distruct 1.0.3

a package to generate 3d molecular structures from distance constraints

diSTruct

python package name: distruct Implementation of Maxent-stress Optimization of Biomolecular systems.

diSTruct is in essence an implementation of the MaxEnt-Stress graph drawing algorithm (Gansner, E.; Hu, Y. and North, S. C.: "A Maxent-Stress Model for Graph Layout" in IEE Trans. Vis. Comput. Graph. 2013) for generating biomolecular structures from distance constraints.

Requirements

The actual structure generation in diSTruct is build on the MaxEnt-stress graph drawing implementation in NetworKit.

It aims to conveniently combine this with the PDB module in Biopython.

It also depends on lxml and requires cython.

Installation Instructions

First install NetworKit following the instructions in their homepage. Make sure you can import networkit without error. Sometimes there are issues with automatically installing all dependenicies. Simply install missing packages manually.

Install cython, Biopython and lxml. Install diSTruct with

pip install distruct

Publications

Please cite

@article{10.1093/bioinformatics/btz578,
    author = {Taubert, Oskar and Reinartz, Ines and Meyerhenke, Henning and Schug, Alexander},
    title = "{diSTruct v1.0: generating biomolecular structures from distance constraints}",
    journal = {Bioinformatics},
    year = {2019},
    month = {07},
    abstract = "{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.Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btz578},
    url = {https://doi.org/10.1093/bioinformatics/btz578},
    note = {btz578},
    eprint = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz578/29161648/btz578.pdf},
}

Tests

To run the python tests install pytest an run python -m pytest in /path/to/distruct/tests