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Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity.

ABSTRACT: In order to quantify the interactions between molecules of biological interest, the determination of the dissociation constant (K d) is essential. Estimation of the binding affinity in this way is routinely performed in "favorable" conditions for macromolecules. Crucial data for ligand-protein binding elucidation is mainly derived from techniques (e.g., macromolecular crystallography) that require the addition of high concentration of salts and/or other additives. In this study we have evaluated the effect of temperature, ionic strength, viscosity, and hydrophobicity on the K d of three previously characterized protein-ligand systems, based on variation in their binding sites, in order to provide insight into how these often overlooked unconventional circumstances impact binding affinity. Our conclusions are as follows: (1) increasing solvent viscosity in general is detrimental to ligand binding, (2) moderate increases in temperature have marginal effects on the dissociation constant, and (3) the degree of hydrophobicity of the ligand and the binding site determines the extent of the influence of cosolvents and salt concentration on ligand binding affinity.

SUBMITTER: Papaneophytou CP

PROVIDER: S-EPMC4137368 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity.

Papaneophytou Christos P CP Grigoroudis Asterios I AI McInnes Campbell C Kontopidis George G

ACS medicinal chemistry letters 20140709 8

In order to quantify the interactions between molecules of biological interest, the determination of the dissociation constant (K d) is essential. Estimation of the binding affinity in this way is routinely performed in "favorable" conditions for macromolecules. Crucial data for ligand-protein binding elucidation is mainly derived from techniques (e.g., macromolecular crystallography) that require the addition of high concentration of salts and/or other additives. In this study we have evaluated ...[more]

PMID: 25147617

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Project description:Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.

Dataset Information

Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity.

Publications

Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity.

Similar Datasets

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