PMC:3975954 / 2012-6944 JSONTXT

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majority of gene products that crowd a living cell interact, at least transiently, with other protein molecules. Virtually all cellular events, such as signal transduction, intracellular transport, DNA replication, transcription, translation, splicing, secretion, cell cycle control and intermediary metabolism, are mediated by protein-protein interactions (PPIs) [1]. The same applies to host-pathogen systems, where PPIs are essential in the establishment of infection [2]. The binding domains of interacting proteins reveal high structural and physical-chemical affinity with an associated degree of conservation. This is further evidenced by the fact that close protein homologs frequently interact in the same way [3-7]. With this in mind, we can expect understanding of the human interactome to provide insight into physiopathological mechanisms [8].\nNumerous experimental techniques have been explored to attain the human interactome: two-hybrid screening [9,10], affinity purification mass spectrometry [11], DNA microarrays [12], protein microarrays [13-15], synthetic lethality [16], phage display [17], X-ray crystallography and nuclear magnetic resonance spectroscopy [18], fluorescence resonance energy transfer [19], surface plasmon resonance [20], atomic force microscopy [21], and electron microscopy [22]. These methods have major drawbacks that render them non-applicable in large-scale PPI prediction, namely the amount of time, associated cost and minimal protein interaction network coverage per run. Additionally, high-throughput approaches are also often associated with low-specificity and large numbers of both false negatives and false positives [23]. Moreover, these techniques were developed to detect intra-species PPIs, which renders them sub-optimal in inter-species PPI identification. Still, experimental methods remain the only viable methodology to validate PPIs.\nAs an alternative to experimental methods, a wide range of computational approaches for the prediction of intra-species PPIs have been proposed. Computational methods can be categorized according to the types of information they analyze. One common approach consists of using text mining to extract known PPIs from the biomedical literature [24]. Additionally, there are methods based on genomic data (gene neighborhood [25-28], gene fusion [29,30], phylogenetic profiles [31-33], codon usage similarity [34]), protein structure (homology-based method [35], threading-based method [36]), domain information (single domain pairs [37-41], multi-domain pairs [42,43]), protein sequence [44-56], and Gene Ontology (GO) [57] annotation semantic similarity ([58-61]). In contrast, computational efforts to predict inter-species PPIs have been very limited. Dyer et al.[2] combined domain information with a maximum likelihood estimator algorithm [37], while Davis et al.[62] adapted an approach following the threading-based method [36]. To provide a better prediction, Tastan et al.[63] applied a method combining multiple data sources, and used a random forest classifier to predict interactions between HIV-1 virus and human proteins. Despite these advances, the interactomes of several species are still far from complete. Nonetheless, the results of some of these studies provide great working knowledge of the characteristics of protein and gene interaction networks. For instance, the topological characteristics of protein interaction networks (PINs) have been proven to reflect the functionality of the interacting genes. This was demonstrated in yeast, where essential genes were more likely to be well connected and globally centered in the PIN [64,65].\nHere we present a computational model to predict inter-species PPIs within the human oral cavity, an environment particularly prone to bacterial colonization. This is mostly due to the fact that human, microbial and environmental factors interact in a dynamic equilibrium within the human oral cavity [66]. Determination of the salivary interactome will clarify the role of saliva in oral biology and enable the identification of disease biomarkers. The presence of blood exudate proteins and exfoliated epithelial cells in saliva suggest it may be an alternative to blood as a diagnostic fluid in many instances. Additionally, if we consider the systemic nature of saliva, the ease and low cost associated with its handling, and the minimal risk linked to its collection for both medical staff and the patient, the reason for studying the oral cavity becomes clear [67].\nAs a result of this work, analysis of the resulting PPI network revealed some interesting features. Some of the PPIs involving the Rothia mucilaginosa microorganism are very specific and relevant. Moreover, our method not only predicted new PPIs between periodontal pathogens and the host, but also PPIs between different periodontal pathogens, suggesting a synergistic course of action."}