BACKGROUND
the 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   <cit> .
the same applies to host-pathogen systems, where ppis are essential in the establishment of infection  <cit> .
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  <cit> .
with this in mind, we can expect understanding of the human interactome to provide insight into physiopathological mechanisms  <cit> .
numerous experimental techniques have been explored to attain the human interactome: two-hybrid screening  <cit> , affinity purification mass spectrometry  <cit> , dna microarrays  <cit> , protein microarrays  <cit> , synthetic lethality  <cit> , phage display  <cit> , x-ray crystallography and nuclear magnetic resonance spectroscopy  <cit> , fluorescence resonance energy transfer  <cit> , surface plasmon resonance  <cit> , atomic force microscopy  <cit> , and electron microscopy  <cit> .
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  <cit> .
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.
as 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  <cit> .
additionally, there are methods based on genomic data , protein structure , domain information , protein sequence  <cit> , and gene ontology   <cit>  annotation semantic similarity .
in contrast, computational efforts to predict inter-species ppis have been very limited.
dyer et al.
<cit>  combined domain information with a maximum likelihood estimator algorithm  <cit> , while davis et al.
<cit>  adapted an approach following the threading-based method  <cit> .
to provide a better prediction, tastan et al.
<cit>  applied a method combining multiple data sources, and used a random forest classifier to predict interactions between hiv- <dig> 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  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  <cit> .
here 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  <cit> .
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  <cit> .
as 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.
RESULTS
we conducted a series of pre-test analyses to assess the performance of our model.
then, we proceeded to test our approach on high-quality experimental protein-protein interaction  data collected from five databases.
the selected databases exclusively contain manually curated ppi data.
computational model for predicting the human-microbial interactome
figure  <dig> summarizes the procedure used to achieve the model of the human-microbial interactome.
the starting point of this work is a set of  <dig>  proteins identified by proteomic studies as being present in the oral cavity and available on the oralcard database  <cit> .
since there is no well-established gold standard for ppis, we collected data from five databases containing high-quality experimentally determined interactions as described further on in methods.
extracted ppis from the five databases were merged, creating our gold standard of positive interactions.
the gold standard of negative interactions was obtained by randomly pairing the protein list on the premise that all protein pairs produced must differ from those on the positive dataset.
a total of  <dig>  positive and a similar number of negative pairs were obtained.
simultaneously, for each possible pair of proteins, we constructed five clusters of features based on:  literature;  primary protein sequence information;  orthologous profiles;  biological process similarity, and;  enriched conserved domain pairs.
this was performed by accessing public databases, extracting, and then processing the collected data.
the gold standard dataset was used to train a naïve bayes classifier and to perform further validations on the final model.
the classifier was then applied to the set of all possible pairs of protein interactions.
finally, by aggregating all individual pairs of predicted interactions, the final network was obtained.
evaluating the reconstruction of the human interactome
in this section, we evaluate the performance of the proposed method when applied to the set of human proteins from the gold standard.
we performed a 5-fold cross-validation to assess the combined and individual contributions of the clusters of features.
table  <dig> shows the results for the performance of each individual cluster while table  <dig> presents the contribution of each cluster to the final classifier by iteratively removing each cluster.
for each line the metrics are obtained by considering only that cluster of features on the classifier.
auc, area under the receiver operating characteristic  curve; ca, classification accuracy.
for each line the metrics are obtained by removing that cluster of features from the classifier.
auc, area under the receiver operating characteristic  curve; ca, classification accuracy.
the best performance is achieved through the ensemble of the five clusters, returning an area under the receiver operating characteristic  curve  of  <dig> , a precision of  <dig>  and a recall of  <dig> .
this result is above the performance of any individual feature and can only be achieved with the participation of all, meaning that all features are required and have a complementary contribution.
the sequence is simultaneously the feature with the best overall performance  and the one that causes the most negative impact when removed from the classifier, making the auc drop to  <dig> .
