Data & Visualizations
Visualizations for the output job data.
Predicted Sequences
NOTE: Lower ProteinMPNN scores are better.
Sequence Statistics
General statistics related to the predicted sequences above.
Amino Acid Probabilities
Calculated amino acid probabilities based on the predicted sequences.
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 |
|---|---|
| Chains | A |
| Excluded Amino Acids | none |
| Fixed Positions | none |
| Homo-oligomer | false |
| Input Structure | 64aeee36bf3a7e1dc1ee8779.pdb |
| Invert Selection | false |
| Model Type | v_48_020 |
| Model Version | soluble |
| Number Sequences | 50 |
| Sampling Temperature | 0.10000000149011612 |
Files
The following files were either used as input(s) or produced by this job.
Output Files
Download all as a zip file:
Citations
Please cite the following if you wish to publish data produced from this job.
Dauparas, J., et al. “Robust Deep Learning–Based Protein Sequence Design Using Proteinmpnn.” Science, vol. 378, no. 6615, 2022, pp. 49–56, https://doi.org/10.1126/science.add2187.
Neurosnap Inc. - Computational Biology Platform for Research. Wilmington, DE, 2022. https://neurosnap.ai/.