diSTruct v1.0: generating biomolecular structures from distance constraints.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
15 12 2019
Historique:
received: 15 05 2019
revised: 10 07 2019
accepted: 17 07 2019
pubmed: 23 7 2019
medline: 8 7 2020
entrez: 23 7 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31329240
pii: 5536876
doi: 10.1093/bioinformatics/btz578
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

5337-5338

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Oskar Taubert (O)

Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

Ines Reinartz (I)

Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

Henning Meyerhenke (H)

Department of Computer Science, Humbold-Universität zu Berlin, Berlin, Germany.

Alexander Schug (A)

John von Neumann Institute for Computing, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich 52425, Germany.

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Classifications MeSH