it also has a very interesting recall of  <dig> , partially due to the fact that all protein sequences are recognized and therefore the feature has full coverage.
in contrast, the clusters of orthologous groups  and domain-domain interactions  have the lowest individual aucs, mainly due to the low coverage of their features.
despite that, they benefit from a considerably high precision that contributes positively to the final classifier.
this is especially true for the cogs which, when removed, cause the major drop in precision.
the literature and the gene ontology  features, while not outstanding in any particular metric, have consistent performance on almost all metrics.
nevertheless, they make a very relevant contribution to the final classifier while the removal of the literature causes a drop of the auc to  <dig>  and the gos to  <dig> .
global characterization of the human-microbial interactome
the classifier returned a set of  <dig>  million possible interactions with a probability higher than  <dig> .
this corresponds to an average degree of  <dig> interactions per protein, which is much above the range of  <dig> to  <dig> documented in previous studies  <cit> .
additionally, there are reports of yeast two-hybrid screenings, the most commonly used high-throughput experimental method, reaching false-positive rates of 70%.
with this in mind, and in order to minimize the presence of false-positives in our predicted interactome, we filtered our prediction results to consider only very high confidence ppis .
we neglected the recall for the sake of precision.
as can be observed in figure  <dig>  the cutoff of 1-10− <dig> is the lowest probability value where an increment does not imply a decrease in the number of interactions.
this cut-off resulted in a total of  <dig>  ppis, with  <dig>  being between human proteins,  <dig>  between human and microbial proteins, and  <dig>  between microbial proteins.
the average number of protein interactions per protein after the cutoff was  <dig>  figure  <dig> is a visual representation of the interactions between the various organisms found in the oral cavity and the human host.
intra-species interactions are not shown.
the thickness of the ribbons between each organism is correlated with the number of ppis between both organisms, meaning that the organisms sharing highest number of ppis with the human are rothia mucilaginosa, leptotrichia buccalis, and actinomyces odontolyticus .
with the exception of homo sapiens with  <dig>  proteins, the most represented organisms in the human oral cavity are rothia mucilaginosa  , actinomyces odontolyticus , and streptococcus salivarius , with  <dig>   <dig>  and  <dig> proteins, respectively.
these organisms are opportunistic pathogens known to be associated with periodontitis  <cit>  and caries  <cit> .
the most frequent biological processes are related to host-microbial interactions: go: <dig> , go: <dig> , and go: <dig> .
we also identified the top three human hub-proteins present in our data: epidermal growth factor receptor  , fibronectin , and cullin-associated nedd8-dissociated protein  <dig>  .
in terms of non-human original hub-proteins, the most common are a serine/threonine protein kinase from leptotrichia buccalis , a kinase domain protein from parviromonas micra atcc  <dig> , and ras-related protein sec <dig> from saccharomyces cerevisiae .
discussion
functional analysis of the human-microbial interactome
unsurprisingly, the most frequent go biological processes in our final ppi dataset are associated with host-pathogen interactions.
the preeminence of innate immune response and viral interaction with host as the most frequent biological processes are self-explanatory.
however, the association between small molecule metabolism and host-microbial interactions is not so direct.
when faced with an infection, the body will respond by initiating two major cellular signaling pathways with opposing functions: the nuclear factor -kb and glucocorticoid-mediated signal transduction cascades.
while the nf-kb pathway promotes the immune response and inflammation, the glucocorticoid-mediated signal transduction cascade suppresses it.
in order to explain the association between small molecule metabolism and host-pathogen interactions we must focus on the nf-kb cascade, as it is known to mediate the transcriptional activation of several cytokines  involved in immunity  <cit> .
tumor necrosis factor -α and tnf-β, two of these cytokines, play key roles in immune regulation and inflammation  <cit> .
however, these cytokines are mainly responsible for the metabolic instabilities that occur during the infection, as they increase the metabolism of triglycerides inducing hyperlipidemia , stimulate lipolysis , accelerate glycogen breakdown and glucose consumption and uptake, and increase the serum levels of hormones that regulate glucose metabolism.
