Biomolecular Crystallographic Computing with Jupyter
The ease of use of Jupyter notebooks has helped biologists enter scientific computing,
especially in protein crystallography, where a collaborative community develops extensive
libraries, user-friendly GUIs, and Python APIs. The APIs allow users to use the libraries in Jupyter.
To further advance this use of Jupyter, we developed a collection of code fragments that use
the vast Computational Crystallography Toolbox (cctbx) library for novel analyses. We made versions
of this library for use in JupyterLab and Colab. We also made versions of the snippet library
for the text editors VS Code, Vim, and Emacs that support editing live code cells in Jupyter
notebooks via the GhostText web browser extension. Readers of this paper may be inspired to adapt this latter capability
to their domains of science.
literate programming, reproducible research, scientific rigor, electronic notebooks, JupyterLab, Jupyter notebooks, Colab notebook, OnDemand notebooks, computational structural biology, computational crystallography, biomolecular crystallography, protein crystallography, biomolecular structure, computational molecular biophysics, biomedical research, data visualization, scientific communication, GhostText, text editors, snippet libraries, SciPy software stack, interactive software development
DOI10.25080/gerudo-f2bc6f59-004