Superwater as a generative AI framework to predict water molecule positions on protein structures

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy. SuperWater surpasses existing methods, delivering state-of-the-art performance in both crystal water coverage and prediction precision, achieving water localization within 0.3 ± 0.06 Å of experimentally validated positions. We demonstrate the capabilities of SuperWater through case studies involving protein hydration, protein-ligand binding, and protein-protein binding sites. This framework can be adapted for various applications, including structural biology, binding site prediction, multi-body docking, and water-mediated drug design. Water molecules are crucial for protein stability and interactions, yet predicting their precise positions around protein structures remain challenging. Here, the authors present SuperWater, a generative AI framework combining a score-based diffusion model and equivariant graph neural networks, to achieve unprecedented accuracy in water placement, demonstrating the capabilities involving protein hydration, protein-ligand binding and protein-protein interactions.