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Imaging G protein-coupled receptors while quantifying their ligand-binding free-energy landscape.

ABSTRACT: Imaging native membrane receptors and testing how they interact with ligands is of fundamental interest in the life sciences but has proven remarkably difficult to accomplish. Here, we introduce an approach that uses force-distance curve-based atomic force microscopy to simultaneously image single native G protein-coupled receptors in membranes and quantify their dynamic binding strength to native and synthetic ligands. We measured kinetic and thermodynamic parameters for individual protease-activated receptor-1 (PAR1) molecules in the absence and presence of antagonists, and these measurements enabled us to describe PAR1's ligand-binding free-energy landscape with high accuracy. Our nanoscopic method opens an avenue to directly image and characterize ligand binding of native membrane receptors.

SUBMITTER: Alsteens D

PROVIDER: S-EPMC5087271 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Imaging G protein-coupled receptors while quantifying their ligand-binding free-energy landscape.

Alsteens David D Pfreundschuh Moritz M Zhang Cheng C Spoerri Patrizia M PM Coughlin Shaun R SR Kobilka Brian K BK Müller Daniel J DJ

Nature methods 20150713 9

Imaging native membrane receptors and testing how they interact with ligands is of fundamental interest in the life sciences but has proven remarkably difficult to accomplish. Here, we introduce an approach that uses force-distance curve-based atomic force microscopy to simultaneously image single native G protein-coupled receptors in membranes and quantify their dynamic binding strength to native and synthetic ligands. We measured kinetic and thermodynamic parameters for individual protease-act ...[more]

PMID: 26167642

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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

Imaging G protein-coupled receptors while quantifying their ligand-binding free-energy landscape.

Publications

Imaging G protein-coupled receptors while quantifying their ligand-binding free-energy landscape.

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