1. ABOUT THE DATASET -------------------- Title: Predictive modelling of the COMATOSE transporter reveals a conserved ligand binding pocket for acyl-CoAs and validates previous functional studies Creator(s): Foteini Bifsa[1,2], Katie Simmons[1,2] Organisation(s): [1] School of Biomedical Sciences, University of Leeds, Leeds, LS2 9JT, UK [2[ Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK Rights-holder(s):Unless otherwise stated, Copyright 2025 University of Leeds Publication Year: 2025 Description: Models of plant peroxisomal transporter COMATOSE (CTS) generated with AlphaFold2/3. Three distinct states were produced of the apo, ATP-bound and ADP-bound conformations. Dockings of C22:0-CoA and CoA alone to models of plant peroxisomal transporter COMATOSE (CTS). These models include the apo, ATP-bound and ADP-bound conformations generated with AlphaFold2/3. Cite as: Bifsa F., Simmons K. (2025): Dataset for 'Predictive modelling of the COMATOSE transporter reveals a conserved ligand binding pocket for acyl-CoAs and validates previous functional studies.' University of Leeds. https://doi.org/10.5518/1759 Related publication: Bifsa F., Simmons K., Muench S.P., Baker A. (2025). ‘Predictive modelling of the COMATOSE transporter reveals a conserved ligand binding pocket for acyl-CoAs and validates previous functional studies.’ Scientific Reports, 2025. (Submitted) Contact: Prof Alison Baker, A.baker@leeds.ac.uk 2. TERMS OF USE --------------- Copyright 2025 University of Leeds. Unless otherwise stated, this dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/. 3. PROJECT AND FUNDING INFORMATION ---------------------------------- Title: Structural insights into the mechanism of a peroxisomal ABC transporter Dates: Sept 21 - Sept 25 Funding organisation: BBSRC Grant no.: BB/T007222/1 4. CONTENTS ----------- File listing Predicted models of COMATOSE CTS_ATP-bound_AF3_model_4.cif CTS_ADP-bound_AF3_model_0.cif CTS_ADP-bound_AF3_model_1.cif CTS_ADP-bound_AF3_model_2.cif CTS_ADP-bound_AF3_model_3.cif CTS_ADP-bound_AF3_model_4.cif CTS_Apo_AF2_model_0.pdb CTS_Apo_AF2_model_1.pdb CTS_Apo_AF2_model_2.pdb CTS_Apo_AF2_model_3.pdb CTS_Apo_AF2_model_4.pdb CTS_Apo_AF3_model_0.cif CTS_Apo_AF3_model_1.cif CTS_Apo_AF3_model_2.cif CTS_Apo_AF3_model_3.cif CTS_Apo_AF3_model_4.cif CTS_ATP-bound_AF3_model_0.cif CTS_ATP-bound_AF3_model_1.cif CTS_ATP-bound_AF3_model_2.cif CTS_ATP-bound_AF3_model_3.cif Docking dataset C22_AF2_apo_best.pdb C22_AF3_ADP_best.pdb C22_AF3_ATP_best.pdb CoA_AF2_apo_best.pdb CoA_AF3_ADP_best.pdb CoA_AF3_ATP_best.pdb CTS Dockings Whole.pse CTS_AF3_ADP.pdb CTS_AF3_ATP.pdb CTS_full_AF2_apo_prep.pdb 5. METHODS ---------- 1. AlphaFold2 and AlphaFold3 were used to generate the predictive models of CTS. The full canonical sequence of the wildtype CTS (Uniprot identification number Q94FB9) was the input in each of the runs. AlphaFold2 was used to predict protein structures via the official Google Colab notebook provided by DeepMind. AlphaFold2 models were downloaded from June 2023. Predicted protein structures generated using AlphaFold3 were done without accessing the online AlphaFold Server. Instead, models were obtained by running the AlphaFold3 pipeline locally using the publicly available implementation provided by DeepMind. Additional inputs were ligands: ADP or ATP and both with magnesium ions added. The pTM scores were pooled together from the output of each AlphaFold run and shown in violin plots. 2. These were performed according to methods published in David. J. Wright et al., 2020. Briefly, the region of CTS assigned for docking studies was chosen to encompass the entirety of the TMDs and excluded the NBDs as the latter is location of ATP binding and hydrolysis. A 20 Å clip of the CTS structure around these residues was termed as the receptor for docking studies using Glide (Schrödinger Release 2024-3, Glide, Schrödinger, LLC, New York, NY, 2024). The CTS structure was prepared using the Protein Preparation Wizard in the Maestro Graphical User Interface (GUI). This aimed to remove any steric clashes of amino acid side chains and optimise the position of hydrogen atoms to facilitate docking studies. The receptor grid was generated using Schrödinger software, allowing docking of ligands in a 20×20×20 Å grid. The C22-CoA and CoA ligands were prepared using the LigPrep module (Schrödinger Release 2024-3, Glide, Schrödinger, LLC, New York, NY, 2024) to produce an energy-minimised 3D structure. Docking of C22-CoA and CoA was carried out using the Glide module of Schrödinger software using the XP mode with flexible ligand sampling and biased sampling of torsions for all predefined functional groups. Epik state penalties were added to the docking score. A maximum of 10 poses for the ligand was requested in the output file and post-docking minimisation was carried out. Associated references: - Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021) Highly accurate protein structure prediction with AlphaFold. Nature 2021 596:7873, Nature Publishing Group 596, 583–589 https://doi.org/10.1038/s41586-021-03819-2 - Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., et al. (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024 630:8016, Nature Publishing Group 630, 493–500 - Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., et al. (2004) Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening. J Med Chem, J Med Chem 47, 1750–1759 https://doi.org/10.1021/JM030644S, - Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., et al. (2004) Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem, J Med Chem 47, 1739–1749 https://doi.org/10.1021/JM0306430 - Wright, D. J., Simmons, K. J., Johnson, R. M., Beech, D. J., Muench, S. P. and Bon, R. S. (2020) Human TRPC5 structures reveal interaction of a xanthine-based TRPC1/4/5 inhibitor with a conserved lipid binding site. Commun Biol, Nature Research 3, 1–11 https://doi.org/10.1038/S42003-020-01437-8;TECHMETA