these metabolic changes possibly explain the great number of “small molecule metabolic process” biological processes.
analysis of hub proteins
the top three hub proteins identified share a common trait: these are exploited by pathogens in an attempt to gain entry to the host and survive inside it.
egfr is a transmembrane protein mainly produced in the salivary glands and the kidneys  <cit> .
its association with microbial invasion has already been reported for salmonella typhimurium <cit> , candida albicans <cit> , reovirus <cit> , and vaccinia virus <cit> .
apparently, all these pathogens initiate cellular invasion, at least to some extent, by binding to egfr.
this suggests the possibility that several other pathogens are using the egfr to start host colonization, as supported by buret et al.
<cit> .
similarly to egfr, fibronectin appears to also play the role of a “microbial-anchor”.
this glycoprotein is found bound to the β <dig> integrins in the cell surface, and is generally seen as a key protein for bacterial adhesion within the oral cavity  <cit> .
the cand <dig> protein, formerly tip120a, was found to interact with most of the proteins in the cullin family  <cit> .
the cullin protein family plays a key role in the ubiquitination of cellular proteins, i.e.
performing a post-translational modification in order to label the target protein with ubiquitin molecules.
this labeling frequently results in the commitment of the ubiquitin-linked protein to proteasomal degradation  <cit> .
consequently, cand <dig> was suggested to function as a global regulator of cullin-containing ubiquitin ligases  <cit> .
being one of the top hub-proteins, we investigated the relationship between the ubiquitination pathway and pathogen colonization of the host cells.
as expected, we found that certain bacteria corrupt the ubiquitination machinery as a means of regulating their virulence factors, or to trigger internalization of bacteria into host cells  <cit> .
such a mechanism improves the survival and replication chances of bacteria inside the host.
study of the microbiome role in periodontitis
when the data analysis is focused on a particular disease such as periodontal disease four main features can be observed: i) rothia mucilaginosa, a microorganism present in the normal human oral microbiome but considered an opportunistic pathogen  <cit> , is the species with the most interactions, with some of them revealing important and specific interactions; ii) new interactions are predicted between periodonto-pathogens and the host, and; iii) interactions between periodonto-pathogens are also predicted, most likely explaining a synergistic course of action, as has been previously proposed  <cit> .
regarding the first observation, the analysis of the sub-network pertaining to rothia mucilaginosa shares the characteristics previously described for the hub proteins with 37/ <dig> interactions with the efgr protein, 40/ <dig> interactions with fibronectin and 34/ <dig> interactions with cand <dig>  furthermore, this sub-network presents two predicted interactions which have not been described before: r. mucilaginosa proteins d2nsf <dig> and c6r5r <dig>  which are predicted to interact with human immunoglobulin chains , and could be related to the immune response specific for this species, explaining why these interactions are worth investigating.
if we consider the bacteria most associated with periodontal disease, our model predicts few interactions between a. actinomycemcomitans, p. gingivalis, and the host proteins.
as mentioned before, this is due to the fact that these organisms are not well represented in the original protein data set.
however, besides the interactions predicted between these bacteria and the human hub proteins described above, in the case of porphyromonas gingivalis it is possible to identify at least two potentially interesting new types of interactions between bacterial ribosomal proteins and a major histocompatibility complex protein .
furthermore, we also identified a possible interaction between the bacterial enolase  and a host aquaporin which could interfere with the homeostasis mechanisms of the host.
additionally, when we consider the interactions of p. gingivalis with other bacteria, we find that the same enolase might interact with outer membrane proteins of haemophilus influenza and pasteurella multocida.
the role of bacterial enolase as a multitask protein involved not only in carbohydrate metabolism but also in virulence has been recognized recently  <cit> .
this suggests that previously unknown and important ppis for oral colonization and biofilm formation may be present in this dataset.
finally the fact there are possible interactions between p.gingivalis proteases and those of other periodonto-pathogens such as kingella oralis and treponema denticola is interesting.
this may even shed some light on the synergistic aspects of oral biofilm in periodontal disease  <cit> .
