Kuang Xiaohan, Su Zhaoqian, Liu Yunchao Lance, Lin Xiaobo, Spencer-Smith Jesse, Derr Tyler, Wu Yinghao, Meiler Jens
Data Science Institute, Vanderbilt University, Nashville, 37212, TN, USA.
Computer Science Department, Vanderbilt University, Nashville, 37240, TN, USA.
bioRxiv. 2024 Nov 20:2024.11.18.624208. doi: 10.1101/2024.11.18.624208.
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.
水分子在维持蛋白质结构稳定性和促进分子相互作用方面发挥着重要作用。准确预测蛋白质结构周围的水分子位置对于理解其生物学作用至关重要，并且对蛋白质工程和药物发现具有重要意义。在此，我们介绍SuperWater，这是一种新颖的生成式人工智能框架，它将基于分数的扩散模型与等变图神经网络相结合，以高精度预测蛋白质周围的水分子位置。SuperWater超越了现有方法，在晶体水覆盖率和预测精度方面均提供了最先进的性能，在实验验证位置的0.3±0.06 Å范围内实现了水的定位。我们通过涉及蛋白质水合作用、蛋白质-配体结合和蛋白质-蛋白质结合位点的案例研究展示了SuperWater的能力。该框架可适用于各种应用，包括结构生物学、结合位点预测、多体对接和水介导的药物设计。
