Adaptive spectral clustering for conformation analysis

Markov state models have become very popular for the description of conformation dynamics of molecules over long timescales. The construction of such models requires a partitioning of the configuration space such that the discretization can serve as an approximation of metastable conformations. Since the computational complexity for the construction of a Markov state model increases quadratically with the number of sets, it is desirable to obtain as few sets as necessary. In this paper we propose an algorithm for the adaptive refinement of an initial coarse partitioning. A spectral clustering method is applied to the final partitioning to detect the metastable conformations. We apply this method to the conformation analysis of a model tri-peptide molecule, where metastable β and γ-turn conformations can be identified.