Data & Visualizations
Various data visualizations for this jobs output data.
| Model Rank | Mean pLDDT | Max PAE | pTM |
|---|
This implementation outputs 5 structures per protein ranked from best to worst by their mean pLDDT for monomers or 80*iptm + 20*ptm for multimers.
Important Metrics
These plots provide additional confidence metrics that can be used to better assess the predicted structures. For more details as well as tips and tricks we highly recommend checking out our blog post on interpreting AlphaFold2 results.
predicted Local Distance Difference Test (pLDDT)
MSA Sequence Coverage
Predicted Aligned Error (PAE)
Config
The configuration that was used for this job.
| Configuration Setting | Set Value |
|---|---|
| Custom MSA | none |
| Custom Template | none |
| MSA Mode | MMseqs2 (UniRef+Environmental) |
| Model Type | AlphaFold2-ptm |
| Number Ensembles | 1 |
| Number Recycles | 6 |
| Pair Mode | unpaired+paired |
| Target Sequence(s) | Antifreeze_protein |
| Template Mode | none |
| Training Mode | false |
| Use Amber | true |
Files
The following files have been produced by this job.
Download all as a zip file:
Citations
Please cite the following if you wish to publish data produced from this job.
Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) doi: 10.1038/s41592-022-01488-1
Jumper et al. "Highly accurate protein structure prediction with AlphaFold." Nature (2021) doi: 10.1038/s41586-021-03819-2
If you're using AlphaFold-multimer, please also cite: Evans et al. "Protein complex prediction with AlphaFold-Multimer." biorxiv (2021) doi: 10.1101/2021.10.04.463034v1
Neurosnap Inc. Accelerate your research with the latest in computational biology. Wilmington, DE, 2022. https://neurosnap.ai/.