hide
Free keywords:
-
Abstract:
Motivation: Coiled coils are a widespread structural motif consisting of multiple α-helices that wind around a central axis to bury their hydrophobic core. Although their backbone can be uniquely described by the Crick parametric equations, these have little practical application in structural prediction, given that most coiled coils in nature feature non-canonical repeats that locally distort their geometry. While AlphaFold has emerged as an effective coiled-coil modeling tool, capable of accurately predicting changes in periodicity and core geometry along coiled-coil stalks, it is not without limitations. These include the generation of spuriously bent models and the inability to effectively model globally non-canonical coiled coils. In an effort to overcome these limitations, we investigated whether dividing full-length sequences into fragments would result in better models.
Results: We developed CCfrag to leverage AlphaFold for the piece-wise modeling of coiled coils. The user can create a specification, defined by window size, length of overlap, and oligomerization state, and the program produces the files necessary to run structural predictions with AlphaFold. Then, the structural models and their scores are integrated into a rich per-residue representation defined by sequence-or structure-based features, which can be visualized or employed for further analysis. Our results suggest that removing coiled-coil sequences from their native context can in some case improve the prediction confidence and avoids bent models with spurious contacts. In this paper, we present various use cases of CCfrag, and propose that fragment-based prediction is useful for understanding the properties of long, fibrous coiled coils, by showing local features not seen in full-length models.
Availability and Implementation: The program is implemented as a Python module. The code and its documentation are available at https://github.com/Mikel-MG/CCfrag.
