GPU-Accelerated Implementation of Continuous Constant pH Molecular Dynamics in Amber: pKa Predictions with Single-pH Simulations
GPU-Accelerated Implementation of Continuous Constant pH Molecular Dynamics in Amber: pKa Predictions with Single-pH Simulations
复制标题
DOI：
10.1021/acs.jcim.9b00754
复制
发表时间：
2019-11-01
影响因子：
5.6
通讯作者：
Shen, Jana
中科院分区：
文献类型：
--
作者：
Harris, Robert C.;Shen, Jana
基于Amber分子动力学包的pmemd引擎中最新的广义Born隐式溶剂模型，我们提出了一个连续恒定pH分子动力学（CpHMD）的GPU实现。为了测试该工具快速预测pK(a)的准确性，对10种基准蛋白中的120多个可滴定残基进行了一系列2 ns的单ph模拟，这些基准蛋白先前用于测试各种连续CpHMD方法。计算得到的pK(a) s相对于试验的均方根偏差为0.80，相关系数为0.83。此外，90%的pK(a)的收敛估计误差低于0.1 pH单位。令人惊讶的是，这种精确度与我们之前的副本交换模拟相似，每个副本2 ns，交换尝试频率为2 ps(-1) (Huang, Harris, and Shen J. Chem。Inf. Model. 2018, 58, 1372-1383)。有趣的是，对于两种酶的连接滴定位点，虽然在单pH模拟中残基特异性质子化状态采样在2 ns内没有收敛，但连接残基的质子化分数似乎在很大程度上收敛，并且实验宏观pKa值被复制到1 pH单位内。与不同交换尝试频率的复制交换模拟的比较表明，频繁的交换尝试（如2 ps(-1)）低估了两个宏观pK(a) s之间的分裂，而单ph模拟高估了分裂。在更大的蛋白质中，单ph值与氢键天冬氨酸二元体的复制交换模拟也出现了同样的趋势。在单个NVIDIA GeForce RTX 2080显卡上，对400个残基蛋白进行2 ns的单ph模拟大约需要1小时，这比在高性能计算集群节点的单个CPU核心上运行CpHMD快1000倍以上。因此，我们设想gpu加速的连续CpHMD可以用于各种应用的常规pK(a)预测，从协助质子化状态分配的MD模拟到提供pH依赖的结合自由能修正和识别共价药物设计的反应热点。
We present a GPU implementation of the continuous constant pH molecular dynamics (CpHMD) based on the most recent generalized Born implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. To test the accuracy of the tool for rapid pK(a) predictions, a series of 2 ns single-pH simulations were performed for over 120 titratable residues in 10 benchmark proteins that were previously used to test the various continuous CpHMD methods. The calculated pK(a)'s showed a root-mean-square deviation of 0.80 and correlation coefficient of 0.83 with respect to experiment. Also, 90% of the pK(a)'s were converged with estimated errors below 0.1 pH units. Surprisingly, this level of accuracy is similar to our previous replica-exchange simulations with 2 ns per replica and an exchange attempt frequency of 2 ps(-1) (Huang, Harris, and Shen J. Chem. Inf. Model. 2018, 58, 1372-1383). Interestingly, for the linked titration sites in two enzymes, although residue-specific protonation state sampling in the single-pH simulations was not converged within 2 ns, the protonation fraction of the linked residues appeared to be largely converged, and the experimental macroscopic pKa values were reproduced to within 1 pH unit. Comparison with replica-exchange simulations with different exchange attempt frequencies showed that the splitting between the two macroscopic pK(a)'s is underestimated with frequent exchange attempts such as 2 ps(-1), while single-pH simulations overestimate the splitting. The same trend is seen for the single-pH vs replica-exchange simulations of a hydrogen-bonded aspartyl dyad in a much larger protein. A 2 ns single-pH simulation of a 400-residue protein takes about 1 h on a single NVIDIA GeForce RTX 2080 graphics card, which is over 1000 times faster than a CpHMD run on a single CPU core of a high-performance computing cluster node. Thus, we envision that GPU-accelerated continuous CpHMD may be used in routine pK(a) predictions for a variety of applications, from assisting MD simulations with protonation state assignment to offering pH dependent corrections of binding free energies and identifying reactive hot spots for covalent drug design.