CONCLUSIONS
the continuous yield of large-scale data mainly from microarrays and yeast two-hybrid studies has made the study of ppis very appealing.
the main issue associated with ppi study is the high prevalence of false positives and negatives in experimental ppi data.
being the only “reliable” source of ppis, inaccurate experimental ppi data will contaminate training datasets and therefore compromise the performance of computational ppi prediction methods.
for this reason, we believe that an improvement in the quality of experimental ppi data will greatly impact the performance of new computational ppi prediction approaches.
while this is not the case at present, we must consider how to avoid the effects of false positives and false negatives in the final ppi prediction model.
we proposed a probabilistic bayesian-based method to integrate several data sources, to obtain more robust and reliable ppi predictions.
by applying naïve bayes, we automatically up-weigh the most informative features and down-weigh the less informative ones, allowing for automatic error-correction.
our individual feature analysis results show a great relevance of the selected features.
when applied on a naïve bayes classifier, the individual features synergize, boosting the auc up to  <dig> .
this suggests that the reliability of prediction improves with the increase of significant features, meaning that the ensemble final model actually reduces the disadvantages of the individual methods.
cytoscape was successfully used to validate the network when tested with real pathway examples, discovering new potentially interesting interactions in oral biology, both between the host and the periodontal pathogens and between different periodontal pathogens.
we believe our work may be applied in several scientific areas, and even in other ppi related studies.
an example is biomedical ppi screening, to assess if interactions of particular interest might occur and what the related interaction probability is.
another example is pharmacologic research, as a well-established ppi network can provide insights on potential drug targets, but also new uses for existent in-market drugs.
finally, and based on the fact that protein interaction networks are dynamic  <cit> , our work can support researchers in identifying evolutionary patterns.

 the 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. the same applies to host-pathogen systems, where ppis are essential in the establishment of infection. 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. with this in mind, we can expect understanding of the human interactome to provide insight into physiopathological mechanisms. numerous experimental techniques have been explored to attain the human interactome: two-hybrid screening, affinity purification mass spectrometry, dna microarrays, protein microarrays, synthetic lethality, phage display, x-ray crystallography and nuclear magnetic resonance spectroscopy, fluorescence resonance energy transfer, surface plasmon resonance, atomic force microscopy, and electron microscopy. 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. 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. as 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. additionally, there are methods based on genomic data, protein structure, domain information, protein sequence, and gene ontology annotation semantic similarity. in contrast, computational efforts to predict inter-species ppis have been very limited. dyer et al. combined domain information with a maximum likelihood estimator algorithm, while davis et al. adapted an approach following the threading-based method. to provide a better prediction, tastan et al. applied a method combining multiple data sources, and used a random forest classifier to predict interactions between hiv- 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 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. here 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. 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. as 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.  we conducted a series of pre-test analyses to assess the performance of our model. then, we proceeded to test our approach on high-quality experimental protein-protein interaction data collected from five databases. the selected databases exclusively contain manually curated ppi data. computational model for predicting the human-microbial interactome figure  summarizes the procedure used to achieve the model of the human-microbial interactome. the starting point of this work is a set of proteins identified by proteomic studies as being present in the oral cavity and available on the oralcard database. since there is no well-established gold standard for ppis, we collected data from five databases containing high-quality experimentally determined interactions as described further on in methods. extracted ppis from the five databases were merged, creating our gold standard of positive interactions. the gold standard of negative interactions was obtained by randomly pairing the protein list on the premise that all protein pairs produced must differ from those on the positive dataset. a total of positive and a similar number of negative pairs were obtained. simultaneously, for each possible pair of proteins, we constructed five clusters of features based on: literature; primary protein sequence information; orthologous