Investigation of the Linear Interaction Energy Method

Cited by 81 publications

References 37 publications

Free Energy Calculations: Approximate Methods for Biological Macromolecules

Free Energy Calculations: Approximate Methods for Biological Macromolecules

Binding affinity prediction with different force fields: Examination of the linear interaction energy method

MM/GBSA and LIE estimates of host–guest affinities: dependence on charges and solvation model

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