Abstract
MotivationThe implementation of biomolecular modelling methods and analyses can be cumbersome, often carried out with in-house software reimplementing common tasks, and requiring the integration of diverse software libraries.ResultsWe present Biobox, a Python-based toolbox facilitating the implementation of biomolecular modelling methods.Availability and implementationBiobox is freely available on https://github.com/degiacom/biobox, along with its API and interactive Jupyter notebook tutorials.
Highlights
Introduction general biomolecular modelling tasksHereafter, we present Biobox for the Models rationalising sparse and low-resolution information on biomolecular structure, dynamics, and interactions can provide key insight into first time, and illustrate its main features by summarising recent published research featuring its usage.biological function at the atomic level
Protein-protein docking is the prediction of how proteins of known atomic structure assemble in specific complexes
JabberDock has been extended to transmembrane protein docking (Rudden and Degiacomi, 2021), and applied to the prediction of the bo3 oxidase dimeric structure (Olerinyova et al, 2021) by leveraging mass photometry data
Summary
Biobox features methods to transform electron densities into point clouds and vice versa, and to generate point spatial arrangements respecting predefined shapes and symmetries. Biobox leverages on Pandas indexing features to select atoms of interest and, through NumPy, enables direct access to advanced data analysis features within popular scientific computing packages (Harris et al, 2020). Protein-protein docking is the prediction of how proteins of known atomic structure assemble in specific complexes.
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