10.1021/acs.jctc.5b01233.s001 Florian Sittel Florian Sittel Gerhard Stock Gerhard Stock Robust Density-Based Clustering To Identify Metastable Conformational States of Proteins American Chemical Society 2016 dynamics trajectories protein states energy barriers parameter choice space density Markov state models model problems method metastable states component analysis microstate Identify Metastable Conformational States villin headpiece energy estimates data dihedral angle content color plots energy minima path algorithm pancreatic trypsin inhibitor 2016-04-08 00:00:00 Journal contribution https://acs.figshare.com/articles/journal_contribution/Robust_Density_Based_Clustering_To_Identify_Metastable_Conformational_States_of_Proteins/3187617 A density-based clustering method is proposed that is deterministic, computationally efficient, and self-consistent in its parameter choice. By calculating a geometric coordinate space density for every point of a given data set, a local free energy is defined. On the basis of these free energy estimates, the frames are lumped into local free energy minima, ultimately forming microstates separated by local free energy barriers. The algorithm is embedded into a complete workflow to robustly generate Markov state models from molecular dynamics trajectories. It consists of (i) preprocessing of the data via principal component analysis in order to reduce the dimensionality of the problem, (ii) proposed density-based clustering to generate microstates, and (iii) dynamical clustering via the <i>most probable path</i> algorithm to construct metastable states. To characterize the resulting state-resolved conformational distribution, dihedral angle content color plots are introduced which identify structural differences of protein states in a concise way. To illustrate the performance of the method, three well-established model problems are adopted: conformational transitions of hepta-alanine, folding of villin headpiece, and functional dynamics of bovine pancreatic trypsin inhibitor.