Effect of Water Networks On Ligand Binding: Computational Predictions vs Experiments

Abstract

Rational drug design focuses on the explanation and prediction of complex formation between therapeutic targets and small-molecule ligands. As a third and often overlooked interacting partner, water molecules play a critical role in the thermodynamics of protein−ligand binding, impacting both the entropy and enthalpy components of the binding free energy and by extension, on-target affinity and bioactivity. The community has realized the importance of binding site waters, as evidenced by the number of computational tools to predict the structure and thermodynamics of their networks. However, quantitative experimental characterization of relevant protein−ligand−water systems, and consequently the validation of these modeling methods, remains challenging. Here, we investigated the impact of solvent exchange from light (H 2 O) to heavy water (D 2 O) to provide complete thermodynamic profiling of these ternary systems. Utilizing the solvent isotope effects, we gain a deeper understanding of the energetic contributions of various components. Specifically, we conducted isothermal titration calorimetry experiments on trypsin with a series of psubstituted benzamidines, as well as carbonic anhydrase II (CAII) with a series of aromatic sulfonamides. Significant differences in binding enthalpies found between light vs heavy water indicate a substantial role of the binding site water network in protein−ligand binding. Next, we challenged two conceptually distinct modeling methods, the grid-based WaterFLAP and the molecular dynamicsbased MobyWat, by predicting and scoring relevant water networks. The predicted water positions accurately reproduce those in available high-resolution X-ray and neutron diffraction structures of the relevant protein−ligand complexes. Estimated energetic contributions of the identified water networks were corroborated by the experimental thermodynamics data. Besides providing a direct validation for the predictive power of these methods, our findings confirmed the importance of considering binding site water networks in computational ligand design.