The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition

# The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition [https://doi.org/10.5061/dryad.8sf7m0cxn](https://doi.org/10.5061/dryad.8sf7m0cxn) The data files provided in this dataset are mainly targeted towards recreating all figures (Main Text and Supplemental) from our manuscript. ## Description of the data and file structure Each figure generated from a numerical analysis, and its related files, are enumerated in different zipped directories. Each directory is named after the figure that encodes (Main Text = F1, F2, F3, F4, F6; SI = SF01 ...). Within each folder contains either a python script or a jupyter notebook, which can be used to execute the necessary codes to reproduce the figures. Raw data is either provided as pkl files or as Excel sheets. Necessary information related to distribution statistics is included as Excel workbooks. Other files not directly related to figure reproduction are provided as well. MSM-related files are provided in `msm_objects`. The directory `seeds` contains every single coordinate file used to begin production simulations for adaptive sampling of *apo* and *holo* AtSWEET13 simulations. The interactive contents within each directory are described in the file `per_directory_readme.md`. ## Sharing/Access information Files related to simulation analyses (scripts, outputs of scripts that were compiled into pkl files), Markov state model pipelines, and some topology files have also been made available on Box: [https://uofi.box.com/s/mqwnlc52xnkf6rnmron36tclv68kklv9\\](https://uofi.box.com/s/mqwnlc52xnkf6rnmron36tclv68kklv9\\) A copy of the contents with our related Github repository (`GITHUB`) is provided in zipped format as a means of version control. However, this Github repository mainly exists as a means to point towards the provided Box link (i.e., is mostly an empty repository). ## Code/Software Codes and jupyter notebooks provided here can be used to reproduce figures. Analyses unrelated to direct figure reproduction are provided on Box. These python codes can be executed using a conda environment build from either the provided `source_data.txt` or `source_data.yml`.