Abstract
α-helices are deformable secondary structural components regularly observed in protein folds. The overall flexibility of an α-helix can be resolved into constituent physical deformations such as bending in two orthogonal planes and twisting along the principal axis. We used Principal Component Analysis to identify and quantify the contribution of each of these dominant deformation modes in transmembrane α-helices, extramembrane α-helices, and α-helices in soluble proteins. Using three α-helical samples from Protein Data Bank entries spanning these three cellular contexts, we determined that the relative contributions of these modes towards total deformation are independent of the α-helix's surroundings. This conclusion is supported by the observation that the identities of the top three deformation modes, the scaling behaviours of mode eigenvalues as a function of α-helix length, and the percentage contribution of individual modes on total variance were comparable across all three α-helical samples. These findings highlight that α-helical deformations are independent of cellular location and will prove to be valuable in furthering the development of flexible templates in de novo protein design.
Highlights
ObjectivesWe aim to expand on that research by elaborating on how the dominant deformation modes and scaling behaviour depend on the location of the α-helix in the cell, namely, whether the protein is surrounded by membrane or aqueous environments
The earliest computational protein design strategies focused on rigid backbone templates
Upon performing Principal Component Analysis (PCA), the total deformation of the α-helical sample was segmented into constituent modes, with each mode describing a part of the total deformation
Summary
We aim to expand on that research by elaborating on how the dominant deformation modes and scaling behaviour depend on the location of the α-helix in the cell, namely, whether the protein is surrounded by membrane or aqueous environments. We aim to substantiate and validate the conclusions reached by Emberly et al [4] using a dataset that is over 500% the size of theirs
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