The data provided corresponds to the molecular dynamics simulations performed for the wild-type (WT) and E220A mutant of HvoExoI glycosyl hydrolase in covalent complex with glucose (reaction intermediate) and with laminaripentaose bound. The data was used to study the effect of the mutation and how this area affects the correct positioning of the catalytic water and, thus, catalysis.

Three repicates of 1 microsecond each were performed for each system using the AMBER program. For each replicate (r0, r1 and r2), around 10,000 PDB files of the enzyme:substrate complexes are provided.

METHODOLOGICAL INFORMATION

A detailed descriprion of the methods can be found in the related paper: S. Luang, X. Fernández-Luengo, V. A. Streltsov, J. -D. Maréchal, L. Masgrau and M. Hrmova. “The structure and dynamics of water molecule networks underlie catalytic efficiency in a glycoside exo-hydrolase”, Comms Biol (2025) DOI: to be updated when available

System solvation, coordinate, and topology file acquisitions for standard protein residues and ligands (prmtop and inpcrd files) were performed in tleap of the AMBERTools21. The systems were parametrized using the Amber ff99SB-ILDN force fields for proteins, carbohydrates, and TIP3P water molecules. The parametrisation of the covalent non-standard Glc-D285 adduct was performed with GAFF, and the atomic charges were obtained using initial geometry optimisation followed by the calculation of restrained electrostatic potential (RESP) charges with Gaussian16. All systems were solvated in TIP3P water molecules and neutralised with Na+ ions, using a cubic box, with standard periodic boundary conditions set. Simulations were carried out in AMBER 22 with CUDA acceleration. The cMD simulation protocol consisted of (i) initial minimisation (105 steps); (ii) heating (100 ps, with protein backbones restrained); (iii) NVT equilibration (100 ps, with restrained backbones); (iv) NPT equilibration (500 ps, unrestrained); and (v) NPT production at 300 K under the pressure of 1.0 atm. Each simulation was carried out with a time step of 2 ps using the SHAKE algorithm to constrain bonds involving hydrogens. A cutoff value of 14.0 Å was used for non-bonding interactions and the Particle Mesh Ewald method for long-range electrostatics. Production simulations were run for 1000 ns (one μs) in all cases with three replicates of each system.

1. Description of methods used for collection-generation of data:
2. Case, D. A. et al. Amber 2021. University of California, San Francisco, USA (2021).
3. Lindorff-Larsen, K. et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78, 1950–1958 (2010).
4. Kirschner, K. N. et al. GLYCAM06: a generalizable biomolecular force field. Carbohydrates. J. Comput. Chem. 29, 622–655 (2008).
5. Jorgensen, W., Chandrasekhar, J., Madura, J., Impey, R. & Klein, M. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
6. Frisch, M. J. et al. Gaussian 16, Revision C.01 (2016).
7. Case, D. A. et al. AMBER 2022. University of California, San Francisco, USA (2022).
8. Ryckaert, J. P., Ciccotti, G. & Berendsen, J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (2017).

9. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089 (1993).

10. Methods for processing the data: All files can be read with text file editors and visualized with any software that reads standard PDB files for 3D models.