Adaptive Multilevel Summation (AMS) is a rare event sampling method that requires minimal parameter tuning and that allows unbiased sampling of transition pathways of a given rare event. Previous simulation studies have verified the efficiency and accuracy of AMS in the calculation of transition times for simple systems in both Monte Carlo and molecular dynamics (MD) simulations. Now, AMS is applied for the first time to a MD simulation of protein-ligand dissociation, representing a leap in complexity from the previous test cases. Of interest is the dissociation rate, which is typically too low to be accessible to conventional MD. The present study joins other recent efforts to develop advanced sampling techniques in MD to calculate dissociation rates, which are gaining importance in the pharmaceutical field as indicators of drug efficacy. The system investigated here, benzamidine bound to trypsin, is an example common to many of these efforts. The AMS estimate of the dissociation rate was found to be (2.6 ± 2.4) × 102 s−1, which compares well with the experimental value.
展开▼
机译：自适应多级汇总（AMS）是一种稀有事件采样方法，它需要最少的参数调整，并且可以对给定稀有事件的过渡路径进行无偏采样。先前的模拟研究已经验证了AMS在简单系统的蒙特卡罗（Monte Carlo）和分子动力学（MD）模拟中计算过渡时间的效率和准确性。现在，AMS首次应用于蛋白质-配体解离的MD模拟，这代表了与先前测试案例相比复杂性的飞跃。令人感兴趣的是解离速率，该解离速率通常太低以至于常规MD无法获得。本研究与其他最近的努力一起，在MD中开发先进的采样技术以计算解离速率，在药物领域作为药物功效指标越来越重要。此处研究的系统，将苯甲bound与胰蛋白酶结合，是许多此类努力的一个共同实例。 AMS的解离速率估计为（2.6±2.4）×10 2 s -1 ，与实验值相比较。
展开▼
