质子与蛋白质结合:pK(a) 使用明确和隐性溶剂模型进行计算.
Thomas Simonson1, Jens Carlsson, David A Case
1Laboratoire de Biochimie (UMR7654 du CNRS), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France. thomas.simonson@polytechnique.fr
Journal of the American Chemical Society
|April 1, 2004
概括
分子动力学自由能量模拟准确地预测了蛋白质pKa转移. 隐式溶剂模型,如泛化的Born (GB) 模型,显示出计算这些转移的前景,即使是复杂的蛋白质重组.
科学领域:
- 生物物理学的生物物理.
- 计算化学计算化学
- 蛋白质科学 蛋白质科学
背景情况:
- 可电离的残留物对蛋白质的功能至关重要.
- 质子结合 (pKa) 揭示了静电相互作用.
- 准确的pKa预测对于理解蛋白质行为至关重要.
研究的目的:
- 使用分子动力学自由能量模拟 (MDFE) 计算阿斯巴酸盐残留物的pKa转移.
- 将显式溶剂模拟与隐式溶剂模拟使用一般化Born (GB) 模型进行比较.
- 为了研究蛋白质重组对pKa转移的影响.
主要方法:
- 分子动力学自由能量模拟 (MDFE) 使用明确和隐性 (GB) 溶剂.
- 计算两个蛋白质中的三个阿斯巴酸侧链的质子pKa移位.
- 在电离过程中对电介质反应和蛋白质重组的分析.
主要成果:
- 显式溶剂模拟正确地预测了pKa转移与AMBER和CHARMM力场的方向.
- 由于多个子状态或显著的重组,观察到一些阿斯巴酸盐的非线性介电反应.
- 使用GB溶剂的MDFE准确地描述了蛋白质的重组,并且与实验数据和明确的溶剂模拟有很好的一致性.
结论:
- MDFE,特别是与GB隐性溶剂,是pKa预测的强大工具,捕获复杂的蛋白质动态.
- 隐式溶剂模型为pKa计算提供了对显式溶剂的计算效率高但准确的替代方案.
- 了解蛋白质重组是准确的pKa预测的关键,特别是在具有大转移的埋藏残留物中.
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