Protein crystallography produces most of the protein structures used in structure-based drug design. The process of protein structure determination is computationally intensive and error-prone because many software packages are involved. Here, we attempt to support the reproducibility of this computational work by using Jupyter notebooks to document the decisions made, the code, and selected output. We have made libraries of code templates to ease running the crystallography packages in Jupyter notebooks when editing them with JupyterLab or Colab. Our combined use of GitHub, snippet libraries, Jupyter notebooks, JupyterLab, and Colab will help modernize the computing done by structural biologists.

Keywords:literate programmingreproducible researchscientific rigorelectronic notebooksJupyterLabJupyter notebookscomputational structural biologycomputational crystallographybiomolecular crystallographyprotein crystallographybiomolecular structurebiomedical researchprotein*drug interactionsRNA*drug interactionsmolecular graphicsmolecular visualizationscientific communicationmolecular artworkcomputational molecular biophysics