A systematic computational analysis of protein sequences containing known nuclear domains led to the identification of 28 novel domain families. This represents a 26% increase in the starting set of 107 known nuclear domain families used for the analysis. Most of the novel domains are present in all major eukaryotic lineages, but 3 are species specific. For about 500 of the 1200 proteins that contain these new domains, nuclear localization could be inferred, and for 700, additional features could be predicted. For example, we identified a new domain, likely to have a role downstream of the unfolded protein response; a nematode-specific signalling domain; and a widespread domain, likely to be a noncatalytic homolog of ubiquitin-conjugating enzymes.

We investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a "small-world network", linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional space reveals three clusters that correspond to cellular compartments containing secreted, cytoplasmic, and nuclear proteins. The projection method takes into account the existence of "bridging" domains, that is, instances where two domains might not occur with each other but frequently co-occur with a third domain; in such circumstances the domains are neighbors in the projection. While the majority of domains are specific to a compartment ("locale"), and hence may be used to localize any protein that contains such a domain, a small subset of domains either are present in multiple locales or occur in transmembrane proteins. Comparison with previously annotated proteins shows that SMART domain data used with this approach can predict, with 92% accuracy, the localizations of 23% of eukaryotic proteins. The coverage and accuracy will increase with improvements in domain database coverage. This method is complementary to approaches that use amino-acid composition or identify sorting sequences; these methods may be combined to further enhance prediction accuracy.