DOI: 10.5061/dryad.8sf7m0cxn
title: The Arabidopsis AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognition

description: Transporters are targeted by endogenous metabolites and exogenous
 molecules to reach cellular destinations, but it is generally not
 understood how different substrate classes exploit the same transporter’s
 mechanism. Any disclosure of plasticity in transporter mechanism when
 treated with different substrates becomes critical for developing general
 selectivity principles in membrane transport catalysis. Using extensive
 molecular dynamics simulations with an enhanced sampling approach, we
 select the Arabidopsis sugar transporter AtSWEET13 as a model system to
 identify the basis for glucose versus sucrose molecular recognition and
 transport. Here we find that AtSWEET13 chemical selectivity originates
 from a conserved substrate facial selectivity demonstrated when committing
 alternate access, despite mono-/di-saccharides experiencing differing
 degrees of conformational and positional freedom throughout other stages
 of transport. However, substrate interactions with structural hallmarks
 associated with known functional annotations can help reinforce selective
 preferences in molecular transport.
description: Molecular dynamics trajectories were generated using AMBER18,
 performed on NCSA Blue Waters supercomputing resources. Analyses were
 performed with python scripts. This work has been
 performed by Austin T. Weigle & Diwakar Shukla at the University
 of Illinois Urbana-Champaign. This Dryad dataset has
 been made available in compliance with the "minimum
 dataset"/source data requirement per the Data Availability guidelines
 by Communications Biology. For further inquiries
 concerning further data availability, please contact Prof. Diwakar Shukla
 at diwakar@illinois.edu. Austin T. Weigle (https://orcid.org/0000-0002-1619-2452) prepared this Dryad repository.
description: # 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`.