Combinatorial and graph theoretical approach to systems biology and mol. evo.

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Previously, we developed several methods to predict domain-domain interactions (1,3,6). Most recently we extended one of our recently developed parsimony approach (2). The modified method allowed for preferential selection of the so-called co-occurring domains as possible mediators of interactions between proteins. It has been proposed that such co-occurring domains are significantly more likely to mediate interactions between proteins than other domain pairs. Therefore, we also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the new method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset which is typically used as a gold standard test set. Consequently, we concluded this test set is not representative of genome wide protein interactions and results benchmarked using this data set must be treated with caution. Building on our previous work (4) on graph theoretical studies of biological networks we investigated the relation between topological properties of protein interaction networks end essentiality. In particular we showed that previously proposed network theoretical explanation for essentiality are incorrect. Instead, the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins (7). Our studies of evolutionary relationships focused on comparative analysis of evolutionary pressure in different clades within groups of orthologous proteins (5). Our results indicate that the evolutionary pressure acting on the informational ortholog groups is not uniform across different sub-groups of organisms in this study. This suggests that fine-tuning of these informational group proteins in each lineage makes them less exchangeable between lineages. In part, this differeintation might relate to them functioning as parts of multi-protein complexes with several distinct subunits conserved subunits. In contrast, the non-informational groups might not experience such lineage-specific differences in selective pressure, as they usually catalyze individual reactions in metabolic pathways with the flux of substrates mediating most functional interactions between them.