profiles; biological process similarity, and; enriched conserved domain pairs. this was performed by accessing public databases, extracting, and then processing the collected data. the gold standard dataset was used to train a naïve bayes classifier and to perform further validations on the final model. the classifier was then applied to the set of all possible pairs of protein interactions. finally, by aggregating all individual pairs of predicted interactions, the final network was obtained. evaluating the reconstruction of the human interactome in this section, we evaluate the performance of the proposed method when applied to the set of human proteins from the gold standard. we performed a 5-fold cross-validation to assess the combined and individual contributions of the clusters of features. table  shows the results for the performance of each individual cluster while table  presents the contribution of each cluster to the final classifier by iteratively removing each cluster. for each line the metrics are obtained by considering only that cluster of features on the classifier. auc, area under the receiver operating characteristic curve; ca, classification accuracy. for each line the metrics are obtained by removing that cluster of features from the classifier. auc, area under the receiver operating characteristic curve; ca, classification accuracy. the best performance is achieved through the ensemble of the five clusters, returning an area under the receiver operating characteristic curve of, a precision of and a recall of. this result is above the performance of any individual feature and can only be achieved with the participation of all, meaning that all features are required and have a complementary contribution. the sequence is simultaneously the feature with the best overall performance and the one that causes the most negative impact when removed from the classifier, making the auc drop to. it also has a very interesting recall of, partially due to the fact that all protein sequences are recognized and therefore the feature has full coverage. in contrast, the clusters of orthologous groups and domain-domain interactions have the lowest individual aucs, mainly due to the low coverage of their features. despite that, they benefit from a considerably high precision that contributes positively to the final classifier. this is especially true for the cogs which, when removed, cause the major drop in precision. the literature and the gene ontology features, while not outstanding in any particular metric, have consistent performance on almost all metrics. nevertheless, they make a very relevant contribution to the final classifier while the removal of the literature causes a drop of the auc to and the gos to. global characterization of the human-microbial interactome the classifier returned a set of million possible interactions with a probability higher than. this corresponds to an average degree of interactions per protein, which is much above the range of to documented in previous studies. additionally, there are reports of yeast two-hybrid screenings, the most commonly used high-throughput experimental method, reaching false-positive rates of 70%. with this in mind, and in order to minimize the presence of false-positives in our predicted interactome, we filtered our prediction results to consider only very high confidence ppis. we neglected the recall for the sake of precision. as can be observed in figure  the cutoff of 1-10− is the lowest probability value where an increment does not imply a decrease in the number of interactions. this cut-off resulted in a total of ppis, with being between human proteins, between human and microbial proteins, and between microbial proteins. the average number of protein interactions per protein after the cutoff was figure  is a visual representation of the interactions between the various organisms found in the oral cavity and the human host. intra-species interactions are not shown. the thickness of the ribbons between each organism is correlated with the number of ppis between both organisms, meaning that the organisms sharing highest number of ppis with the human are rothia mucilaginosa, leptotrichia buccalis, and actinomyces odontolyticus. with the exception of homo sapiens with proteins, the most represented organisms in the human oral cavity are rothia mucilaginosa, actinomyces odontolyticus, and streptococcus salivarius, with and proteins, respectively. these organisms are opportunistic pathogens known to be associated with periodontitis and caries. the most frequent biological processes are related to host-microbial interactions: go:, go:, and go:. we also identified the top three human hub-proteins present in our data: epidermal growth factor receptor, fibronectin, and cullin-associated nedd8-dissociated protein. in terms of non-human original hub-proteins, the most common are a serine/threonine protein kinase from leptotrichia buccalis, a kinase domain protein from parviromonas micra atcc, and ras-related protein sec from saccharomyces cerevisiae. discussion functional analysis of the human-microbial interactome unsurprisingly, the most frequent go biological processes in our final ppi dataset are associated with host-pathogen interactions. the preeminence of innate immune response and viral interaction with host as the most frequent biological processes are self-explanatory. however, the association