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

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 asso..., 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 [e-mail]. Austin T. Weigle (https://orcid.org/0000-0002-1619-2452) prepared this Dryad repository., , # 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-rela...