title: Structural Bridges through Fold Space.
abstract: Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.
authors.affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom.
authors.forename: Hannah
authors.initials: H
authors.lastname: Edwards
authors.affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom.
authors.forename: Charlotte M
authors.initials: CM
authors.lastname: Deane
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D011506:Proteins