# Software

Several of our in-house developments to analyze 'big-data' MD simulations can be obtained from our github github.com/moldyn.## FastPCA

The*FastPCA*package is an implementation of the principal component analysis of large MD data sets, using either Cartesian atom coordinates, interatom distances or backbone dihedral angles as input coordinates. In particular, it features the dihedral angle PCA on a torus (dPCA+)by Sittel et al., 2017, which performs maximal gap shifting to treat periodic data correctly. It is optimized and parallelized with constant memory consumption for large data sets. Technical information and the source code can be found at the project site.

## Clustering

The*clustering*package is a highly optimized C++11 program suite parallelized with CUDA and OpenMP. It supports for:

- geometric density-based clustering by Sittel et al., 2016, as adapted by Nagel et al., 2019
- dynamical coring and treatment of noise by Nagel et al., 2019
- dynamic clustering via most probable path algorithm by Jain et al., 2014

## Ramacolor

The*ramacolor*script (written in python3) as proposed in Sittel et al., 2016 can be downloaded directly from here. Ramacolor plots visualize the secondary structures of given microstates. Hence, states can be assigned as dynamically active/inactive at a glance.

## dissipation-corrected targeted molecular dynamics (dcTMD)

Python scripts used for dissipation-corrected targeted molecular dynamics by Wolf et al., 2018 analysis for usage with "*pullf.xvg" files from Gromacs. More information can be found at the github page.## Data-Driven Langevin Package

We have developed together with R. Hegger a systematic computational approach to describe the conformational dynamics of biomolecules in reduced dimensionality using data-driven Langevin equation modeling.For details see Hegger et al., 2009 and Schaudinnus et al., 2016.

The software can be downloaded from github.com/moldyn/Data-Driven-Langevin.