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eSBMTools is a python package that assists setup and evaluation of structure-based model (SBM) simulations of proteins and nucleic acids, both at Cα and all-atom level. The tools interface with GROMACS and support its standard output formats. Information from other sources like bioinformatics or experimental data can be added to the standard SBM.

eSBMTools is a powerful package that makes the job of running simulations much easier. It can generate the necessary files for running  simulations with GROMACS, compile data from several simulations, produce free energy graphs, make histograms and a lot more. Above all, it is designed to be simple to use and understand for the user.

The tools are well documented so that you can write your own python scripts for more complex problems and a better control over the simulation. To help you get a feel of what eSBMTools can do and its capabilities, a few examples have been compiled and are available in the package itself.


diSTruct is a python package to generate 3d molecular structures from distance constraints. The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct; an adaptation of the generic MaxEnt-Stress graph drawing algorithm to the domain of biological macromolecules. diSTruct is fast, provides reliable structural models even from incomplete or noisy distance data and integrates access to graph analysis tools. diSTruct is written in C++, Cython and Python 3. It is available from https://github.com/KIT-MBS/distruct.git or in the Python package index under the MIT license.

We directly solve the distance geometry problem for arbitrary distance data from experimental or other sources. The key achievements are speed of interpretation, minimizing the error with respect to constraints and the ability to deal with sparse and noisy data. The rapid construction of 3D-models even for a large protein of 700 amino acids takes only on the order of 10 s on common CPUs. This allows to interactively interpret possible sources of ambiguity or errors in the input distance data and helps to improve, e.g. NMR shift assignment.

The algorithm is implemented in the python package diSTruct. diSTruct builds on Biopython (Cock et al., 2009; Hamelryck and Manderick, 2003), a toolkit for computational biology and the NetworKit (Staudt et al., 2016) package for graph analysis. Our design focus is to provide an interactive python-based toolkit that (i) provides structural models from distance data with minimal constraint errors, (ii) is able to handle noisy and incomplete data, (iii) maintains familiarity for users that know Biopython and (iv) provides an interface from biological context to a rich graph analysis suite.