News from Oct 31, 2016
Core sets are disjoint metastable regions in the conformational space, which need to be known prior to the construction of the core-set model. In our publication Density-based cluster algorithms for the identifcation of core sets we show that density- based can efficiently and reliably identify core-sets in a high-dimensional and rugged energy-landscape. The resulting core-set models need up to an order of magnitude less states than conventional Markov state models. Moreover, using the density-based clustering one can extend the core-set method to systems which are not strongly metastable. We test this approach on a molecular-dynamics simulation of a highly flexible 14-residue peptide.