<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have
not been determined, but that represent an as yet unknown structural superfamily
or fold. Once such a structure is solved, it can be used as a template for modelling
homologous proteins. This will aid in unveiling the structural diversity of the protein
space. Currently, no reliable method for accurate 3D structural prediction is available
when a sequence or a structure homologue is not available. Here we present a
systematic methodology for selecting target proteins whose structure is likely to
adopt a new, as yet unknown superfamily or fold. Our method takes advantage
of a global classification of the sequence space as presented by ProtoNet-3D, which
is a hierarchical agglomerative clustering of the proteins of interest (the proteins in
Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in
the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet
structurally solved, along with the probability of each of the proteins belonging to a
new superfamily or fold. The sorted list has been self-validated against real structural
data that was not available when the predictions were made. The practical application
of using our computational–statistical method to determine novel superfamilies for
structural genomics projects is also discussed.</p>
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Comp Funct Genomics. 2003 Oct; 4(5):542-548