Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, CH-8093 Zürich, Switzerland.
J Chem Phys. 2010 Feb 21;132(7):074110. doi: 10.1063/1.3301140.
The identification of metastable states of a molecule plays an important role in the interpretation of molecular simulation data because the free-energy surface, the relative populations in this landscape, and ultimately also the dynamics of the molecule under study can be described in terms of these states. We compare the results of three different geometric cluster algorithms (neighbor algorithm, K-medoids algorithm, and common-nearest-neighbor algorithm) among each other and to the results of a kinetic cluster algorithm. First, we demonstrate the characteristics of each of the geometric cluster algorithms using five two-dimensional data sets. Second, we analyze the molecular dynamics data of a beta-heptapeptide in methanol--a molecule that exhibits a distinct folded state, a structurally diverse unfolded state, and a fast folding/unfolding equilibrium--using both geometric and kinetic cluster algorithms. We find that geometric clustering strongly depends on the algorithm used and that the density based common-nearest-neighbor algorithm is the most robust of the three geometric cluster algorithms with respect to variations in the input parameters and the distance metric. When comparing the geometric cluster results to the metastable states of the beta-heptapeptide as identified by kinetic clustering, we find that in most cases the folded state is identified correctly but the overlap of geometric clusters with further metastable states is often at best approximate.
确定分子的亚稳态在解释分子模拟数据方面起着重要作用，因为可以根据这些状态来描述自由能面、该景观中的相对种群，以及最终研究分子的动力学。我们相互比较了三种不同的几何聚类算法（邻域算法、K-均值算法和常见最近邻算法）以及动力学聚类算法的结果。首先，我们使用五个二维数据集展示了每种几何聚类算法的特征。其次，我们使用几何和动力学聚类算法分析了甲醇中β-七肽的分子动力学数据——该分子表现出明显的折叠态、结构多样的展开态以及快速折叠/展开平衡。我们发现几何聚类强烈依赖于所使用的算法，并且基于密度的常见最近邻算法是三种几何聚类算法中最稳健的，相对于输入参数和距离度量的变化。当将几何聚类结果与动力学聚类确定的β-七肽的亚稳态进行比较时，我们发现折叠态通常能正确识别，但几何聚类与进一步的亚稳态之间的重叠通常最多只是近似。
