title: Adaptive spectral clustering with application to tripeptide conformation analysis.
abstract: A decomposition of a molecular conformational space into sets or functions (states) allows for a reduced description of the dynamical behavior in terms of transition probabilities between these states. Spectral clustering of the corresponding transition probability matrix can then reveal metastabilities. The more states are used for the decomposition, the smaller the risk to cover multiple conformations with one state, which would make these conformations indistinguishable. However, since the computational complexity of the clustering algorithm increases quadratically with the number of states, it is desirable to have as few states as possible. To balance these two contradictory goals, we present an algorithm for an adaptive decomposition of the position space starting from a very coarse decomposition. The algorithm is applied to small data classification problems where it was shown to be superior to commonly used algorithms, e.g., k-means. We also applied this algorithm to the conformation analysis of a tripeptide molecule where six-dimensional time series are successfully analyzed.
authors: Institut für Informatik, Universität Rostock Albert-Einstein-Str. 21, D-18059 Rostock, Germany.
authors: Fiete
authors: F
authors: Haack
authors: Konstantin
authors: K
authors: Fackeldey
authors: Susanna
authors: S
authors: Röblitz
authors: Olga
authors: O
authors: Scharkoi
authors: Marcus
authors: M
authors: Weber
authors: Burkhard
authors: B
authors: Schmidt
mesh_terms: D000465:Algorithms; D056004:Molecular Dynamics Simulation; D009842:Oligopeptides / Q000032:analysis*; D011487:Protein Conformation
chemical_list: D009842:Oligopeptides