Data & Visualizations
Visualizations for the output job data.
Metrics & Scores
Predictions are ranked by DiffDock's scoring function and ordered from best to worst prediction. Click on a row to display its prediction in the above protein viewer.
AI models produce responses and outputs through sophisticated algorithms and learning techniques, which may result in inaccuracies. By engaging with this model, you accept responsibility for any potential harm resulting from its responses or outputs.
Config
The configuration that was used for this job.
| Configuration Setting | Set Value |
|---|---|
| Dense Sampling | false |
| Docking Mode | Protein-RNA |
| Flexible Backbone | false |
| Ligand Structure | 6487c7540dd6ff4171363619.pdb |
| Receptor Structure | 6487c7530dd6ff4171363618.pdb |
| Simulation Steps | 100 |
Files
The following files were either used as input(s) or produced by this job.
Input Files
Download all as a zip file:
Output Files
Download all as a zip file:
Citations
Please cite the following if you wish to publish data produced from this job.
LightDock: a new multi-scale approach to protein–protein docking Brian JimĂ©nez-GarcĂa, Jorge Roel-Touris, Miguel Romero-Durana, Miquel Vidal, Daniel JimĂ©nez-González and Juan Fernández-Recio Bioinformatics, Volume 34, Issue 1, 1 January 2018, Pages 49–55, https://doi.org/10.1093/bioinformatics/btx555
LightDock goes information-driven Jorge Roel-Touris, Alexandre M.J.J. Bonvin, Brian JimĂ©nez-GarcĂa Bioinformatics, btz642; doi: https://doi.org/10.1093/bioinformatics/btz642
Integrative Modeling of Membrane-associated Protein Assemblies Jorge Roel-Touris, Brian JimĂ©nez-GarcĂa & Alexandre M.J.J. Bonvin Nat Commun 11, 6210 (2020); doi: https://doi.org/10.1038/s41467-020-20076-5
Neurosnap Inc. - Computational Biology Platform for Research. Wilmington, DE, 2022. https://neurosnap.ai/.