Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas78712, United States.
Electronic, Optical, and Nano Materials Department, Sandia National Laboratories, Albuquerque, New Mexico87185, United States.
J Phys Chem B. 2023 Feb 16;127(6):1367-1375. doi: 10.1021/acs.jpcb.2c07237. Epub 2023 Feb 3.
Rare-earth metals (REMs) are crucial for many important industries, such as power generation and storage, in addition to cancer treatment and medical imaging. One promising new REM refinement approach involves mimicking the highly selective and efficient binding of REMs observed in relatively recently discovered proteins. However, realizing any such bioinspired approach requires an understanding of the biological recognition mechanisms. Here, we developed a new classical polarizable force field based on the AMOEBA framework for modeling a lanthanum ion (La) interacting with water, acetate, and acetamide, which have been found to coordinate the ion in proteins. The parameters were derived by comparing to high-level quantum mechanical (QM) calculations that include relativistic effects. The AMOEBA model, with advanced atomic multipoles and electronic polarization, is successful in capturing both the QM distance-dependent La-ligand interaction energies and experimental hydration free energy. A new scheme for pairwise polarization damping (POLPAIR) was developed to describe the polarization energy in La interactions with both charged and neutral ligands. Simulations of La in water showed water coordination numbers and ion-water distances consistent with previous experimental and theoretical findings. Water residence time analysis revealed both fast and slow kinetics in water exchange around the ion. This new model will allow investigation of fully solvated lanthanum ion-protein systems using GPU-accelerated dynamics simulations to gain insights on binding selectivity, which may be applied to the design of synthetic analogues.
稀土金属 (REMs) 在许多重要行业中都至关重要,例如发电和储能,以及癌症治疗和医学成像。一种有前途的新型 REM 精炼方法涉及模拟 REM 在最近发现的蛋白质中观察到的高度选择性和高效结合。然而,要实现任何这样的仿生方法,都需要了解生物识别机制。在这里,我们开发了一种新的基于 AMOEBA 框架的经典极化力场,用于模拟镧离子 (La) 与水、乙酸盐和乙酰胺的相互作用,这些物质已被发现可在蛋白质中配位离子。参数是通过与包括相对论效应在内的高水平量子力学 (QM) 计算进行比较得出的。带有先进原子多极子和电子极化的 AMOEBA 模型成功地捕获了 QM 距离依赖性 La-配体相互作用能和实验水合自由能。开发了一种新的成对极化阻尼 (POLPAIR) 方案来描述带电荷和中性配体的 La 相互作用中的极化能。在水中模拟 La 时,水配位数和离子-水距离与先前的实验和理论发现一致。水停留时间分析显示,离子周围的水交换具有快速和慢速动力学。该新模型将允许使用 GPU 加速动力学模拟研究完全溶剂化的镧离子-蛋白质系统,以深入了解结合选择性,这可应用于合成类似物的设计。