@article{38946,
  keywords = {Proteins, harmonic-analysis, nonlinear dimensionality reduction, geometric   diffusions, structure definition, monte-carlo, conformational dynamics, equilibrium ensemble, molecular-dynamics simulations, state models},
  author = {Lilia Nedialkova and Miguel Amat and Ioannis Kevrekidis and Gerhard Hummer},
  title = {Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions},
  abstract = { <p>Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small-but nontrivial-differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space. (C) 2014 AIP Publishing LLC.</p> },
  year = {2014},
  journal = {Journal of Chemical Physics},
  volume = {141},
  pages = {114102},
  month = {09/2014},
  isbn = {0021-9606},
  language = {English},
}