between small molecule metabolism and host-microbial interactions is not so direct. when faced with an infection, the body will respond by initiating two major cellular signaling pathways with opposing functions: the nuclear factor -kb and glucocorticoid-mediated signal transduction cascades. while the nf-kb pathway promotes the immune response and inflammation, the glucocorticoid-mediated signal transduction cascade suppresses it. in order to explain the association between small molecule metabolism and host-pathogen interactions we must focus on the nf-kb cascade, as it is known to mediate the transcriptional activation of several cytokines involved in immunity. tumor necrosis factor -α and tnf-β, two of these cytokines, play key roles in immune regulation and inflammation. however, these cytokines are mainly responsible for the metabolic instabilities that occur during the infection, as they increase the metabolism of triglycerides inducing hyperlipidemia, stimulate lipolysis, accelerate glycogen breakdown and glucose consumption and uptake, and increase the serum levels of hormones that regulate glucose metabolism. these metabolic changes possibly explain the great number of “small molecule metabolic process” biological processes. analysis of hub proteins the top three hub proteins identified share a common trait: these are exploited by pathogens in an attempt to gain entry to the host and survive inside it. egfr is a transmembrane protein mainly produced in the salivary glands and the kidneys. its association with microbial invasion has already been reported for salmonella typhimurium, candida albicans, reovirus, and vaccinia virus. apparently, all these pathogens initiate cellular invasion, at least to some extent, by binding to egfr. this suggests the possibility that several other pathogens are using the egfr to start host colonization, as supported by buret et al.. similarly to egfr, fibronectin appears to also play the role of a “microbial-anchor”. this glycoprotein is found bound to the β integrins in the cell surface, and is generally seen as a key protein for bacterial adhesion within the oral cavity. the cand protein, formerly tip120a, was found to interact with most of the proteins in the cullin family. the cullin protein family plays a key role in the ubiquitination of cellular proteins, i.e. performing a post-translational modification in order to label the target protein with ubiquitin molecules. this labeling frequently results in the commitment of the ubiquitin-linked protein to proteasomal degradation. consequently, cand was suggested to function as a global regulator of cullin-containing ubiquitin ligases. being one of the top hub-proteins, we investigated the relationship between the ubiquitination pathway and pathogen colonization of the host cells. as expected, we found that certain bacteria corrupt the ubiquitination machinery as a means of regulating their virulence factors, or to trigger internalization of bacteria into host cells. such a mechanism improves the survival and replication chances of bacteria inside the host. study of the microbiome role in periodontitis when the data analysis is focused on a particular disease such as periodontal disease four main features can be observed: i) rothia mucilaginosa, a microorganism present in the normal human oral microbiome but considered an opportunistic pathogen, is the species with the most interactions, with some of them revealing important and specific interactions; ii) new interactions are predicted between periodonto-pathogens and the host, and; iii) interactions between periodonto-pathogens are also predicted, most likely explaining a synergistic course of action, as has been previously proposed. regarding the first observation, the analysis of the sub-network pertaining to rothia mucilaginosa shares the characteristics previously described for the hub proteins with 37/ interactions with the efgr protein, 40/ interactions with fibronectin and 34/ interactions with cand furthermore, this sub-network presents two predicted interactions which have not been described before: r. mucilaginosa proteins d2nsf and c6r5r which are predicted to interact with human immunoglobulin chains, and could be related to the immune response specific for this species, explaining why these interactions are worth investigating. if we consider the bacteria most associated with periodontal disease, our model predicts few interactions between a. actinomycemcomitans, p. gingivalis, and the host proteins. as mentioned before, this is due to the fact that these organisms are not well represented in the original protein data set. however, besides the interactions predicted between these bacteria and the human hub proteins described above, in the case of porphyromonas gingivalis it is possible to identify at least two potentially interesting new types of interactions between bacterial ribosomal proteins and a major histocompatibility complex protein. furthermore, we also identified a possible interaction between the bacterial enolase and a host aquaporin which could interfere with the homeostasis mechanisms of the host. additionally, when we consider the interactions of p. gingivalis with other bacteria, we find that the same enolase might interact with outer membrane proteins of haemophilus influenza and pasteurella multocida. the role of bacterial enolase as a multitask protein involved not only in carbohydrate metabolism but also in virulence has been recognized recently. this suggests that previously unknown and important ppis for oral colonization and biofilm formation may be present in this dataset. finally the fact there are possible interactions between p.gingivalis proteases and those of other periodonto-pathogens such as kingella oralis and treponema denticola is interesting. this may even shed some light on the synergistic aspects of oral biofilm in periodontal disease.  