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OpenMDlr: parallel, open-source tools for general protein structure modeling and refinement from pairwise distances.
Davidson, Russell B; Woods, Jess; Effler, T Chad; Thavappiragasam, Mathialakan; Mitchell, Julie C; Parks, Jerry M; Sedova, Ada.
Afiliação
  • Davidson RB; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Woods J; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Effler TC; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Thavappiragasam M; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Mitchell JC; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Parks JM; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
  • Sedova A; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
Bioinformatics ; 38(12): 3297-3298, 2022 06 13.
Article em En | MEDLINE | ID: mdl-35512391
ABSTRACT

SUMMARY:

Easy-to-use, open-source, general-purpose programs for modeling a protein structure from inter-atomic distances are needed for modeling from experimental data and refinement of predicted protein structures. OpenMDlr is an open-source Python package for modeling protein structures from pairwise distances between any atoms, and optionally, dihedral angles. We provide a user-friendly input format for harnessing modern biomolecular force fields in an easy-to-install package that can efficiently make use of multiple compute cores. AVAILABILITY AND IMPLEMENTATION OpenMDlr is available at https//github.com/BSDExabio/OpenMDlr-amber. The package is written in Python (versions 3.x). All dependencies are open-source and can be installed with the Conda package management system. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteínas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article