JCP: Density-based clustering and core sets
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.