CCfrag: A Tool for Modeling Coiled-Coil Fragments with AlphaFold
Author Information
Author(s): Martinez-Goikoetxea Mikel, Lengauer Thomas
Primary Institution: Max Planck Institute for Biology
Hypothesis
Dividing full-length sequences into fragments will result in better models for coiled coils.
Conclusion
Using CCfrag improves the modeling quality of challenging coiled coils by simplifying the prediction task.
Supporting Evidence
- CCfrag improves prediction confidence for coiled coils by modeling them in fragments.
- Fragment-based modeling reveals local structural properties not seen in full-length models.
- Using CCfrag, the pLDDT scores for EEA1 were significantly better than those of the full-length prediction.
Takeaway
This study shows that breaking down long protein sequences into smaller pieces can help scientists make better predictions about their structure.
Methodology
CCfrag is a Python module that divides protein sequences into overlapping fragments and integrates AlphaFold predictions into a per-residue representation.
Potential Biases
The study suggests that AlphaFold's training data may introduce bias in modeling long coiled coils.
Limitations
CCfrag does not implement a wrapper to run AlphaFold, which may limit its usability depending on the user's setup.
Digital Object Identifier (DOI)
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