Hinz Florian B, Masters Matthew R, Nguyen Julia T, Mahmoud Amr H, Lill Markus A
Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland.
Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.
J Chem Inf Model. 2025 Mar 24;65(6):2794-2805. doi: 10.1021/acs.jcim.4c02349. Epub 2025 Feb 28.
Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein is critical for ligand recognition and binding. Therefore, identifying the location and thermodynamic properties of relevant water molecules is important for generating and optimizing lead compounds for affinity and selectivity for a given target. Computational methods have been developed to identify these hydration sites (HS), but are largely limited to simplified models that fail to capture multibody interactions or dynamics-based methods that rely on extensive sampling. Here, we present a method for fast and accurate localization and thermodynamic profiling of HS for protein structures. The method is based on a geometric deep neural network trained on a large, novel data set of explicit water molecular dynamics simulations. We confirm the accuracy and robustness of our model on experimental data and demonstrate its utility on several case studies.
水在蛋白质和其他生物分子的结构与功能中起着基础性作用。蛋白质周围水分子的热力学特征对于配体识别和结合至关重要。因此，确定相关水分子的位置和热力学性质对于生成和优化针对给定靶点的亲和力和选择性的先导化合物很重要。已经开发了计算方法来识别这些水化位点（HS），但在很大程度上局限于无法捕捉多体相互作用的简化模型或依赖大量采样的基于动力学的方法。在此，我们提出了一种用于蛋白质结构HS快速准确定位和热力学分析的方法。该方法基于一个在大量全新的显式水分子动力学模拟数据集上训练的几何深度神经网络。我们在实验数据上证实了我们模型的准确性和稳健性，并在几个案例研究中展示了其效用。
