Crystallographic Ensembles Reveal the Structural Basis of Binding Entropy in SARS-CoV2 Macrodomain.

Structure-based drug design has traditionally focused on optimizing static, enthalpic interactions between ligands and proteins or on displacing binding site solvent molecules to entropically favor binding. A potentially large contributor to binding thermodynamics is the difference in conformational entropy of the protein upon binding a ligand; however, this has been difficult to quantify especially in high throughput. Here, through multiconformer ensemble modeling of hundreds of ligand-bound SARS-CoV-2 Macrodomain (Mac1) X-ray structures, we show how ligand binding reorganizes both protein conformational entropy and water molecules. By applying an optimal transport-based clustering algorithm, we show how specific protein-ligand interactions patterns drive the magnitude and spatial redistribution of conformational entropy and solvent networks. Using isothermal titration calorimetry (ITC), we demonstrate a correlation between experimental binding thermodynamics and conformational entropy estimated from structural ensemble models, showing that increased conformational heterogeneity and a less connected hydrogen-bonded water network lead to more entropic binding. These results establish a framework for extracting thermodynamically meaningful information from crystallographic ensembles, enabling the integration of entropic effects into prospective, ensemble-aware drug discovery.