快速精准定位蛋白周围水分子并分析其热力学特性
Accelerated Hydration Site Localization and Thermodynamic Profiling
- 用几何深度学习模型分析显式水分子动力学数据
- 在多个案例中准确识别水分子结合位点及热力学参数
- 适合药物设计中优化亲和力与选择性的研究人员
水在蛋白质及其他生物分子的结构与功能中起着基础作用。蛋白质周围水分子的热力学特征对配体结合与识别至关重要。因此，识别关键水分子的位置及其热力学行为，对生成和优化先导化合物以提高对靶标的亲和力与选择性具有重要意义。现有计算方法多依赖简化模型，无法捕捉多体相互作用，或依赖大量采样的动态方法。本文提出一种快速且准确的水合位点定位与热力学分析方法，基于大规模新型显式水分子动力学模拟数据训练的几何深度神经网络。我们在实验数据上验证了模型的准确性与鲁棒性，并在多个案例研究中展示了其应用价值。
原文摘要 · Abstract (English)
Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein are critical for ligand binding and recognition. Therefore, identifying the location and thermodynamic behavior of relevant water molecules is important for generating and optimizing lead compounds for affinity and selectivity to a given target. Computational methods have been developed to identify these hydration sites, but are largely limited to simplified models that fail to capture multi-body interactions, or dynamics-based methods that rely on extensive sampling. Here we present a method for fast and accurate localization and thermodynamic profiling of hydration sites for protein structures. The method is based on a geometric deep neural network trained on a large, novel dataset of explicit water molecular dynamics simulations. We confirm the accuracy and robustness of our model on experimental data and demonstrate it's utility on several case studies.
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