Institute for Theoretical Physics, Freie Universität Berlin, Berlin, Germany.
Mathematics for Life and Materials Sciences, Zuse Institue Berlin, Berlin, Germany.
PLoS One. 2018 Dec 12;13(12):e0207718. doi: 10.1371/journal.pone.0207718. eCollection 2018.
The transfer of protons through proton translocating channels is a complex process, for which direct samplings of different protonation states and side chain conformations in a transition network calculation provide an efficient, bias-free description. In principle, a new transition network calculation is required for every unsampled change in the system of interest, e.g. an unsampled protonation state change, which is associated with significant computational costs. Transition networks void of or including an unsampled change are termed unperturbed or perturbed, respectively. Here, we present a prediction method, which is based on an extensive coarse-graining of the underlying transition networks to speed up the calculations. It uses the minimum spanning tree and a corresponding sensitivity analysis of an unperturbed transition network as initial guess and refinement parameter for the determination of an unknown, perturbed transition network. Thereby, the minimum spanning tree defines a sub-network connecting all nodes without cycles and minimal edge weight sum, while the sensitivity analysis analyzes the stability of the minimum spanning tree towards individual edge weight reductions. Using the prediction method, we are able to reduce the calculation costs in a model system by up to 80%, while important network properties are maintained in most predictions.
质子通过质子转移通道的转移是一个复杂的过程，对于这个过程，可以通过在过渡网络计算中直接采样不同的质子化状态和侧链构象，来提供一种有效且无偏差的描述。原则上，对于感兴趣的系统中每一个未采样的变化，例如一个未采样的质子化状态变化，都需要进行新的过渡网络计算，这会带来显著的计算成本。没有或包含未采样变化的过渡网络分别称为未受扰的或受扰的。在这里，我们提出了一种预测方法，该方法基于对基础过渡网络的广泛粗粒化，以加速计算。它使用最小生成树和相应的未受扰过渡网络的敏感性分析作为初始猜测和确定未知受扰过渡网络的细化参数。由此，最小生成树定义了一个连接所有无环和最小边权重和的节点的子网，而敏感性分析分析了最小生成树对单个边权重降低的稳定性。使用预测方法，我们能够将模型系统的计算成本降低多达 80%，而在大多数预测中，重要的网络特性都得到了保留。