the continuous yield of large-scale data mainly from microarrays and yeast two-hybrid studies has made the study of ppis very appealing. the main issue associated with ppi study is the high prevalence of false positives and negatives in experimental ppi data. being the only “reliable” source of ppis, inaccurate experimental ppi data will contaminate training datasets and therefore compromise the performance of computational ppi prediction methods. for this reason, we believe that an improvement in the quality of experimental ppi data will greatly impact the performance of new computational ppi prediction approaches. while this is not the case at present, we must consider how to avoid the effects of false positives and false negatives in the final ppi prediction model. we proposed a probabilistic bayesian-based method to integrate several data sources, to obtain more robust and reliable ppi predictions. by applying naïve bayes, we automatically up-weigh the most informative features and down-weigh the less informative ones, allowing for automatic error-correction. our individual feature analysis results show a great relevance of the selected features. when applied on a naïve bayes classifier, the individual features synergize, boosting the auc up to. this suggests that the reliability of prediction improves with the increase of significant features, meaning that the ensemble final model actually reduces the disadvantages of the individual methods. cytoscape was successfully used to validate the network when tested with real pathway examples, discovering new potentially interesting interactions in oral biology, both between the host and the periodontal pathogens and between different periodontal pathogens. we believe our work may be applied in several scientific areas, and even in other ppi related studies. an example is biomedical ppi screening, to assess if interactions of particular interest might occur and what the related interaction probability is. another example is pharmacologic research, as a well-established ppi network can provide insights on potential drug targets, but also new uses for existent in-market drugs. finally, and based on the fact that protein interaction networks are dynamic, our work can support researchers in identifying evolutionary patterns. 
for the final prediction model, we used an ensemble methodology combining five distinct ppi prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. the dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint. the positive and negative datasets were merged and used for training and validation of a naïve bayes classifier. the obtained ppis were used to create our positive dataset and, indirectly, our negative dataset. after subjecting our dataset to the prediction model, the classified result was filtered for very high confidence ppis, leading to a set of ppis to be further explored. the oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. in addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions between the host and microorganisms.
 the oral cavity is a complex ecosystem where human chemical compounds coexist with a particular microbiota. however, shifts in the normal composition of this microbiota may result in the onset of oral ailments, such as periodontitis and dental caries. in addition, it is known that the microbial colonization of the oral cavity is mediated by protein-protein interactions between the host and microorganisms. nevertheless, this kind of ppis is still largely undisclosed. to elucidate these interactions, we have created a computational prediction method that allows us to obtain a first model of the human-microbial oral interactome.  we collected high-quality experimental ppis from five major human databases. the obtained ppis were used to create our positive dataset and, indirectly, our negative dataset. the positive and negative datasets were merged and used for training and validation of a naïve bayes classifier. for the final prediction model, we used an ensemble methodology combining five distinct ppi prediction techniques, namely: literature mining, primary protein sequences, orthologous profiles, biological process similarity, and domain interactions. performance evaluation of our method revealed an area under the roc-curve value greater than, supporting our primary hypothesis, as no single set of features reached an auc greater than. after subjecting our dataset to the prediction model, the classified result was filtered for very high confidence ppis, leading to a set of ppis to be further explored.  we believe this dataset holds not only important pathways involved in the onset of infectious oral diseases, but also potential drug-targets and biomarkers. the dataset used for training and validation, the predictions obtained and the network final network are available at http://bioinformatics.ua.pt/software/oralint. protein-protein interactionsoral interactomebayesian classification 