Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. 1. General Remarks Many important findings in pharmacology, cell biology, and pathobiology have been achieved with the aid of virtual interactomics including computer-aided structural analysis, prediction and in silico simulation of interacting sites, protein complexes, and interaction networks. Virtual interactomics has been developed in the last thirty years, and it is in fact based on gradual bioinformatic processing of experimental data. These data were usually obtained from individual studies of interactions, and various large-scale experimental methods such as the two-hybrid system, phage display library studies reverse interactomics, SPOT arrays or microarray studies, and extended sequence studies [1–7]. In addition to sequence data, three-dimensional (3D) structures are ever more frequently required for interactomic predictions. X-ray crystallography or nuclear magnetic resonance studies represent the most frequent sources of 3D structures, whereas combination of electron microscopy of molecular complexes with X-ray crystallography turns out to be interesting for the same purpose [8–11]. Alternatively, sophisticated 3D structure simulations such as homology modeling or combination of cryoelectron microscopy densities, and
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