Edgar D. Coelho

Computational prediction of the human-microbial oral interactome

BackgroundThe 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.ResultsWe 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.ConclusionsWe 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.

OralCard: A bioinformatic tool for the study of oral proteome

OBJECTIVES The molecular complexity of the human oral cavity can only be clarified through identification of components that participate within it. However current proteomic techniques produce high volumes of information that are dispersed over several online databases. Collecting all of this data and using an integrative approach capable of identifying unknown associations is still an unsolved problem. This is the main motivation for this work. RESULTS We present the online bioinformatic tool OralCard, which comprises results from 55 manually curated articles reflecting the oral molecular ecosystem (OralPhysiOme). It comprises experimental information available from the oral proteome both of human (OralOme) and microbial origin (MicroOralOme) structured in protein, disease and organism. CONCLUSIONS This tool is a key resource for researchers to understand the molecular foundations implicated in biology and disease mechanisms of the oral cavity. The usefulness of this tool is illustrated with the analysis of the oral proteome associated with diabetes melitus type 2. OralCard is available at http://bioinformatics.ua.pt/oralcard.

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction.

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

Computational methodology for predicting the landscape of the human-microbial interactome region level influence.

Microbial communities thrive in close association among themselves and with the host, establishing protein-protein interactions (PPIs) with the latter, and thus being able to benefit (positively impact) or disturb (negatively impact) biological events in the host. Despite major collaborative efforts to sequence the Human microbiome, there is still a great lack of understanding their impact. We propose a computational methodology to predict the impact of microbial proteins in human biological events, taking into account the abundance of each microbial protein and its relation to all other microbial and human proteins. This alternative methodology is centered on an improved impact estimation algorithm that integrates PPIs between human and microbial proteins with Reactome pathway data. This methodology was applied to study the impact of 24 microbial phyla over different cellular events, within 10 different human microbiomes. The results obtained confirm findings already described in the literature and explore new ones. We believe the Human microbiome can no longer be ignored as not only is there enough evidence correlating microbiome alterations and disease states, but also the return to healthy states once these alterations are reversed.

Fighting Fire with Fire: Computational Prediction of Microbial Targets for Bacteriocins

Recently, we have witnessed the emergence of bacterial strains resistant to all known antibacterials. Due to several limitations of existing experimental methods, these events justify the need of computer-aided methods to systematically and rationally identify new antibacterial agents. Here, we propose a methodology for the systematic prediction of interactions between bacteriocins and bacterial protein targets. The protein-bacteriocin interactions are predicted using a mesh of classifiers previously developed by the authors, allowing the identification of the best bacteriocin candidates for antibiotic use and potential drug targets.

Uncovering microbial duality within human microbiomes: A novel algorithm for the analysis of host-pathogen interactions

Microbial species thrive within human hosts by establishing complex associations between themselves and the host. Even though species diversity can be measured (alpha- and beta-diversity), a methodology to estimate the impact of microorganisms in human pathways is still lacking. In this work we propose a computational approach to estimate which human pathways are targeted the most by microorganisms, while also identifying which microorganisms are prominent in this targeting. Our results were consistent with literature evidence, and thus we propose this methodology as a new prospective approach to be used for screening potentially impacted pathways.

A Cytoscape-Based Web Tool for Representing Protein Networks

The full understanding of the biological and molecular processes that take place in the human oral cavity can only be attained through the identification of the components that participate within each process. Even though technological advances in computer science are promoting innovative methods and tools of great relevance in biomedical research, there is still a lack of efficient web tools to represent the complexity of these systems.

Data Integration Solution for Organ-Specific Studies: An Application for Oral Biology

The human oral cavity is a complex ecosystem where multiple interactions occur and whose comprehension is critical in understanding several disease mechanisms. In order to comprehend the composition of the oral cavity at a molecular level, it is necessary to compile and integrate the biological information resulting from specific techniques, especially from proteomic studies of saliva. The objective of this work was to compile and curate a specific group of proteins related to the oral cavity, providing a tool to conduct further studies of the salivary proteome. In this paper we present a platform that integrates in a single endpoint all available information for proteins associated with the oral cavity. The proposed tool allows researchers in biomedical sciences to explore microorganisms, proteins and diseases, constituting a unique tool to analyse meaningful interactions for oral health.