Data & Visualizations
Visualizations for the output job data.
Additional Controls
AlphaFold2 predicts 5 different structures using different weights and ranks them from best to worst by their mean pLDDT for monomers or 80*iptm + 20*ptm for multimers. The Uncertainty metric ranges between 0 and 100 where 0 means high certainty and 100 means high uncertainty (lower is better). This AlphaFold2 implementation uses ColabFold as it is faster and more accurate.
Predicted Metrics
| Model Rank | Mean pLDDT | Max PAE | pTM | ipTM | pDockQ | Uncertainty | Overall Quality |
|---|
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)
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 |
|---|---|
| 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: AGPYAVELGEAGTFTILSKSGITDVYPSTVTGNVGTSPITGAALLLNCDEVTGAMYTVDSAGPLPCSINSPYLLELAVSDMGIAYNDAAGRVPADHTELGTGEIGGLTLEPGVYKWSSDVNISTDVTFNGTMDDVWIMQISGNLNQANAKRVTLTGGALAKNIFWQVAGYTALGTYASFEGIVLSKTLISVNTGTTVNGRLLAQTAVTLQKNTINAPTEQYEEAPL |
| Template Mode | none |
| Training Mode | false |
| Use Amber | true |
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.
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
If using MSA, please also cite: Steinegger, M., Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988
Neurosnap Inc. - Computational Biology Platform for Research. Wilmington, DE, 2022. https://neurosnap.ai/.