Supporting Data for: A Coarse-Grained MARTINI Model for Mucins

This repository contains the MARTINI 31 parameters used to develop the coarse-grained model of the mucin, MUC5B. The coarse-grained bonded parameters were generated through implementing a modified Boltzmann inversion algorithm from the PyCGTOOL2 package on a series of atomistic simulations of 18 short glycopeptides that comprise the MUC5B glycoprotein. This uses the MINI2 structure of MUC5B from a previous atomistic simulation study.3 The dataset relates to the following preprint: Kanesalingam, T.; Weiand, E.; Cann, P.; Masen, M.; Ewen, J. P. A Coarse-Grained MARTINI Model for Mucins. ChemRxiv Preprint 2025. https://doi.org/10.26434/chemrxiv-2025-c4f4t. Simulation Setup Files The Glycopeptides folder contains the input and output PyCGTOOL2 parameterisation files for each of the 18 short glycopeptides, along with the LAMMPS4 parameter, topology and script files for the coarse-grained simulation of the glycopeptides. The output files from PyCGTOOL also include GROMACS5 parameter and topology files. Validation data for the coarse-grained parameters of each of the glycopeptides are also provided. The MUC5B folder contains the LAMMPS and GROMACS parameter, topology and script files for the coarse-grained molecular dynamics simulations of the resulting MUC5B model. A coarse-grained model implementing the standard MARTINI 31 and downscaled MARTINI 36 Lennard-Jones parameters of the glycoprotein backbone are provided. For both of these models, the number of repeats of MUC5B's Proline-Threonine-Serine (PTS)-rich domains are varied from 1 (30 amino acids) to 5 (150 amino acids), with the LAMMPS and GROMACS parameter, topology and script files for all models also provided. Files for atomistic simulations with CHARMM36m7 used for validation of the MARTINI 3 parameters are also available for both the glycopeptides and MUC5B models. The parameters and topology used for the CHARMM36m atomistic simulations were generated using the Glycan Reader & Modeler tool8-10 from the CHARMM-GUI11. Simulation Trajectories Files The CHARMM36m and MARTINI 3 trajectories (*.lammpstrj) generated from LAMMPS, which were used for the development and validation of the MARTINI 3 parameters, have been provided for the 18 individual glycopeptides and the 30-amino acid MUC5B glycopeptide. The water solvent has been removed from all trajectories in this repository. References Souza, P. C. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nature Methods 2021, 18, 382–388. https://doi.org/10.1038/s41592-021-01098-3 Graham, J. A.; Essex, J. W.; Khalid, S. PyCGTOOL: Automated Generation of Coarse-Grained Molecular Dynamics Models from Atomistic Trajectories. Journal of Chemical Information and Modeling 2017, 57, 650–656. https://doi.org/10.1021/acs.jcim.7b00096 Kearns, F. L.; Rosenfeld, M. A.; Amaro, R. E. Breaking Down the Bottlebrush: Atomically Detailed Structural Dynamics of Mucins. Journal of Chemical Information and Modeling 2024 64, 7949-7965. https://doi.org/10.1021/acs.jcim.4c00613 Thompson, A. P.; Aktulga, H. M.; Berger, R.; Bolintineanu, D. S.; Brown, W. M.; Crozier, P. S.; in ’t Veld, P. J.; Kohlmeyer, A.; Moore, S. G.; Nguyen, T. D.; Shan, R.; Stevens, M. J.; Tranchida, J.; Trott, C.; Plimpton, S. J. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications 2022, 271, 108171. https://doi.org/10.1016/j.cpc.2021.108171 Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1-2, 19-25. https://doi.org/10.1016/j.softx.2015.06.001 Thomasen, F. E.; Skaalum, T.; Kumar, A.; Srinivasan, S.; Vanni, S.; Lindorff-Larsen, K. Rescaling protein-protein interactions improves Martini 3 for flexible proteins in solution. Nature Communications 2024, 15, 6645. https://doi.org/10.1038/s41467-024-50647-9 Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nature Methods 2017, 14, 71–73. https://doi.org/10.1038/nmeth.4067 Jo, S.; Song, K. C.; Desaire, H.; MacKerell, A. D.; Im, W. Glycan reader: Automated sugar identification and simulation preparation for carbohydrates and glycoproteins. Journal of Computational Chemistry 2011, 32, 3135–3141. https://doi.org/10.1002/jcc.21886 Park, S.-J.; Lee, J.; Patel, D. S.; Ma, H.; Lee, H. S.; Jo, S.; Im, W. Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank. Bioinformatics 2017, 33, 3051–3057. https://doi.org/10.1093/bioinformatics/btx358 Park, S. J.; Lee, J.; Qi, Y.; Kern, N. R.; Lee, H. S.; Jo, S.; Joung, I.; Joo, K.; Lee, J.; Im, W. CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates. Glycobiology 2019, 29, 320–331. https://doi.org/10.1093/glycob/cwz003 Jo, S.; Kim, T.; Iyer, V. G.; Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry 2008, 29, 1859–1865. https://doi.org/10.1002/jcc.20945