Computational prediction of the human-microbial oral interactome

BMC Systems Biology

José Luís Oliveira¹  Marlene Barros²  Maria José Correia³  Nuno Rosa³  Carlos Pereira⁵  Sérgio Matos¹  Joel P Arrais⁴  Edgar D Coelho¹ 

[1] Department of Electronics, Telecommunications and Informatics (DETI), Institute of Electronics and Telematics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, Portugal;Centre for Neurosciences and Cell Biology, University of Coimbra, Coimbra, Portugal;Department of Health Sciences, Institute of Health Sciences, The Catholic University of Portugal, Viseu, Portugal;Centre for Informatics and Systems of the University at Coimbra (CISUC), University of Coimbra, Coimbra, Portugal;Department of Informatics Engineering and Systems, Polytechnic Institute of Coimbra, Engineering Institute of Coimbra (IPC-ISEC), Coimbra, Portugal

关键词: Bayesian classification;    Oral interactome;    Protein-protein interactions;   

Others  :  1141337
DOI  :  10.1186/1752-0509-8-24

 received in 2013-08-27, accepted in 2014-02-17,  发布年份 2014

【 摘 要 】

Background

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 (PPIs) 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.

Results

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 (AUC) value greater than 0.926, supporting our primary hypothesis, as no single set of features reached an AUC greater than 0.877. After subjecting our dataset to the prediction model, the classified result was filtered for very high confidence PPIs (probability ≥ 1-10⁻⁷), leading to a set of 46,579 PPIs to be further explored.

Conclusions

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 webcite.

【 授权许可】

   
2014 Coelho et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 3.

Figure 4.

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