The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.
"et al., 2010). Computational prediction maps are fast and efficient to implement, and usually include satisfyingly large numbers of nodes and edges, but are necessarily imperfect because they use indirect information (Plewczynski and Ginalski, 2009). While high-throughput maps attempt to describe unbiased , systematic, and well-controlled data, they were initially more difficult to establish, although recent technological advances suggest that near completion can be reached within a few years for highly reliable, comprehensive protein-protein interaction and gene regulatory network maps for human (Venkatesan et al., 2009). "
[Show abstract][Hide abstract] ABSTRACT: Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
[Show abstract][Hide abstract] ABSTRACT: Determining the primary sequences of informational macromolecules is no longer a limiting factor for our ability to completely understand the biological functioning of cells and organisms. Similarly, our understanding of transcriptional regulation (transcriptomics) has been greatly enhanced by the availability of microarrays. Our next hurdle is to learn the biochemical functions of all the gene products (proteomics) and the totality of all the interactions among them (interactomics). Using traditional biochemical methods, this will take a very long time. More efficient methods are needed to address these questions, or at least to suggest possible candidates for further testing. High-resolution imaging using molecule-specific tags will reveal details of cellular architecture that are expected to provide additional insights and clues about the interactions and functions of many gene products. Computer modeling of macromolecular structures and functional systems will be of key importance. We present here a brief historical and futuristic perspective of genomics and some of its other 'omics offshoots in the post-genomic era.
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