Robust Density-Based Clustering To Identify Metastable
Conformational States of Proteins
SittelFlorian
StockGerhard
2016
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.