Intraproteomic Networks: New Forays Into Predicting Interaction Partners
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, 20894, USA
This extract was created in the absence of an abstract.
Biological treasure troves of complete protein complements of diverse organisms (proteomes) have been unveiled in the past few years as a result of the tremendous success of genome projects. The fundamental fascination of most biochemists and molecular biologists is how the different polypeptides comprising the proteome interact to conduct “business” in various biological systems. The flood of genomic data has made large-scale attacks on this problem through computational and experimental methods very feasible. On the computational side, the main progress has been in the form of identification and classification of the individual protein domains, thereby helping to narrow down to the actual determinants of the intraproteomic interactions (Ponting et al. 2000; Lander et al. 2001). On the experimental side, high-throughput proteomic analysis has yielded protein-interaction maps for different organisms at an unprecedented level of detail (Matthews et al. 2001; Tucker et al. 2001). Initial analysis of this data reveals that the interactions within the proteome of an organism constitute a scale-free network characterized by hubs of highly connected polypeptides, each of which interact with several proteins with few or no further connections (Snel et al. 2002; Wolf et al. 2002).
Despite these advances, the precise set of changing interactions that are related to the organism's responses to changing environments, or those that are involved in development and differentiation of multicellular organisms, is not easily deduced from these studies. Furthermore, the exact determinants of the interactions in a polypeptide and the effects of modifications on them cannot be extrapolated directly from these large-scale studies. This is where a new genre of computational studies could provide potentially interesting results. Essentially, these studies would need to go beyond the identification of the individual modules involved in interactions and predict some of the actual interactions themselves. While the great structural …











