Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites

The supplementary materials for the article "Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites" by Popov et al. It includes the following data:

Supplementary Data 1: List of top 16 binding sites predicted with BiteNet
Supplementary Data 2: List of top 209 docked drug-like molecules along with molecular properties
Supplementary Data 3: RBD RMSD profiles for the selected 20 drug-like molecules
Supplementary Data 4: RBD and Binding site RMSF profiles for the selected 20 drug-like molecules
Supplementary Data 5: MSA for the Spike protein
Supplementary Data 6: Topological importance and amino acid conservation calculated for the Spike protein
Supplementary Data 7: Binding poses of the drug-like candidates superimposed to the crystallographic structures observed in PDB.
Supplementary Data 8: Structural model of Spike with opened binding site used for the molecular docking.
Supplementary Data 9: Input files used for the molecular dynamics simulation of Spike bound to the experimentally validated hit candidate 8011-6716.
Supplementary Data 10: Raw experimental data of the luminescence signal and infection efficacy used in Figures 6c,d.