  We present an unsupervised data processing workflow that is specifically
designed to obtain a fast conformational clustering of long molecular dynamics
simulation trajectories. In this approach we combine two dimensionality
reduction algorithms (cc\_analysis and encodermap) with a density-based spatial
clustering algorithm (HDBSCAN). The proposed scheme benefits from the strengths
of the three algorithms while avoiding most of the drawbacks of the individual
methods. Here the cc\_analysis algorithm is for the first time applied to
molecular simulation data. Encodermap complements cc\_analysis by providing an
efficient way to process and assign large amounts of data to clusters. The main
goal of the procedure is to maximize the number of assigned frames of a given
trajectory, while keeping a clear conformational identity of the clusters that
are found. In practice we achieve this by using an iterative clustering
approach and a tunable root-mean-square-deviation-based criterion in the final
cluster assignment. This allows to find clusters of different densities as well
as different degrees of structural identity. With the help of four test systems
we illustrate the capability and performance of this clustering workflow:
wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b),
NTL9 and Protein B. Each of these systems poses individual challenges to the
scheme, which in total give a nice overview of the advantages, as well as
potential difficulties that can arise when using the proposed method.
