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Aerosol-based ligand soaking of reservoir-free protein crystals.

ABSTRACT: Soaking of macromolecular crystals allows the formation of complexes via diffusion of molecules into a preformed crystal for structural analysis. Soaking offers various advantages over co-crystallization, e.g. small samples and high-throughput experimentation. However, this method has disadvantages, such as inducing mechanical stress on crystals and reduced success rate caused by low affinity/solubility of the ligand. To bypass these issues, the Picodropper was previously developed in the authors' laboratory. This technique aimed to deliver small volumes of compound solution in response to crystal dehydration supported by the Free Mounting System humidity control or by IR-laser-induced protein crystal transformation. Herein, a new related soaking development, the Aerosol-Generator, is introduced. This device delivers compounds onto the solution-free surface of protein crystals using an ultrasonic technique. The produced aerosol stream enables an easier and more accurate control of solution volumes, reduced crystal handling, and crystal-size-independent soaking. The Aerosol-Generator has been used to produce complexes of DPP8 crystals, where otherwise regular soaking did not achieve complex formation. These results demonstrate the potential of this device in challenging ligand-binding scenarios and contribute to further understanding of DPP8 inhibitor binding.

SUBMITTER: Ross B

PROVIDER: S-EPMC8202026 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Aerosol-based ligand soaking of reservoir-free protein crystals.

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