分子动力学
水准点(测量)
计算机科学
采样(信号处理)
常量(计算机编程)
生物系统
化学
蛋白质动力学
理论(学习稳定性)
化学物理
计算化学
机器学习
大地测量学
滤波器(信号处理)
计算机视觉
生物
程序设计语言
地理
作者
David J. Reilley,Jian Wang,Nikolay V. Dokholyan,Anastassia N. Alexandrova
标识
DOI:10.1021/acs.jctc.1c00338
摘要
The pH-dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. The challenges and expense of investigating dynamic, atomic scale behavior experimentally means that computational methods, particularly constant pH molecular dynamics (CpHMD), are well situated tools for this. However, these methods often struggle with affordable sampling of sufficiently long time scales while also obtaining accurate pKa prediction and verifying the structures they generate. We introduce Titr-DMD, an affordable CpHMD method that combines the quasi-all-atom coarse-grained discrete molecular dynamics (DMD) method for conformational sampling with Propka for pKa prediction, to circumvent these issues. The combination enables rapid sampling on limited computational resources, while simulations are still performed on the atomic scale. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective and inexpensive method to study pH-coupled protein dynamics.
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