论文标题
手机学习和图形方法协助的配体解开途径和机制分析
Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods
论文作者
论文摘要
我们提出了两种方法，可以通过将轨迹聚集到代表未连接路径的集合中，以揭示有偏见的解关模拟中的蛋白质解开机制。第一种方法是基于接触主成分分析，用于降低输入数据的维度，然后识别解开路径并训练机器学习模型以进行轨迹聚类。第二种方法簇轨迹根据其成对的平均欧几里得距离采用邻居网络算法，该算法考虑了距离集合中的输入数据偏置，并且优于树状图构建。最后，我们描述了一个更复杂的案例，其中与路径鉴定相关的反应坐标是单个配体氢键，强调了涉及解除路径反应坐标检测的挑战。
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for reducing the dimensionality of the input data, followed by identification of unbinding paths and training a machine learning model for trajectory clustering. The second approach clusters trajectories according to their pairwise mean Euclidean distance employing the neighbor-net algorithm, which takes into account input data bias in the distances set and is superior to dendrogram construction. Finally, we describe a more complex case where the reaction coordinate relevant for path identification is a single intra-ligand hydrogen bond, highlighting the challenges involved in unbinding path reaction coordinate detection.
